IIM SKILLS vs. Ivy Professional School Data Analytics Course Review

by | Nov 2, 2024 | Courses, Data Analytics, Institute Review

Data analytics is one of the most competitive and high-paying jobs in the technical industry. Please read this article – Data Analytics IIM SKILLS vs. Ivy Professional School Data Analytics Course to get a better understanding of courses and institutions.

IIM SKILLS vs. Ivy Professional School Data Analytics Course

 

Data Analytics

Data analytics skills are known as observational skills in simple definition, in high demand as more companies aim to improve operations and drive results by using data to inform decision-making. Gain crucial data science competencies by taking a data business analytics online course and receiving a certificate of completion from any reputable institute.

As businesses generate more data in the digital age, the demand to analyze and interpret this quantitative information grows. Professionals with data-analytical abilities are invaluable because they can derive relevant insights from organized and unstructured data acquired by businesses. To succeed in this field, you must first master the principles of data analysis, such as how data is gathered, saved, and retrieved, how to interface with databases using Structured Query Language (SQL), and how to visualize data using applications such as Microsoft Excel or Tableau.

The primary advantage of data analytics is to use the data capacity in analytical models to appropriate business choices. A variety of businesses demands analytical skills as it is required in financial transactions, operation deals, promotion, and human resource management by company executives. Managers with data science credentials can influence change and success in the corporate world by harnessing actionable insights through analytics and conveying findings to stakeholders via visualization tools such as Tableau. As traditional business positions become more complicated and flexible, there is an increasing demand for people who can analyze data as well as convey results to drive corporate plans.

The usage of data and information technology (IT) expands helps and business and technological methods. Individuals in this sector, known as business systems analysts, can effectively bridge the gap between IT and leadership teams. The critical role in improving an organization’s function, and profitability by implementing more efficient authorizing change.

Learn how to harness the power of technology by taking a short online course in data analytics or business analytics. Gain up-to-date skills in data science and play a significant role in making data-driven decisions for your organization, whether you are an experienced data analyst or joining the profession for the first time.

How Do Data Analytics Help Types of Businesses?

Data analytics assists businesses in all elements of their operations, from sourcing supplies to estimating demand to accounting, human resource activities, and all aspects of marketing, among others.

Businesses are highly dependent on analytics to identify numbers in their operations. Furthermore, it provides thorough insights on what is and is not working on all aspects of business operations, such as marketing and sales scenarios such as content, social media campaigns, email marketing, website traffic, number of sales, and much more.

Data analysis gives significant value by providing the necessary knowledge to streamline corporate actions and results. To improve the outcomes of all of their business activities, a corporation can use analytics to better comprehend the following scenarios:

  • Making sense of useless corporate data
  • Highlight minor errors
  • Reduce customer service challenges
  • Make businesses proactive rather than reactive.
  • Urgency 
  • Getting rid of the unknown

Many businesses profit greatly from data analytics since data has become the lifeblood of an organization’s efficiency, and cloud computing services enable all possible data mining, data modeling, and data processing requirements. Let’s look at some of the top industries that are employing data analytics and exploiting data to drive future growth in their respective fields.

Use of Data Analytics in Industries

Banking and securities, government, media and entertainment, and a few other areas have seen a surge in demand for analytical solutions and tools.

Securities and Banking

Hedge funds, retail trade, and large banks in the banking and financial markets have all deployed big data technologies to monitor trade analytics used in high-frequency trading, sentiment measurement, pre-trade decision-support analytics, predictive analytics, and so on. Other hot areas that heavily rely on data and analytics solutions include risk analysis, anti-money laundering, KYC (know your customer), fraud reduction, and demand enterprise risk management.

Banking and security organizations monitor financial market movements utilizing data analytics techniques and solutions. 

Insurance Industry

To understand and manage their connections with clients regarding claims, underwriting, future consumer requirements, and much more, property and casualty insurance firms use telematics, agent interactions, customer contacts, smart homes, and even social media campaigns. Companies leverage data observation to attract new customers with improved customer service, allowing them to maximize revenue.

Manufacturing Sector

Manufacturing industries of various types, as well as natural resources such as oil, gas, minerals, metals, agricultural products, and so on, have generated a large amount of data during the previous few years. The data is not only massive but also complex and difficult to manage, making them an ideal target for data analytics. However, this set of data is currently unexplored.

Now, the sector is aggressively investing in predictive analytics technologies and embracing digitization to turn itself into a data-driven decision-making industry. Companies are ingesting and integrating various types of important data, such as geospatial data, text, graphical data, IoT data, and temporal data, into datasets to provide usable information.

Pharmaceuticals and Healthcare

Pharma and the healthcare sector are also investing in modern cloud technology and big data to improve medical services and consumer satisfaction through analytics. Wearable trackers and RPM (remote patient monitoring) are only two examples of advancements in the healthcare business; they also aid in determining whether patients are taking their meds and adhering to the treatment plan correctly.

Data accumulated over time provides clinicians with extensive information and helps medical practitioners with their patient’s well-being as well as actionable insights. 

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Travel & Transportation

Transportation The transportation business has various unexplored big data prospects. Governments in various nations employ big data to better understand people’s needs, transform cities into smart cities with IoT, control traffic, optimize route planning and intelligent transportation systems, and manage congestion.

The business sector uses big data to provide a better customer experience during holidays, corporate meetings, and other stays, much as the government sector invested and improved data for day transportation. Logistics organizations are also employing data analytics to manage revenue, speed up operations, and gain a competitive advantage. The transportation industry is only now beginning to see widespread embrace of change.

Wholesale and Retail Industry

The retail and wholesale industries thrive on higher profits and better customer service. To stay ahead of the competition, retailers are adopting data analytics to deal with expanding big data to precisely anticipate customer needs and then deliver those needs. Analytics enables them to compete in a crowded market and stay ahead of the competition. Extraction of detailed insights from data analytics solutions allows them to keep their customers pleased and return to their stores. Retailers use data analytics in practically every facet of their operations, including:

  • Improve marketing by personalizing the customer experience.
  • Improve logistics and supply chain management.
  • Manage prices to increase sales.
  • Make use of retail data analytics.

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Final Opinion:

Data analytics provides the technologies and tools needed to create a big data world with organized and unstructured data collections. Companies can utilize these solutions to optimize efficiency and avoid resource and time waste by strengthening their decision-making capabilities using data-driven insights. Many others are following suit and transforming their businesses to become data-driven to achieve better and more sustainable results. 

Read further about two educational platforms in this article- IIM SKILLS vs. Ivy Professional School

 IIM SKILLS vs. Ivy Professional School Data Analytics Course

IIM SKILLS

IIM SKILLS conducts a wide range of educational courses for every kind of student, including beginners, graduates, technologists, and mid-level and senior-level professionals. It has a reputation as a leading educational institution of online professional skills courses, providing complete training to excel at work or establish a business. These courses cover a wide range of topics, combining theory and practice, and apply to a variety of occupations and sectors. The well-planned content assists pupils in developing their abilities and receiving the greatest teaching. They can transform their passions into lucrative and fulfilling professions by identifying and improving their skill set.

The Data Analysis course duration is a path that fills students with confidence and skills. It is laid out at IIM abilities to help you gain the necessary abilities as you progress through the courses of the Data Analysis course. Reinforcing these industry-relevant abilities will provide you an advantage over your competitors in finding lucrative career opportunities in the market.

Modules Covered

Excel: Basic and Advanced

  •  Excel Environment Formatting and Conditional Formatting Overview
  •  Sorting, Filtering, and Validation of Data
  •  Knowledge of Name Ranges
  •  Chart types explained: column and bar charts, line and area charts, pie charts, doughnut charts, scatter plots
  •  Formatting a graph
  •  Creating and personalizing sophisticated charts such as thermometer charts, waterfall charts, and population pyramids
  • Data Analysis with Pivot Tables – Using the row and column shelves, values, and filters
  •  Summary reports, advantages and disadvantages of data stacking and cross-tabulation
  •  Modify the aggregation kinds and summarization.
  •  Using pivot data to create groups and bins
  •  Calculated field concept, usage, and restrictions
  •  Various report layouts are available, including outline, condensed, and tabular formats.
  •  Grand totals and subtotals can be displayed and hidden.
  • Reports making

Visual Basic Application components and working pattern

  •  What Is Logic and What Is VBA?
  •  What Is VBA?
  •  Macro Recordings Overview, IDE
  • VBA Language Elements That Are Required
  •  Programming statements, variables and data types, keywords, and syntax
  •  Comments, Operators
  •  Range Objects in Action
  •  Structures for Programming and Control, Looping Structures, With-End with Bloc, Worksheet and Workbook Functions, Automatic Procedures, and Event
  •  VBA Objects and Memory Management
  • Communicating with Your Users: Basic Dialog Boxes and User Forms
  •  Using Add-ins and User Form Controls
  •  Using the User Interface to Access Your Macros
  •  Using VBA, retrieve data from an Access database in Excel.

SQL

  •  What is SQL – A Brief Overview Installing MS SQL Server on Windows OS
  • Use of Server Management Studio Overview
  •  Understanding fundamental database principles
  •  Statements to Insert, Update, and Delete
  •  Subsetting, Filters, and Sorting are all part of the Select statement. 
  •  Grouping and aggregation
  •  SQL Functions: Number, Text, Date, and so on
  •  SQL Keywords: top, distinct, null, and so on
  •  SQL Operators: Relational (both single and multi-valued), Logical Application of wildcard operators and wildcard characters, et cetera
  •  Understanding the Fundamentals of SSIS
  •  Packages: An Introduction
  •  Parameterization and Visualization
  •  Using SSRS to Create Visualizations

Power BI

  • Power BI Overview and Installation
  • When working on a project, introduce different options in the Power BI Desktop Task pipeline.
  • Power Query Conditional Columns
  • Query Editor allows you to clean and alter data.
  • Define data granularity
  • Merging and appending data
  • Introduction to DAX Calculated Tables, Columns, and Measures
  • Time Intelligence in DAX
  • DAX functions that are frequently used in real-time 
  • Power BI Reports Development
  • Working with Various Visuals/Charts
  • Report Formatting Options
  • To filter visualizations, use a slicer.
  • Utilizing Filters 
  • Custom Visuals from the Gallery can be downloaded and used.
  • Include an R or Python visual.
  • Use key performance indicators.

Python

  • Statistics Fundamentals
  •  Central tendencies measurements
  •  Variance measures
  •  Frequency measurements
  • Operations on Matrixes
  •  Calculus Derivatives and Integration Overview
  •  Maxima and minima
  •  The area behind the curvature
  •  Theoretical Optimization

Processing with NumPy (Numerical Python)

Pandas Data operations (Series & Data structures)

Tableau- Tableau product, measure and dimensions

Introduction of R for Data Science, Data exporting in various formats

Duration: Six months

Average Course Fee:  INR 49900 + Taxes.

Placement: 100% Job Support.

Study material access: Lifetime access to all live and recorded Sessions.

Tools Access:  Lifetime access to all the tools

Faculty support: Doubt clearing and direct guidance with the Faculty

Refund policy: Money back guarantee after dissatisfaction

Other Professional Courses from IIM SKILLS

Ivy Professional School

Ivy Professional School is a pioneer in the country’s big data analytics and data science training disciplines and is the official training partner of corporations such as Genpact, HSBC, eBay/PayPal, ICRA, ITC, Capgemini, Cognizant, etc. Ivy Professional School has consistently ranked among the top data science and analytics schools in the US for the last five years. The specialist instructors at Ivy Professional School are affiliated with major institutions such as ISI, IIMs, IITs, and prestigious US universities.

Since its beginning in 2007, IVY has trained over 10,000 people from 10 countries, fifty organizations, and one hundred colleges (including IIMs, IITs, and ISIs). Ivy recently completed three face-to-face classroom sessions for a large analytics organization.

Individual participants can make use of Ivy’s extensive course content on analytic technologies such as SAS, R, Hadoop, Big Data, SPSS, machine learning, and the new buzzword Data Visualization.

The course and curriculum are heavily industry-focused, ranging from basic to advanced levels. Doubts will never go unanswered in any class, and what students like the most about the faculty is how fully they help a person understand the principles. Students will get hands-on experience with real-world projects and case studies in data analytics utilizing Hadoop, Spark, Hive, and AWS.

Course Modules

Dashboard and Automation Learning with the use of Advanced Excel

  • MS Excel Basics, Cell Reference, Basic Functions, and Usage
  • Sorting, filtering, advanced filtering, subtotal pivot tables, and slicers are all available.
  • Goal Look for and solve various charts Graphs: When and how to utilize them
  • Conditional Formatting: Vlookup, Hlookup, Match, and Index
  • Error Handling, Worksheet & Workbook Reference
  • IF and Nested IF are logical operators and functions.
  • Validation of Data
  • Text Features
  • Controls for Forms
  • Dashboard
  • Industry Case Studies from Insurance, Sales, Marketing, and Web Analytics

SQL Queries and Database Management

  • Fundamentals of Databases
  • Efficient Relational and Create Database Models
  • Case Studies in Database Model Design
  • Data Handling Case Study Implementation
  • Importing and Exporting a Large Volume of Data into a Database
  • DDL, DML, DCL, and DQL SQL Statements
  • Merging, joining, sorting, indexing, co-related queries, and so forth are all examples of transactional SQL queries.
  • Exercises in Data Manipulation Using SQL Queries
  • Using SQL Statements to Create Database Models
  • Individual SQL Statement Handling Projects

Tableau

  • An Overview of Data Visualization
  • Introduction to Tableau Data Visualization
  • Tableau Fundamentals
  • Utilizing Sorting and Filters
  • Making Dual Axis and Combination Charts
  • Calculations in a Table
  • Overview of the Calculated Field
  • 4 Case Studies Using Retail, Airline, and Bank Data

Business analysis and statistics

  • Data formats, and Visual Representation
  • Index Numbers, Correlation, and Data Modeling
  • Central Tendency and Dispersion Measures
  • Time Series Analysis and Forecasting
  • Bayesian Probability Theory Probability Mathematical Expectation and Distribution
  • Sampling and Distribution of Sampling
  • Estimation Theory and Hypothesis Testing

R

  • Introduction to R Data Handling in R Analytics and Statistics Overview
  • Overview of Analytics and Statistics: String and Character Functions in R
  • Time Series Theory Overview Linear Regression in R Logistic Regression in R
  • Case Study Feature Engineering and Dimension Reduction, as well as Case Study Decision Trees

Python

  • Python Scientific Distribution Essentials
  • Python Data Manipulation and Visualization Introduction to Predictive Modeling
  • Linear Regression Modeling
  • Logistic Regression Modeling
  • Forecasting Time Series

Machine Learning

  • Decision Tree for Supervised Learning
  • Ensemble Learning: Supervised Learning
  • Analytics and Text Mining

Program Name: Data Analytics With Visualization Certification Course

Duration: 195 hours

Average Course Fee: INR 40,684 / (Inclusive of All Taxes)

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Frequently Asked Questions (FAQs) on IIM SKILLS vs. Ivy Professional School Data Analytics Course

Question 1) What exactly is Big Data Analytics?

Big Data analytics has become the backbone of huge industrial and commercial firms in making critical decisions based on data analysis results. Data analytics is simply the act of analyzing data to identify patterns and develop conclusions to make an essential choice. Nowadays, thanks to technological advancements, data may be evaluated utilizing advanced analytical approaches such as the analysis of structured, unstructured, and semi-structured data. Big data is a term used to describe large data sets that cannot be handled by traditional analysis databases. Data analysis has become a sought-after vocation by corporations and enterprises since it allows them to make faster, more efficient, and more intelligent judgments.

There are numerous career opportunities in the field of data analysis, but there are fewer people to fill them. Big data analytics is a booming area with an increasing number of enterprises and firms, but there are fewer people to fill these positions. As a result, firms are willing to pay top dollar to individuals with the necessary skill set in the field of big data analysis. The job of data analysis is not only limited to concluding for the company to make better decisions, but they can also take on other jobs such as machine learning, data architecture, and data modeling where they get to explore new algorithms and new aspects of data analysis.  

Question 2) Have in India and around the World any thriving Scope of Data Analytics? 

Answer:  Data analytics is surely in high demand because enterprises and large corporate firms require these analysts to help them make faster and more successful business decisions. The employment of new and advanced technologies in this industry demonstrates the importance of data analysis and processing in enterprises and firms. They are acquiring the greatest software and machines available to assist them in the processing and analysis of large amounts of data. Data analysis assists in making pricing strategy decisions, determining ways to cut various costs, and optimizing the procedures used by the business or firm to make the system in the business cost-efficient and effective.

Data analysis studies are not limited to just analyzing patterns to come up with effective solutions; rather, there are many jobs in computer-related skills as well, such as developing new software, retrieving data using machine language, and using machine learning to better understand the process of data analysis and data mining. The scope of data analysis is not restricted to a specific sector of corporations or enterprises; it is a skill that is necessary in all operational companies all over the world.

Question 3) What are some of the quintessential skills for Data Analytics?

Answer: Jobs in the field of data analytics necessitate strong analytical abilities as well as numerical abilities. To make effective and timely decisions, the individual must be able to think reasonably and critically. In addition to these, prospective candidates for big data analytics employment must possess particular technical skills. A candidate seeking a job in big data analytics should have the following characteristics:

  • Analytical abilities
  • Learning by machine
  • Java, Python, Scala, SQL, and MATLAB programming
  • Logical reasoning
  • Thinking critically
  • Decision-making abilities
  • Observational abilities

Concluding thoughts on IIM SKILLS vs. Ivy Professional School Data Analytics Course

We hope this extensive article on IIM SKILLS vs. Ivy Professional School Data Analytics Course has been able to answer some of your questions on data analytics and related courses, curriculum, duration, practical assignments among other things. Deep dive into the world of analytics and make a prosperous career for yourself. All the very best for the future!

IIM SKILLS vs TCS iON Data Analytics Course Review

by | Nov 2, 2024 | Courses, Data Analytics, Institute Review

If you are from a technical or non-technical background, and considering a course that can evolve your career in the upcoming digital generation, then this is the right page for you. In this article IIM SKILLS vs TCS iON Data Analytics Course, we are going to introduce you to everything. The need for data analysts is rapidly expanding along with the amount of internet data that is being generated. Nearly all industries are implementing data tactics to improve business decisions.

IIM SKILLS vs TCS iON Data Analytics Course Review

Many college students and recent graduates find data analysis to be an appealing career choice due to the strong demand for their services. But it can be difficult to grasp how you can get into the industry, particularly if you have a different educational background.

Before you make a final choice, you must be aware of the prerequisites and eligibility standards. Even if you meet the eligibility standards, you must make sure that mastering quantitative subjects like programming and maths is something you are interested in.

A Data Analytics course can boost a career as a data analyst for those interested in this field. However, you must be familiar with data analytics and how to launch your career before enrolling. Let’s start basics of Data analytics in this read- IIM Skills vs TCS ion Data Analytics Course

Data Analytics: A Brief Detail

Analyzing raw data to derive actionable insights is known as data analytics. Various techniques and technologies are utilized in the analytics process for trend finding, projection, and anomaly detection. This data’s structure can be semi-structured, unstructured, or structured. Data visualization tools can be used to visualize the data after analysis in the form of graphs, charts, or illustrations.

Data analysts are experts who examine the data to produce meaningful information. To better commercial and policy decisions, they gather, purge, and analyze data. Data analysts analyze the gathered data using their knowledge of mathematics and programming. After conducting the study, they must be able to explain their conclusions to those who are responsible for making crucial business decisions.

Businesses, governments, pharmaceutical companies, space agencies, and explorers are realizing the value of comprehending the enormous volume of data that at first glance appears to be unrelated thanks to improved data analytics.

Data aids businesses in developing more successful marketing strategies, better client understanding, and all-around improved customer service.

Data analytics can be defined as the process of analyzing raw data to find trends and make inferences about those trends. These discoveries aid in streamlining processes to boost an organization’s or a business’s efficiency.

To keep up with the enormous amount of data generated every second and to extract usable information from it, nearly every firm is looking to hire data analysts. One of the greatest ways to enter this profitable and quickly increasing field is through data analytics courses. India is already one of the leading nations in the world for the export of data analytics services.

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What Are Some Major Reasons to Pursue a Career in Data Analytics?

The data analytics courses are the simplest and maybe quickest method to begin your data analytics profession. In this read- IIM SKILLS vs TCSion Data Analytics Course we provide you a detailed reason for enrolling in the Data Analytics course. IIM Skills and TCSion provides an Advanced Certificate program in Data Analytics.

The following are some of the most persuasive arguments for pursuing a profession in data analytics:

Data Analytics Are Highly Valued by All Enterprises.

Both small and large companies are becoming virtual platforms. The enterprise’s primary priority is to adopt big data, which creates chances in banking and insurance, healthcare, and other industries. The transition has led to better marketing, B2B market interactions, and coordinated operations and manufacturing.

The Absence of Skilled Individuals With Absolute Skill Set

Despite the growing demand for data analytics job profiles, there is a considerable shortage of qualified workers compared to the number of data analysts who are currently employed.

There Are Lots of Employment Openings in Many Industries

To support organizational development, the big data analytics framework offers both technical and non-technical Business analyst profiles to develop legit organizational growth.

Combine Managerial Savvy With Analytical Prowess

Students can excel in the field of data analytics if they have the perfect mix of administrative and analytical skills. With a variety of career prospects and high pay ranges, the industry is seen as a very attractive employment option. Due to their in-demand skill sets, the industry norms offer data scientists and analysts substantial wages.

Wide Variety of Career Prospects

Job seekers can hunt for employment as a data analyst, data architect, statistician, project manager, chief data officer, or ML engineer among many other professions. Depending on your preferences, you can select a degree and a range of specializations.

As an example, forensic data analysts support the recovery and preservation of digital evidence for criminal and cybercrime investigations. Data mining is another specialized field where professionals look at data to find patterns and trends.

Why Data Analytics is Essential for Business?

Answer: Data analytics is the key to unlocking the useful insights that businesses generate from their vast variety of data. Data analytics works with everything from marketing to specific clients to recognizing and reducing business setbacks. Let’s examine some advantages of adopting data analytics.

Develop a Consumer Experience Unique

Businesses collect their users’ feedback from a variety of sources, including multiple social media platforms, traditional retail, and e-commerce. 

Retail clothing companies with both a physical and online presence might combine data from both sources about their sales to assess all sets of data. This helps businesses develop targeted social media campaigns to increase e-commerce sales for product categories in which customers are already interested.

Businesses can use customer data to run behavioral analytics models and further improve the customer experience. A company uses e-commerce transaction data to run a predictive model to identify products for sale.

Stimulate Smart Business Decisions

The management of supply chains has benefited more and more from data analytics. Big data and quantitative techniques are used in supply chain analytics to improve decision-making throughout the chain. Big supply chain analytics, in particular, widens data sets for more in-depth analysis beyond the customary internal data found in enterprise resource planning (ERP) systems. 

Businesses can employ a model to predict how cfhanges in product pricing offerings will affect client demand. Companies use a model to predict changes in product pricing and offerings that will affect customers’ demand in different ways. To evaluate the validity of the concepts generated by such models, changes to product offerings might be made. Organizations use data analytics tools to assess performance and visualize marketing outcomes after gathering informative sales data on the modified items. This will assist small or big businesses in decision-makers deciding whether to implement the changes for progress across the company or not.

Optimize Processes

Data analytics can help organizations increase operational effectiveness. Data collection and analysis regarding the supply chain can reveal the source of production delays or bottlenecks and aid in the prediction of potential future issues. This would prevent production delays.

Companies manage to maximize their inventory levels, especially those in the retail industry based on elements like seasonality, holidays, and secular trends, data analytics can assist in determining the best supply for all of an enterprise’s products.

Reduce the Risk of Dealing With Difficulties

In business, risks abound. It works for employee safety and legal liabilities for an organization that can use data analytics to better evaluate hazards and implement preventative actions. To identify which locations are most vulnerable to theft, for instance, a retail chain could use a propensity model, a statistical tool that predicts future behavior or events. The company might use this information to decide how much security is required at the stores or even whether it should exit any particular location.

The best way to clear sales and minimize inventory can be found by using data analytics.  Companies overestimate their demand for a product and evaluate through statistical models that automatically generate solutions to persistent issues with the product that might be resolved by data analytics.

Security

Data security threats exist in all forms, and it is necessary to be averted by organizations. Organizations can employ data analytics to detect the root causes of security breaches. To restrict vulnerabilities, IT uses such information for security.

Statistical models can be used by IT departments to curb threats. A distributed denial-of-service attack is an example of an attack that frequently involves anomalous access behavior. These configured models can be used to run continuously for data analytics in organizations, with monitoring and alerting systems. It is added on top to find and flag anomalies so that security experts can take rapid action.

IIM Skills vs TCS ion Data Analytics Course

IIM SKILLS

The top educational institution, IIM SKILLS, offers a variety of professional and skill development courses. Their data analytics courses are highly regarded and the most popular globally.

IIM SKILLS, a reputable brand in the field, provides a Master’s certification in Data analytics. You can attract potential employers by adding the shareable certificate to your profiles on LinkedIn and other social media platforms. Additionally, include it in your resume and CV to increase your employability in the eyes of recruiters.

The institute offers the best knowledge with its outstanding faculty, each of whom has years of expertise in their field, skilled experts with in-depth topic knowledge, and a deep understanding of effective instructional techniques. Before presenting you with more advanced concepts, tools, and practical advice, they cover the basics. But there’s more! Making the most of the acquired abilities will enable you to tackle challenges encountered in the field. Mentors provide trade secrets among other things to prepare you for potential issues at work.

Eligibility

IIM SKILLS has very few if any, pre-requisite requirements to be qualified for the course since we firmly think that education should be accessible to everyone. Anyone can start from the beginning to enter this cutting-edge sector if they are attracted by and have a keen interest in data analytics.

Eligibility Criteria

  • A bachelor’s degree in any related field.
  • Basic expertise in computers and technology.
  • A prior experience in statistics and mathematics might be beneficial but is not compulsory. 

Curriculum: IIM Skills offers a Data Analytics Course to Obtain Skills

  • Learn statistical theories: Correlation, central tendency, dispersion, and regression.
  • Learn the Python language’s conceptions.
  • Learn the variables ideas of conditions, loops, functions, etc. to develop Python programs.
  • Learn to use multi-dimensional arrays in NumPy, manage Data Frames in Pandas, and apply machine learning using Scikit-Learn to get a solid understanding of data interpretation in Python.
  • Learn to effectively use techniques including K-means, DBSCAN, and clustering with hierarchies.
  • Learn to use Excel to perform business analytics.
  • Learn knowledge about Microsoft’s Power BI analytics and visualization experiences.
  • Acquire in-depth knowledge of visualization techniques in advanced market analysis, waterfall visualization, and heat maps.

Some Legit Reasons to Choose IIM SKILLS:

  • IIM Skills is the most well-regarded ed-tech company in the world to offer the best professional courses.
  • It is best known for its extensive selection of professional and skill development courses.
  • Training that is 100% practical and goal-oriented.
  • 100% guaranteed internship for real-world experience.
  • Free lifetime access to educational materials.
  • Offers a completely free demo session.
  • Mentors offer advice and assistance in career-related.

Mode of teaching:  Online (virtual)

Course Duration:  6 Months + 2 Months Internship

Course Fees: INR 49990 Rs + 18% Taxes

TCS iON

TCS iON offers aspirants to build skills and enhance their knowledge of data with the theoretical concepts and processes associated with data analysis. For professionals as well as fresh learners in the fields of mechanical engineering, electronics and communication engineering, and computer science engineering, the “Data Analytics and Reporting” online certification program is offered. Students who complete the course will be prepared to work as data scientists, analysts, and modelers.

The objective of TCS iON is to offer an online Data Analytics course for students to gain a thorough understanding of data analytics principles, data pre-treatment, and the methods and tools used in data analysis. For students who meet the requirements, TCS’s iON digital learning hub offers this certification program.

This prestigious industry certification from TCS iON will teach the knowledge of data analytics for statistical and machine learning techniques. With the use of online classrooms, lectures, practical activities, and digital materials, the students are actively using virtual learning approaches. Academic experts and business leaders, including a TCS manager and associate professor from IIT Patna, serve as the course mentors. 

After completing the course, the online certification program in “Data Analytics and Reporting” provides students with the technical and professional abilities they need for further career growth or internships. Program learners receive digital certificates.

Eligibility Conditions

The minimum requirements for the “Data Analytics and Reporting” course are knowledge of basic mathematics, programming, data structures, algorithms, and statistics.

Criteria to Get the Certificate

Students can receive a certificate of success if they receive minimum scores of 40% on Part A, 50% on Part B, and an overall average of 50% on the summative assessment.

Students who complete the coursework, projects, assignments, and practice exams but are unable to write the summative assessment will still receive a participation certificate.

What Skills Do Students Acquire? 

  • Expertise in statistics, 
  • Data visualization, and data science
  • Machine learning
  • Technical expertise
  • Understanding of algorithms

The course is designed by well-experienced and qualified industry experts for students to comprehend the methods for assessing the characteristics of data. The students will be familiar with data reporting methods and obtain the necessary data. Learners will get the opportunity to engage with machine learning models throughout the program and create reports from data to derive insights.

Mode of course study: Online (self-study, virtual classroom)

Duration: 6 Months

Fee: 20,000 (inclusive of GST)

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Frequently Asked Questions (FAQs)

Q. What is the future of Data Analytics in upcoming years?

Future of Data Analytics

In the last few years, the percentage of internet users rose from 20% to 41%, and the figure is growing. Along with the consumers, the amount of data accessible online is growing daily, fostering the development of the data analytics sector. According to studies, the need for data analysts will continue to expand in the medium term more quickly than that of other professions.

Data analytics has had a significant impact on businesses since it enables them to make better decisions rather than relying entirely on assumptions. This is the primary cause of the steadily rising demand for data analysts across all industries. Learning from their data can help all organizations, enterprises, governments, and regulatory agencies. Thus, data analysts are needed in practically every organization.

Data analytics is revolutionizing how organizations operate. According to some studies, big data tactics will displace pilot projects at 75% of businesses by 2024. The enormous amount of data is proof that data analytics has a bright future and will keep expanding in the years to come.

Q. What You Need to Do to Become a Data Analyst?

  • You need a bachelor’s degree in any field to work as a data analyst. Although not a strict prerequisite, having a degree in mathematics, statistics, programming, or another quantitative discipline is encouraged. You should be interested in understanding these topics if you want to pursue a career in data analytics.
  • Many data Analytics courses are created for those with no formal education in these fields who are just entering the profession. In many situations, programming experience and an interest in data analysis may be adequate.

Q. What type of Skills are required for Data Analytics?

To become a data analyst; one needs to get knowledge of the following abilities:

  • Data analytics fundamentals: Learners will get familiar with techniques, procedures, and equipment employed in data analytics must be familiar to you.
  • Technical knowledge: Data analysts analyze huge datasets using a variety of mathematical and statistical techniques. Learners get expertise in mathematics, but they must grasp fundamental ideas like calculus, probability, statistics, and linear algebra.
  • Programming Knowledge: Some technical knowledge is also necessary. Python, R, and SQL are just a few of the programming languages you must learn and master.
  • Knowledge of Data Analytics Tools: Data analysts employ a variety of analytical tools, including Tableau, MS Excel, Google Analytics, and MS Power BI. One needs to be conversant with the data analytics technologies that enable better data collection, manipulation, and analysis.
  • Data analysts should also possess some additional soft skills, such as communication, problem-solving, presentation, analytical thinking, etc. These abilities can be acquired independently through consistent practice.

Conclusion Of this Read-IIM Skills vs TCS ion Data Analytics Course:

The subject of data analytics is well into its early phases of development; it is becoming a self-supporting discipline and creating people with unique and complementary skills in comparison to those in the computer, information, and statistical sciences. However, the evidence speaks to a vigorous expansion in data analytics education that will irrevocably alter the undergraduate students of the future, regardless of its likely ultimate disciplinary standing. In reality, because of rising corporate demand and student interest, undergraduate education in data analytics is set to become a mainstay. As the significance of data skills becomes even more widely acknowledged, there will be an increase in the number of students majoring, minoring, getting certificates, or merely taking courses in data analytics.

IIM SKILLS vs IIT Kanpur Data Analytics Course- A Comprehensive Discussion

by | Nov 2, 2024 | Courses, Data Analytics, Institute Review

Start your career in data analytics and be a successful professional in the technology field. This page about- IIM SKILLS vs. IIT Kanpur Data Analytics Course – A comprehensive discussion is a perfect read. Beginners or already working professionals who are looking for a successful career in data analytics will get a comprehensive discussion. This blog will evaluate both of these educational institutes’ and data analytics courses together so you may make crucial decisions that are suited for you.

 

IIM SKILLS vs IIT Kanpur Data Analytics Course

 

This blog on IIM SKILLS vs. IIT Kanpur Data Analytics Course will help our readers to understand data analytics from the basics and will include all the information related to data analytics to understand. Let’s start by defining Data Analytics.

 

Data Analytics

Data analysis is a systematic computing analysis of data or statistics. Data Analytics is commonly used today by businesses for the identification, explanation, and dissemination of huge and significant data patterns. It also incorporates data patterns to make decisions. Analytics, which heavily depends on the contemporary application of statistics, computer programming, and other operations research to quantify significant performance. It can be useful in sectors for financial information. The practice of data collection is used and created through Data Analytics software.

The Field of Data Analytics Crosses Several Disciplines:

    • To extract useful information from data through analytics.
    •  Substantial usage of computer expertise.
  • Usage of mathematics, statistics, the use of descriptive approaches, and prediction models. 
  • Use in unsupervised machine learning methods like cluster analysis, Principal Component Analysis, segmentation profile analysis, and association analysis.

The phrase “advanced analytics” is increasingly being used to refer to the technical components of analytics, particularly in the newer areas like the application of machine learning methods like neural networks, decision trees, logistic regression, linear to multiple regression analysis, and classification to perform predictive modeling. 

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Data Analytics is Used by Organizations for Many Purposes Such as:

Analytics of Marketing Optimization

Analytics are used by marketing firms to assess the results. Businesses run marketing campaigns and direct initiatives for customer targeting. Marketers can use a lot of consumer purchase, survey, and panel data together with techniques like conjoint analysis, demographic studies, and customer segmentation to comprehend and articulate their marketing strategies.

Quantitative and qualitative data, both structured and unstructured, are combined in marketing analytics to inform strategic judgments about brand and revenue outcomes. Utilizing the data, businesses can predict outcomes and change their strategic planning to improve performance.

Marketing approaches to pricing, advertising and modeling analyses, sales force optimization, and customer analytics, such as segmentation, are analysis methods that are often employed in the field of marketing. Nowadays, more often than not, more traditional marketing analysis techniques are used in conjunction with web analytics and optimization of websites and online campaigns. Because of the emphasis on digital media, marketing mix modeling is now frequently referred to as attribution modeling in the context of digital or marketing mix modeling.

The strategies, approaches, and technical tools, support operational initiative support, which aims to target the best potential customer with the best message in the most cost-effective medium at the best time, and more strategic campaign support, which focuses on how much overall to spend on marketing and how to allocate budgets across a portfolio of brands and the marketing mix.

 

Analytics of Human Behavior 

To understand better workforce capability and alter how businesses are run, statistical analysis of human behavioral data.

Workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and HRIS analytics are other terms for people analytics. Analytics applied to human resource management is known as HR analytics. It has been claimed that People Analytics is a distinct field from HR analytics, with a stronger emphasis on business challenges than on operational procedures. However, experts have different opinions, with many suggesting that in the changing workplace brought on by automation, Human Resources will need to develop People Analytics as a crucial component of a more effective and strategic corporate role. Some experts contend that People Analytics should remain within HR rather than being moved there but with the help of a new breed of HR professional who is more data-driven and business-smart.

 

Analytics for Business Portfolio

Data analytics is also used for business portfolio analysis. Portfolio analysis is a typical use for business analytics. Here, a bank or lending organization has a variety of accounts, each with a different value and level of risk. The accounts may vary according to the holder’s social position (rich, middle class, impoverished, etc.), location, net value, and a variety of other characteristics. For each loan, the lender must strike a balance between the return on the loan and the default risk. How should the portfolio be assessed as a whole then?

Although there aren’t many affluent people, lending to them can be the lowest-risk option. However, lending to the impoverished is riskier and there are a lot more of them. It is necessary to strike a balance between increasing yield and reducing risk. To decide when to lend money to these various borrower segments, or to decide the interest rate to be charged to members of a portfolio segment to compensate for any losses among members in that segment, the analytics solution may integrate time series analysis with many other challenges.

 

Analytics of Risk

In the banking sector, predictive models are created to offer certainty to the risk scores for specific consumers. Credit scores, which are frequently used to assess each applicant’s creditworthiness, are created to anticipate a person’s propensity for delinquency. Additionally, both the scientific community and the insurance sector do risk studies. To determine whether a transaction is legitimate or fraudulent, it is also widely employed in financial institutions like online payment gateway businesses. They exploit the customer’s transaction history for this purpose. When there is an unexpected increase in the volume of client transactions, this is more frequently utilized for credit card purchases. The consumer receives a call to confirm whether the transaction was initiated by him or her. This aids in minimizing loss brought on by such events.

 

Digital Analytics

A collection of technological and business processes known as “digital analytics” is used to define, develop, gather, validate, or transform digital data into reporting, research, analyses, suggestions, optimizations, forecasts, and automation. This also applies to SEO (search engine optimization), where keyword searches are monitored and the resulting information is used for marketing.[31] Digital analytics includes banner advertisements and clicks as well. For their digital marketing projects, an increasing number of brands and marketing agencies rely on digital analytics, where MROI (Marketing Return on Investment) is a significant key performance indicator (KPI).

 

Analytics for Security

Information technology (IT) gathering security events for understanding and analyzing those that offer the highest risk is referred to as security analytics. Products in this category include event management, user behavior analytics, and security information.

 

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IIM SKILLS vs. IIT Kanpur Data Analytics Course – Let’s discuss

IIM SKILLS

IIM Skills offers a 6-month-long Data Analytics Training program that also includes a 2-month, elective, unpaid internship. You achieve proficiency in a wide range of topics, including NumPy, clustering, DB Scan, Python libraries, and many more ideas.

The courses cover a wide variety of topics, combining both theory and live practical, that are relevant to a range of professions and businesses. The extensively designed curriculum by experts aids in students’ skill development and ensures they receive the greatest education. They can transform their passions into successful and fulfilling careers by determining their passions and developing their skill set. 

 

IIM SKILLS is the Best Choice for the Following Reasons:

  • IIM Skills provides lifetime access to the study materials and class recordings.
  • It offers career assistance and advice in job placement.
  • IIM Skills provides demo classes for learners, if they are not satisfied with demo classes, can withdraw their enrolment immediately after the first class.
  • A 2-month internship is also included in the program for practical professional experience and certification.
  • Learners will be able to get updated skill-based training that is useful and based on the latest trends.
  • One of the most crucial reasons for choosing IIM Skills is that learners can study remotely in their comfort zone under the best guidance. 
  • IIM Skills has the best knowledgeable and industry-expert faculty members who are proficient in their subjects.
  • IIM Skills conducts classes at flexible timings, so both students and professionals can take their classes without any misses. Classes are mostly scheduled on weekends, if someone misses due to their immediate obligations, class recordings are also available for them to cover the syllabus.
  • 10% of group discount on group registration.

 

Modules

  • Data Analytics Using Advanced Excel (9 Hours)

Topics included

  • Introduction to Data Handling
  • Data visualization in Excel
  • Data manipulation and its function
  • Data analysis and its reporting
  • Dashboard
  • Dashboard creation by using Excel- Pivot Controls

 

Course Content

  • Introducing Excel
  • Formatting and Conditional Formatting
  • Data Sorting
  • Data Validation and Filtering
  • Data Analytics Using VBA (15 Hours)

Topics include

  • VBA
  • Primary elements of programming language
  • What VBA is and how does it work with Excel?
  • Programming construction in VBA
  • VBA functions and procedures
  • Memory management in VBA
  • User’s communication

 

Course Content

  • What is VBA?
  • Macro recordings and IDE
  • Data Analytics Using SQL (39 Hours)

Topics covered

  • RDBMS conceptualization
  • Utilising and exploring the object
  • Data manipulation
  • Object creation based on Data
  • Reporting services (SQL server)
  • Integration services (SQL server)

 

Course Content

  • Schema – Meta Data
  • Database design
  • Data integration- types of relationship
  • Basic concepts- Queries, Data types & NULL values, operations, and SQL comments

Data Analytics Using Power BI (24 Hours)

  • Introduction of Data Preparation
  • Introduction of Data Modeling
  • Learning to add and remove rows in columns
  • Data Analysis Expression (DAX)
  • Report management visuals in Power BI

 

Course Content

  • Introduce Power BI
  • Power BI installation on desktop (signup)
  • Power BI views
  • Apps templets
  • Task pipeline while working on projects

Data Analytics Using Python-1(10 Hours)

  • Foundation of basic statistics
  • Basic Mathematical Introduction
  • Basic introduction to Data Science and Analytics
  • Python
  • Numerical Python
  • Foundation of Pandas
  • Data cleansing (with Python)

 

Course Content

  • Basic Statistics
  • Computation of variance and central tendencies
  • Frequency computation
  • Ranking
  • In-depth knowledge of Probability & Distribution
  • Conditional Probability

Data Analytics Using Python-2 (10 Hours)

  • Data Analysis with Python
  • Basic tools for visualization
  • Data Visualization
  • Advanced Data Visualization
  • Statistical and hypothesis method, testing, etc

 

Course Content

  • Exploratory Data Analysis
  • Descriptive statistics
  • Summarization
  • Frequency Tables
  • Data distribution &Graph Analysis

Data Analytics Using Tableau (15 Hours)

  • Introduction of Data handling
  • Introduction of summaries-1
  • Introduction of summaries-2
  • Advance report building
  • Fields & Table calculation

 

Course Content

  • Introduction of Tableau
  • How Tableau works
  • Data Introduction, connection, source, and concepts
  • Tableau workspace comprehension
  • Types of Data and properties
  • Shelves and Marks card
  • Work overview

R For Data Science (20 Hours)

  • Data Importing
  • Data exporting
  • Data Analysis & manipulation
  • Using R for Data visualization and database
  • Basics of statistics
  • Testing Problem-solving 
  • Course Content
  • Data import & Export
  • Data export in various forms
  • Data import in various formats
  • Full and partial Data viewing
  • Data types and structure
  • Concepts of base and other useful packages

Data Analytics Using Alteryx (18 hours)

  • Use of Alteryx for Data Analytics

 

Course Content

  • Basic concepts of Alteryx
  • Introduction of tools for Data type change
  • Comprehensive understanding of Alteryx settings
  • Data source connection
  • Data set types
  • Designing Interface
  • Data cleansing
  • Recognising Data fields
  • Use of string Data for filtrations
  • Blending data from various sources

More Details

Data Analytics Master Course

Duration: 6 Months Course

Internship Duration: 2 Months Guaranteed Internship

Training Period: 160+ Hours Live Training & 100+ Hours Theory & Practical

Tools Learning: Learning from Tools

Placement: Regular Job referrals & Interview Guarantee

Fee: INR 49,900 + inclusive Taxes

IIT Kanpur Data Analytics Course

IIT Kanpur offers Advanced Data Analytics Certification. The Data Analytics Course from IIT Kanpur is a great learning process. The faculty team is well qualified and expert in their respective subjects Also they are very quick in response and doubt clearing of students.

Students will be able to learn in-depth business analytics concepts, analytics techniques, and R language programming. Apart from this, the study material is comprehensive, engaging, application-oriented, and practical more than just theory. The case studies and assignments offer students to learn various concepts and theories of different programming, Some tests are technical analytics and more understanding while improving your comprehension.

With core competencies in the highly sought-after fields of data analytics, statistical modeling, and data visualization in R, Tableau, and SQL, IIT Kanpur is the top-ranked institute of national importance in India.

 

Some Significant Reasons to Choose the Data Analytics Course From IIT Kanpur Such as:

  • Receive a certificate from IIT Kanpur with comprehensive job support that is endorsed by the Indian government.
  • Obtain a certificate from IIT Kanpur with full work support that has been approved by the Government of India.
  • 70% of the training will be practical.
  • 7 commercial projects across various industries
  • 7 commercial projects across various industries
  • IIT Kanpur offers a full refund 6 days after the start of classes, if students are not satisfied with our teaching then they have the option to cancel their enrolment.
  • Well-experienced, elite academicians from IIT Kanpur and leaders in their fields from TCS to Accenture.
  • 24×7 access to the course material and videos.

 

Students From IIT Kanpur Get Placement in Some of the Most Reputed Companies Such as:

  • Cognizant
  • TIGER ANALYTICS
  • Accenture
  • PWC
  • Deloitte
  • TATA Consultancy Services
  • AON
  • LARSEN &TOUBRO
  • Capgemini

 

Course Curriculum

Data Analytics in R

  • Introduction of Analytics
  • Basic structure of R
  • Fundamental Statistics using R
  • Logistic and Linear Regression in R
  • Marketing analytics using R
  • Segmentation, Time series foretelling R
  • Decision Range using R
  • Random Forest
  • XGBoost in R
  • Problem Solving
  • Understand Analytics with Actual Business problems

Data Analysis using SQL

  • Fundamentals of SQL
  • Data Types & Operations (SQL)
  • Trending Operations in SQL
  • Comprising Data using SQL
  • Functions in SQL
  • DML and DDL Statements (SQL)
  • Joining and Viewing using SQL

Use of Tableau in Data Visualization

  • Introduction of Power BI, Tableau & Visualization
  • Data Sources Connection
  • Tableau Producing Fields
  • Data Customization & Manipulation
  • Parameters of Statistics
  • Creation of Dashboard & Visualization

Data Analytics using Python

  • Fundamentals of Python
  • Loops and Function
  • Data Summary
  • Data Visualization using Pandas
  • Data Processing using NumPy

Through this program, which is offered through live sessions with industry experts, hands-on laboratories, and live masterclasses from renowned IIT Kanpur faculty, you can develop your data analytics abilities and learn how to demystify data. Enhance your ability to apply analytics by learning important topics.

 

Advanced-Data Analytics Certification

Eligibility: Graduate in any field | Technical background not required

Duration: 180 Hours

Training: Live Training & 100+ Hours Theory & Practical

Tools Learning: Tools-based training

Placement: Regular Job referrals & Interview Guarantee

 

IIM SKILLS vs. IIT Kanpur Data Analytics Course Conclusion

If you have a keen desire to learn a course that will be not just different from the traditional profession but also help you to grow in your career in constant or advent digital transformation then the Data Analytics course is ideal for you. You need to choose your course and institution more careful

Data analytics has recently become one of the most sought-after occupations in India. To help them make sense of the data explosion brought on by the digital age, businesses are looking for qualified individuals. Numerous data analytics courses have appeared in response to this need, each offering to give students the knowledge and abilities necessary to succeed in this industry. Despite this, some courses provide more placement support than others and are not all created equal.

In conclusion, the aforementioned learning platforms are well-regarded and offer top-notch Data Analytics courses. By concentrating on real-world projects and industry-specific themes, students can get practical experience and information that will help them in their careers. 

I hope the information in this blog-IIM SKILLS vs. IIT Kanpur Data Analytics Course will be helpful in making the right choice for the course and educational institution.

 

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Frequently Asked Questions (FAQs) on this read- IIM SKILLS vs. IIT Kanpur Data Analytics Course

Q. Is studying a data analytics course beneficial for a career?

The Data Analytics course is worthwhile for the present and future. It offers several career options in different positions but in the end, it all comes down to one’s effort and professional objectives. Assessing the value of a course in data analytics depends on account the following factors:

  • Career opportunities in Data Analytics: As organizations highly rely on data-driven insights for business decision-making, data analytics is in rising demand across industries. Course in data analytics can lead to employment in several different fields, including data analysis, business analysis, data science, and data consulting. These jobs frequently have vacancies for constant advancement and lucrative reimbursement.
  • Skills: A training program in data analytics offers learners a well-structured curriculum and live practical instruction on several topics like data manipulation, machine learning, data visualization, statistical analysis, and language programming. These technical skills are beneficial in the present job sectors, and market. These skills can increase your employability in different sectors that rely heavily on data.
  • Advanced comprehensive knowledge: An advanced understanding of data analytics principles, processes, and best practices is provided via a program. The course in Data Analytics helps learners to achieve comprehensive statistical details and modeling. The data management and the moral issues related to the use of data can stand out among applicants without a formal education in the subject if they possess such particular knowledge.
  • Networking and connections: Earning a certification/diploma in data analytics gives learners the chance to maintain connections with professors, peers, and business leaders. These relationships may be useful for prospective employees in future partnerships, job placements, and internships. Career development of beginners may also benefit from the advice and insights offered by networking/ connections of academic members and seasoned professionals.
  • Credentials and credibility: Skills and credentials will be more sustainable if the course in data analytics is from a valid educational institution. Employers value such achievement if it is a specific level of academic proficiency and a thorough comprehension of the subject. 
  • Recognition in the industry: Businesses demand such professionals who have training in data analytics. They might favor applicants who have proven a dedication to learning and have accumulated a wide range of abilities through a program. In these situations, having a course in data analytics can help the application stand out.

The value of a data analytics education ultimately depends on your objectives, the particular curriculum learners choose, the employment market, and the capacity to make use of the knowledge and skills have acquired throughout studies.

Q. How Can Data Analytics Work for Business?

Data analytics is very useful for businesses because it helps in making better decision-making, identifying new market trends, updates, and patterns after data research, and improving the overall financial performance of a business’s goals.

Q.  What Tools are Typically Used for Data Analytics?

Tools and Technologies

  • Python and R
  • statistical programs  
  • SAS and SPSS
  • Visualization tools like Tableau 
  • Power BI.

IIM SKILLS vs. Loyola College Chennai Data Analytics Course

by | Nov 2, 2024 | Courses, Data Analytics, Institute Review

Do you want to shape your career in the technical field and are looking for one of the most trending options? Data Analytics and Data Science are some of the most lucrative and trending options in the technical area. In this frequently growing technical world, you must witness the jobs in Data Analytics and Data Science constantly growing due to its required technical field, and the use of analysis tools is in demand for business. This article- IIM SKILLS vs. Loyola College Chennai Data Analytics Course will help in your search for Data Analytics courses and data Science courses and education institutions. Loyola College offers a data science course and hence we are going to make a detailed analysis of the two courses so that you can make an informed decision regarding which of the two data courses to enroll in. 

IIM SKILLS vs. Loyola College Chennai Data Analytics Course

Businesses have started recognizing the growing need to understand and manage the data they are generating in business and the demand for continuous progress. This regular demand for data management catalyzed the need for data scientists and data analysts to continue the growth. Interested students in a data analytics and data science career will work on data modeling, statistics, programming, and other analytical tools. Although data manipulation is a part of both the data science and data analytics professions, they are distinct from each other.

Data Analytics vs. Data Science

Businesses are using data analytics and data science to make better decisions, boost productivity, and maximize the company’s profitability.

Data Analytics

Data analytics experts are in demand for being responsible for data compilation, systematic classification, and maintenance, as well as obtaining insights from data through using several technical tools such as statistics, mathematics, programming, and other approaches. A data analyst’s job is to identify trends and support different problem-solving. Order tracking, recommendation, and other identification are all examples of data analytics in retail.

Data Analytics is the statistical analysis of current data records to solve recent problems. It intends to deliver outcomes that will result in quick changes. Data analytics combines several forms of data to detect diverse connections and streamline results.

Data analysts typically respond to decision-makers’ demands rather than driving the decision-making process.

Essential Data Analytics components:

The following are the primary components of data analytics:

Mining

Data mining is one of the most important components of comprehensive data analytics. It is one of the core elements since it employs cutting-edge analytics methods to reveal the most important features of data sets. Data mining is essential for successful analytics projects in enterprises. Real-time analytics systems that analyze streaming data as it is created or collected, as well as business intelligence (BI) and advanced analytics tools that evaluate and show earlier data, are produced by the data.

Cleaning

Data cleaning is the most important resource for machine learning and analytics. Data is essential in both the corporate and technology industry. In the computer software industry data mining works as a key component. Data cleaning, also known as data cleansing or data manipulation, is an important step in the data analytics pipeline. Data cleansing entails more than just removing inaccurate data.

Modeling

Data modeling is the process of storing information, mapping, and visualizing the complete technique for consistent data usage. Data modeling is used by organizations to explain the many types of data they use, the links between them, and how to organize them. Data modeling is a technique for optimizing data to improve how data moves around businesses for varied business demands.

Data Analysts’ Jobs and Responsibilities

A Data Analyst is proficient in gathering data from many sources and compiling it. Data is generated by businesses in the form of records, customer feedback, transaction data, and so on. Data Analyst is responsible for turning business-driven data into meaningful insights. Data analysts analyze data sets by using different approaches of data manipulation techniques to assist businesses in better or more intelligent decisions. 

Job Description 

  • Report Distribution
  • Trend analysis
  • Collaboration with Stakeholders
  • Consolidating data and establishing infrastructure

Data Science

Data Science works with unstructured data and focuses on discoveries and projections. Data analytics seeks patterns and conclusions by analyzing previously arranged and collected data. Although Data Science is a broader term, it includes Data Analytics to discover new ideas and address analytically difficult business problems utilizing advanced approaches such as machine learning and predictive algorithms.

Data Science is an interdisciplinary field that extracts insights and knowledge from big and complicated data sets using statistical, mathematics, and computer science tools. It tries to answer future questions and solve issues by analyzing massive amounts of structured and unstructured data. Data science is used in a variety of industries, including healthcare, finance, marketing, and operations management, and it is always expanding because of the massive amount of data collected every day.

Essential Data Analytics components:

The following are the primary components of data analytics:

Statistics

Statistics are essential in a Data Scientist’s daily operations and can be considered one of the Data Scientist’s necessary skills. Simply put, statistics examines a set of data for notable trends. It is divided into two sections: descriptive statistics, which attempt to explain trends in existing data, and inferential statistics, which attempt to develop estimates about future data.

Visualization

Data visualization requires comprehending raw records and determining what needs to be exhibited based on the objectives and goals of individuals utilizing it as well as the processes, not just effectively presenting processed data. It improves discoveries, accelerates decision-making, boosts the capacity to keep consumers’ attention, evolves away from the requirements for data scientists, and simplifies the distribution of generated information. 

Machine Learning

Machine Learning is a vitally important part of data science since it allows self-learning computers to accurately foresee natural behavioral patterns in specific contexts. Machine learning is the process of using algorithms to collect data, analyze it, and anticipate future patterns for a subject.

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Data Scientists’ Job Responsibilities:

Data Scientists are those experts who comprehend business difficulties and attainability to devise the finest solutions by using advanced technologies and alternative methodologies. To envision forecasting summaries and handle complicated problems, they employ statistical methodologies, significant approaches, data visualization tools, and machine learning systems. Data Scientists extract beneficial, critical information and insights for corporate leaders and other contributors.

Data Scientists use statistical approaches, data visualization tools, and machine learning blueprints to build prediction models and solve complex issues. Data Scientists extract useful information from unstructured and chaotic data. They also provide crucial facts and insights to company executives and other parties.

Data scientists are altogether problem solvers. They aim to identify the issues that require solutions and then devise various techniques to solve the problem. Daily, a data scientist may be responsible for the following data-related tasks:

  • Data extraction
  •  Merging and analysis
  • Trends, patterns, and predictions
  • To execute the design of different mechanisms and technologies for a wide range of information by using Tableau, Python, Advanced Excel, and others.
  • Simplify data and the modern development complexity of certain models 
  • Concepts of data visualization 

Equivalence of Data Analysis and Data Science

  • Data visualization: Data visualization techniques are useful in communicating data findings and observations in a visual and intelligible style.
  • Machine learning: Machine learning correlates in both Data Science and Data Analytics to construct forecast models and get in-depth observations from data.
  • Data-driven insights: To draw insights and make more informed conclusions, both fields are highly dependent on data analysis and interpretation.
  • Approaches: Statistical approaches and techniques are used in both Data Science and Data Analytics.
  •  Data Science and Data analysis both professions include the exploration and evaluation of massive data to discover market moves, patterns, and variations.
  • Programming: To deal with data analysis, Data Science and Data Analytics require a good command of programming abilities in languages such as Python or R.

Data scientists and data analysts both roles are both equally essential for business, but the level of experience and expertise decide any professional’s key roles and responsibilities in industry and organization.

IIM SKILLS vs. Loyola College Chennai Data Analytics Course & Data Science Course

IIM SKILLS

IIM Skills is an online education site, which provides several professional courses globally to students who have a desire to learn from home/anywhere in their comfort zone. Students can explore various skills development courses. IIM Skills growing its global reach to provide in-depth knowledge about different professional skills for aspirants.

The Data Analytics course from IIM Skills will equip beginners/working professionals with the fundamental theoretical, technical, and practical efficiency required to become an expert data analyst. With an expert faculty, students will have access to lifetime study materials and guidance from industry specialists. Students will get an opportunity to work on real-life business cases with software tools to prepare for a career in data analytics after course completion.

The Data Analytics course modules have been designed with the help of well-qualified industry experts and professionals; these experts will conduct classes. Students will get their help to explore advanced technical tools in data analytics. Students will get knowledge of subjects including basic finance, mathematics, statistical modeling, and the fundamental structure of forecasting. Students will have the chance to develop their basics by asking regular questions in class or off class so that they can get good opportunities after course completion.

Objectives of IIM SKILLS

Studying Data Analytics from IIM skills will provide learners to achieve exposure to industry methods and real-life case scenarios:

  • IIM Skills offers an online platform for its learners, be they students, professionals, homemakers, or people desiring to switch their careers. They can attend their classes at home or anywhere. It reduces unnecessary travel stress and time.
  • IIM SKILLS prioritizes assisting its students in the best possible way and letting them understand the primary tools required in the data analytics business. As a result, students will have the necessary tools and software skills.
  • Students will be able to distinguish themselves and contribute to demonstrating their professionalism in work. It will allow you to maximize both your untapped potential and the value of your data drilling.
  • The Data Analysis course at IIM Skills will provide abilities to establish a path for students to build industry-relevant crucial and contemporary skills to grab the opportunity to find lucrative jobs in a respectful work environment.
  • IIM Skills designed the syllabus to allow their applicants to completely educate themselves with knowledge of many different technology-related sectors. Furthermore, the course emphasizes the use of software tools and methodologies to address real-world business problems.

Course Module:

  • An Introduction to R 
  • Data import, data manipulation
  • Introduction to data handling
  • Basic graphical methods and numerical summaries
  • Writing simple reports of data analysis 
  • Basic concepts of statistics (inferential statistics)
  • Introduction of graphical summaries and numerical summaries (mean, median, standard deviation, and correlation)
  • Introduction of Power BI; it is a Microsoft cloud-based analysis service
  • Introduction of Tableau; It is a business intelligence application for analyzing and displaying data, assisting in dashboards, charts, graphs, maps, etc.
  • Introduction of Excel for data addition, tracking patterns, market trends, and other data management.
  • Python; language programming python for data management and manipulation of data structure.

IIM Skills offers a most demanding course in data analytics with a trial demo option to learn and evolve the career of their learners in new technology. Master certification in Data Analytics will provide interactive sessions and discussions in live classrooms by professional teachers. The teaching methodology of IIM Skill is unique; it provides special guidance to its students if required. Students can connect with their mentors/teachers anytime for their questions or career-related dilemmas if they require it. Students will get internships, career guidance regular job referrals, etc.

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Loyola College Chennai

Loyola College started the Data Science department in June 2019. Loyola College Chennai aims to provide inclusive and empowering learning opportunities to promote scientific curiosity for effective social transformation, platforms, and resources for the creation and sharing of innovative knowledge. The institution helps students develop a sense of responsibility and employability skills.

Loyola College Chennai offers an M.Sc. (Data Science) Programme to provide students with comprehensive knowledge and exposure to data science in flourishing technology. The PG Programme will lay a theoretical framework for students to develop their foundational ideas. 

Data science expertise and customized technical learning will help in extracting insights from very big data sets.

Objectives of Loyola College

  • To develop Work ethics, and analytical, logical, and problem-solving critical abilities for executing professional skills and becoming experts in the field of data sciences.
  • To gain access to academic facilities in an inclusive and inquisitive environment for effective and immersed learning throughout life to achieve greatness in the chosen subject of computational sciences. 
  • To think creatively, analyze scientifically, and make sound decisions to address modern global issues using the knowledge gained in the computational sciences curriculum. 
  • To succeed in fast-growing modern technical approaches and compete in higher career prospects by addressing contemporary difficult problems with ease.

Course Module

  • Fundamentals of Data Science
  • Fundamentals of Mathematics
  • Probability and Statistics
  • Use of Python in Data   Science
  • Machine Learning
  • Statistical Inference
  • Understand Big Data Analytics
  • NoSQL Database
  • Market Analytics
  • Health Analytics
  • Research & Variety of Data Analytics
  • Deep Research
  • Apply Cloud Computing
  • Visualization

Intent of Data Science Program

  • Students will be able to identify, analyze, and resolve data analysis problems by using relevant skills like mathematics, Statistics, computing sciences, and fundamental principles of other disciplines.
  • Students will be able to acquire all necessary skills in handling data analytics problems. They will acquire programming tools and languages.
  • To Understand the professional ethics and cyber rules & regulations, liabilities, and other standards of professional computing applications.
  • Learners will be aware of the statistical approaches and applications to resolve factual problems by using data analysis.
  • To master the advanced theory of Big Data and apply the mechanism to design a program to achieve analytical skills that assist in business decisions and planned goals.
  • Prospective employees will be skilled Data Scientists in the industry by achieving expertise in all contemporary software tools and applications for data analysis, storage, and visualization.
  • Beginners will get the research-based knowledge to apply, and analyze to solve crucial and advanced issues by using data managing methods.

This degree course is known as M.Sc (Data Science). The college has appointed experienced staff members with the best qualifications in their respective fields of specialization and market expertise. Data science can be used to help citizens and the country in a variety of fields, including finance, healthcare, education, and even security and governance.

Students will have regular class interactions, and the PG Programme will include many guest lectures from industry leaders, extensive lab work, and discussion of various business case studies. Students will participate in an internship program at the end of the second semester and complete a major project in the fourth semester.   

Conclusion of this read IIM SKILLS vs. Loyola College Chennai Data Analytics Course & Data Science Course- An Inclusive Courses Comparison.

Data science vs. data analytics has always most talked about topic among students. Data science and data analytics are equally important around the business world and could lead young professionals to achieve excellence in their careers. Understanding the distinction between data science and data analytics can assist you in making the best decision.

In this article, we have covered the difference between Data Analytics and Data Science, essential components, and course modules, and discussed two educational institutes. IIM SKILLS is an online platform, that provides Data Analytics courses on the other hand Loyola College is an offline regular college; that provides Data Science degree courses. Both educational institutions have well-qualified faculty members from relevant industries and provide placement and internship help to their students. IIM Skills and Loyola College, both institutions offer various courses to their students so students can excel in their desired career fields and have amazing career opportunities.

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Frequently Asked Questions (FAQs) on IIM SKILLS vs. Loyola College Data Analytics Course

Question 1) Can a candidate without a technical background acquire analytics skills?

Answer: Yes. A candidate without a technical background can learn data analytics and data science courses. With a keen interest in logical analysis and business, any student with whatever background is an ideal candidate for data Analytics and science.

Question 2) What are some lucrative job roles in data science?

Answer: Data Scientists are in demand not only in technology but in all major industries. Data science career options are extremely lucrative and diverse. Choosing a career in data science will provide young or experienced professionals with a variety of career options and job titles.

Data Scientists- This is an important role in all major industries. Data scientists should have in-depth knowledge of statistics, and different programming languages for complex problem solving. Data Scientists can illustrate the insights of any data in a manageable method to be acknowledged by everyone.

Data Engineer- Data Engineers are the backbone of the industry as they work with the organization’s core to create, design, and manage a massive database structure. They are responsible for constructing data pipelines and managing data flow to ensure that data reaches the appropriate sections of the organization.

Business Analyst- A business analyst is responsible for analyzing data to maximize the company’s efficiency and generate more revenues. Their role is more technical as it requires a greater understanding of contemporary mechanisms to link business and IT for improvement. 

Marketing Analyst- A marketing analyst’s job is to examine and recommend the manufacturing and marketing of products and services in large quantities. They work to monitor customer satisfaction and rate their satisfaction. It helps businesses to improve products and services. 

Machine Learning Engineer- A machine engineer optimizes data and operations. A machine engineer works to draw patterns and implement systems based on research to optimize machine learning algorithms and the performance of tests.

Question 3) Which career option is good; is data science or analytics?

Answer: Data analytics emphasizes the existing data review and working with data observations to resolve complex business issues. The data analyst’s job is to decode the real problem and coordinate with different employees to uncover the findings and collect observations.

A data scientist works to know the source of the existing data to optimize data through a system. Interested candidates in more technical knowledge like machine learning and data modeling, probably like a career in data science. 

Whatever career path candidates choose, must keep in mind that organizations value both data scientists and data analysts. So, choose accordingly and keep in mind that both have a robust job market.

IIM SKILLS vs. IIT Madras Data Analytics Course Review

by | Nov 2, 2024 | Courses, Data Analytics, Institute Review

Technology is playing a bigger role in every sector of businesses to collect relevant data, conduct meaningful operations, etc. They monitor client data, corporate performance, and sales trends. In this era of technology and the digital revolution, learners can review this highly demanding course and make a successful career in the thriving job market of data analytics. In this read- IIM SKILLS vs. IIT Madras (IITM Pravartak) Data Analytics Course, we will include two educational institutions and the required details about data analytics.

IIM SKILLS vs. IIT Madras Data Analytics Course Review

Data Analysis

After gathering data using empirical methods, the following stage is data analysis and interpretation. The distinction between data analysis and interpretation is difficult to make since the two processes are symbolic and blend invisibly. Analysis and interpretation are intricately linked.

The analysis involves a thorough study of the gathered data. Data analysis leads to generalization.

The analysis of generalizations and findings is referred to as interpretation. A generalization is a conclusion about an entire group or category based on knowledge collected from specific cases or examples.

A deep search for the significant findings is known as interpretation. Data analysis will be performed about the study’s purpose.

Data should be examined and structured in light of hypotheses or research questions to produce responses to the research questions.

Data analysis can be presented in both descriptive and graphic form. It can be presented in the form of charts, graphs, or tables.

Data analysis contains several activities, including data classification, coding, tabulation, statistical data analysis, and conclusions related to other variables.

Proper and effective analysis aids in classifying and organizing disorganized material, as well as providing scientific shape. Furthermore, it aids in the analysis of trends and changes that occur throughout a specific period.

Methods of Data Interpretation

Analysts use data interpretation methods to assist individuals in making meaning of numerical data that has been collected, evaluated, and presented. A raw form of data might be difficult for the analysts to grasp; therefore, analysts disintegrate the information so that the data processing can become hassle-free. There are two basic ways to accomplish this: quantitative approaches and qualitative ones.

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Method for Interpreting Qualitative Data

The qualitative data interpretation method is useful in surveying categorical and undiluted data, also known as qualitative data. This approach uses text in data rather than numerical or models. Proportionately the quantitative mechanism of research, and qualitative data are consistently collected through a wide range of person-to-person survey techniques that might be challenging to interpret. In contrast to quantitative data, which can be examined thoroughly and immediately after being collected and sorted, qualitative data must first be coded into numbers before it can be analyzed. This is because analyzing texts in their original state takes more time and results in many inaccuracies. 

The analyst’s coding should also be documented so that it can be reused and evaluated by others. There are two sorts of qualitative data: nominal data and ordinal data. Both of these data kinds are interpreted using the same procedure, although ordinal data interpretation is much easier than nominal data interpretation. Ordinal data is typically labeled with numbers throughout the data-collecting process, and coding is not always required. This is distinct from nominal data, which must still be tagged for correct interpretation.

Method for Interpreting Quantitative Data

The quantitative data interpretation method is used to assess numerical data, also known as quantitative data. This data type comprises numbers and is hence processed using numbers rather than textual. There are two forms of quantitative data: discrete data and continuous data. Continuous data is further subdivided into interval data and ratio data, all of which are numeric. Analysts do not need to use the coding procedure on quantitative data before analyzing it because of its natural existence as a number. Analyzing quantitative data requires the use of statistical modeling techniques such as standard deviation, mean, and median.

Data Analytics Requirement in Businesses

It is deemed a fact that data analytics techniques are beneficial for businesses’ flourishing of all parameters. It assists marketers in designing and accommodating seamless customer journeys and modifying content accordingly to fit the fashion.

According to sources, despite access to consumer data, most marketing professionals claim trouble making data-driven decisions ranging from forecasting customer purchases.  

The difficulty is having access to the technical capability of evaluating large amounts of data instantly. While many businesses are collecting vast amounts of data, it has become difficult for them to handle extensive data efficiently and proficiently.

Businesses in all industries can benefit from data analysis, which allows them to stand out from the competition. Such techniques may also detect potential problems or prevent fraud, notably in the pharmaceutical or financial sectors.

The goal for e-commerce enterprises is to exploit data to their advantage. These businesses gain a better understanding of their customers’ wants and habits by analyzing their users’ browsing behavior. 

This information then assists firms in increasing their profitability. It also allows the corporation to promote things that specific consumers are more likely to order and purchase online.

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Understanding About Data

A corporation or any organization tackles vast volumes of data, and it is a heavy responsibility to handle and develop a long-term strategy and purpose to run a business without any data disruption. Data storage is expensive, complicated, and crucial but processing the data is considerably more so. 

As a result, it is critical to outline your company’s data goals ahead of time, such as “What data sources do you currently have and how are you currently using data?”

What information do you have and want to get for further findings?

Do you wish to learn more about your clientele, for example, or are you taking preventative measures to prevent fraud?

Do you have business goals in place that align with your big data strategy?

Importance and Integrity of Data Evaluation

Data analysts are responsible for determining the true worth of your data. How did the data come about? Inaccurate, flawed, and worthless information may have been gathered haphazardly.

It has been discovered that two-thirds of sales leads do not close due to poor data quality and that up to 25% of client and prospect records have important data inaccuracies that have a direct impact on sales.

So, before spending time and money on data analysis, data professionals check the accuracy of your information. This will assist you in determining whether the data will provide any useful insights. If it does not, gather more accurate data before proceeding.

Thriving Job Market in Data Analytics and Interpretation

Data analytics is used to find and interpret significance in data patterns. As data becomes more crucial in business decision-making, the data analytics employment market is growing.

Global firms will regard data analytics and predictive analytics to be critical components of their operations. Furthermore, as more businesses engage in data and digital assets, the demand for data analysts and experts is likely to grow.

Whether you want to enter the data analytics industry as a recent graduate, make a career shift, or brush up on your abilities, it’s critical to grasp the current environment of the data analytics employment market:

The global market for data analytics is expanding daily. The market is predicted to increase at a compound annual growth rate (CAGR) over the next half-decade, reaching considerable levels by 2028.

It is projected to maintain its global market leadership.

Job Categories in Data Analytics

Data analytics is diverse, despite its fundamental concept of analyzing data to extract information, insights, and forecasts. To get the required results, data analytics frequently overlaps with other disciplines.

Finance, marketing, and business skills, for example, can be integrated with data analytics to provide extremely targeted insights and predictions. These tasks are carried out by:

  • Analysts in Finance
  • Analysts in marketing
  • Analysts of business intelligence (BI)

In terms of the technical aspects of data analytics, computer scientists and engineers might serve as data engineers, IT systems analysts, and data architects, assisting in the organization and usage of data.

Other Forms of Data Analytics Jobs Include:

  • Transportation logistics experts
  • Analysts of operations
  • Analysts of quantitative data
  • Consultants in data analytics
  • Managers of large-scale projects

IIM SKILLS vs. IIT Madras Data Analytics Course – Overview

IIM SKILLS

IIM Skills conducts online courses in Data Analytics for students looking for reputed educational institutes with proper experts’ attention. IIM Skills provides exceptional support to its students throughout the course journey and even after completion of the course with lifetime access to premium study materials.

With the help of qualified experts enrolled can achieve milestones in the thriving job market of data analytics. IIM Skills strongly believes in quality in education delivery. This is the reason the curriculum of IIM skills is uniquely designed by well-experienced industry experts and includes complete modules of the real world. Students will get assignments and projects based on real case studies, and this sort of learning process will aid them in observing their progress and improving their flaws diligently.

The Data Analyst Course From IIM Skills Will Provide Mastery of These Skills:

    • Advanced Excel knowledge 
    • In-depth knowledge of Power BI 
    • Learn to understand the development of Python program
    • Comprehensive knowledge of the SQL program
    • Learn to interact with extensive data variables
  • Be familiar with all advanced concepts of data manipulation and complex data functions.

Course Module

Advanced Excel Knowledge

  • Data Supervision
  • Purpose and result for manipulating Data
  • Data handling and communication
  • Data conception in advanced Excel use
  • Installation of dashboards, charts, graphs, and variables to analyze data

Basic Visual Application

  • Introduction and comprehensive use of VBA (Visual Basic for Applications) in data analytics
  • Foundation and Fundamental Principles of Programming Language 
  • Setting Up and & Procedures in Visual Basic for Applications) 
  •  Design & Memory Management 
  • Code comprehensibility

Description and incorporation for SQL Server

  • Concept Introduction of the SSRS
  • Visualization Framework
  • SSRS Visual guidelines
  • Apply SSIS to Understand Project Building

Data Manoeuvring by Using Python

  • Data Manoeuvring by Using Python 
  • Summing up standard Statistics
  • Advanced use of Mathematics in data analysis
  • Python Use in Data Audit
  • NumPy (Numerical Python) Function
  • Precise knowledge of Pandas
  • Use of Python Data Wrangling, research, Visualization, and exploration of Statistical Techniques
  • Inspection of Modeling and the Data solution
  • Data Analysis arrangement
  • Reports expression (Power BI Visuals)

Tableau

  • Data Brain Storming and Account Writing

Tools Included in Advanced Data Analytics Syllabus

  • Excel: IIM Skills includes comprehensive advanced Excel knowledge for its students so they can obtain and enhance their useful understanding of Excel. Excel is a great help in getting business insights along with other data analytics tools like Power BI, and Tableau. Students will be familiarized with the complex use of Excel for data analysis. They can interpret data queries, language, and complex formulae easily in a friendly class environment. Advanced Excel helps to store extensive research, current market tracking patterns, trends, outline data, and information on campaigns.
  • Tableau: Tableau is yet another beneficial tool for data brainstorming and manipulation. Professionals may design to prepare data and share data comprehensive data insights.
  • Power BI: Power BI is an additional tool known to be useful for cloud-based analysis and explanation. This tool is a product of Microsoft and produces data perception for aiding business purposes. Power BI synthesizes variations of data from various point sources to make a genuine version an information on behalf of the company. 
  • Python: Python is a programming language and it is useful to interpret data and automate tasks in a user-friendly manner. Data Analysts use it for collecting, manipulating, and visualizing data. It is very useful in the banking sector to detect deceptive transactions.

Course Fee: ₹ 49,900 + 18% GST.

Discount: Group discount of 10% on 3 or more registrations.

Internship:2 Months Internship

Placement: Support in placements and Interviews

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IIT Madras (IITM Pravartak)

IITM Pravartak is an approved institution from the Government of India’s Department of Science and Technology. The institution aims to be part of its National Mission on Interdisciplinary Cyber-Physical Systems for comprehensive technology Mission. The further objective of the institute is to achieve total integration with all participants by founding strong links between academia, industry, government, and international organizations. Through intensive and application-oriented research, the IITM Pravartak Technology Innovation Hub focuses on new knowledge in the SNACS field. The IITM-PTF joyfully accepts the responsibility of training young India for the next generation of world-class technologies.

The Data Analyst Course From IITM Pravartak Will Provide Mastery of These Skills:

  • Microsoft Excel 
  • SQL learning for data analysis
  • Introduction to data science
  • Introduction of statistics
  • Data analysis on real projects 
  • Technical evaluation, and project assessment
  • Case studies based on Business
  • Power BI
  • Machine learning techniques

Course Module

Data Transformation (using MS Excel and Python)

Learn data transformation, and data analysis functions to discover the fundamentals of data analytics by using Python and Excel. 

Data operations with SQL

  • Introduction of SQL
  • Database regulations
  • Individual correlation model
  • SQL mechanismTables, charts, graphs, and variables
  • SQL performance, functional views, and stored method
  • SQL function and optimization
  • Advanced analytical concepts
  • Grouping sets

Development of Data Science and Statistics

  • Mean approaches
  • Median methods
  • Mode variations
  • Tables and charts

Business Management, Insights, and problem-solving

  • Understanding the financial problem to resolve business issues
  • Exploratory data analysis
  • Discussion on real-life marketing cases
  • Project on finance, marketing, and business insights

Optimization Techniques

  • Goal-oriented programming
  • Integer programming
  • Mixed integer programming
  • Distribution and networking

Statistics, Data Mining and Machine Learning using R Programming

  • Use of R
  • Advanced statistics
  • Technical analysis
  • Clustering
  • Decision tree

Advance Machine Learning

  • Advanced machine learning-XGBoost
  • Problem-solving
  • Parameter optimization

Visualization with the use of Power BI

    • Power BI Introduction
    • Advanced learning of Power BI
    • Detailed data extraction and insights
    • Data transformation
    • Data analysis and visualization
  • Power BI setting, and connectivity

Program Name: B.Sc. in Programming and Data Science

Duration: 3 years (Maximum limit to complete in 6 years)

Conclusion of This Well-detailed Read – IIM SKILLS vs. IIT Madras Data Analytics Course

 IIM SKILLS vs. IIT Madras (IITM Pravartak) Data Analytics Course is an article about data analytics courses. We tried to give you a synopsis of the most demanding courses in the current time of technical development. Data Analytics has a promising present and future in the thriving job market. 

Data analysis is critical for making educated business decisions, and the demand for data analysts is expected to rise dramatically in the future years. Jobs for young and experienced data professionals are expected to grow. The best understanding of the course details and a clear roadmap for imminent opportunities and the curriculum by experts will surely be a great help to prospective professionals.

Frequently Asked Questions (FAQs) related to IIM SKILLS vs. IIT Madras Data Analytics Course

Question 1) Who is eligible for the data analytics course?

Answer: Data Analytics courses can be opted for anyone such as:

  • Non-IT Professionals 
  • Professionals from every sector such as banking, marketing, and 
  • Data Analytics Professionals
  • Project Managers
  • Software Developers
  • Information Architects
  • Beginners, Graduates from any disciples
  • Individuals who are looking for a career change as well

Question 2) How many programming languages are essential parts of the data analytics syllabus?

Answer: There are several different data analysis programming languages to choose from as a professional in this field. There are some of the top five data analyst languages to use in data analysis such as:

  • Data analysis is critical for making educated choices in business decisions, and the demand for data analysts is expected to rise dramatically in the future years.
  • Python, R, SQL, Java, and Scala are the top five data analysis programming languages.
  • Python is a versatile, object-oriented programming language that is frequently used for data analysis, but R excels at statistical analysis and data visualization.
  • There is no less or wrong programming language to learn for data analysis because every language has significance in the computer business.
  • Begin by studying the principles and d data analysis tools such as Excel, Python, R, and querying languages.
  • Khan Academy, Kaggle, KDnuggets, GitHub, and DataCamp are some of the best tools for studying data analysis.

Computer systems receive instructions from language codes. A high-level programming language is very informative and usually easier to read as well as write than a low-level programming language. The source code of high-level languages is written in an easy-to-read syntax.

C, C++, Java, and JavaScript are popular high-level languages. Processors are capable of running low-level languages without the use of an interpreter. These are machine languages that computers can understand.

Question 3) What is the difference between Python and R languages in data analytics?

Answer: Python and R both have an essential need in data analysis to accomplish the process. For example, R is a beneficial choice for visualizing and statistical data analysis. On the other hand, Python is a more multi-purpose, adaptable language and can be used for more recreational and genuine tasks in data analytics.

Description of Python and R

Python

Python is expected to be one of the most popular programming languages in the future. It can be used to extract information, write code, and build websites.

To use Python for this purpose, you may need to download libraries to reduce the amount of coding required. The programming language has a wide range of applications and is user-friendly for beginners.

R

R is a data analysis programming language. This simple language requires fewer libraries than Python and lets you detect trends and patterns in your data. It may be used to create spectacular data visualizations as well as statistical models.

R is used by data analysts because it provides statistical packages for quantitative applications. Neural networks, phylogenetics, sophisticated charting, and nonlinear regression are among them. R is an open-source language that is also designed to be adaptable.

Differences

  • Differences in R and Python:  The two core programming languages.
  • Learning challenges in data analysis: Python is an excellent choice for beginning programmers, although R can be significantly more difficult to master.
  • Upkeep issues: One of the most unavoidable differences between R and Python is that Python code requires less maintenance than R code.
  • Library power: R has fewer and less sophisticated libraries than Python, making this part of the programming language simpler to understand.
  • Applications:  As previously stated, the two languages offer distinct advantages. R is better for complex statistical calculations, whereas Python is better for deep learning.

 

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