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IIM SKILLS vs. Ivy Professional School Data Analytics Course 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!

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