IIM SKILLS vs. IIT Madras Data Analytics Course Review

by Priyanka Sharma | Jul 2, 2025 | 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.

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.

IIM SKILLS vs. SRM University Data Analytics Course Review

by Priyanka Sharma | Jul 2, 2025 | Courses, Data Analytics, Institute Review

Are you aspiring to a career in data analytics? Read the article below- IIM SKILLS vs. SRM University Data Analytics Course to know in detail about your desired career option in the technical industry. You will get a detailed understanding of the course in Data Analytics, syllabus, eligibility for enrolment, job profiles, and introduction to two reputed educational institutions.

Data analytics is a series of mechanisms to handle data sets with the aid of various software or specialized systems to derive conclusions from them. Data analytics is now one of the most important aspects of commercial industries. Based on scientific data and research, data analytics enables firms to make better-educated business decisions.

Data analytics can help a firm enhance its operational efficiency and customer service, obtain a competitive advantage, and generate revenue tools. Data Analytics tools process and generate results from historical or real-time data.

Two data analytics approaches are exploratory data analysis (EDA) and confirmatory data analysis (CDA). EDA seeks patterns and relationships in data, whereas CDA investigates the veracity of hypotheses about data collection.

Data analysis can be classified as quantitative data analysis, qualitative data analysis, data mining, predictive analytics, and machine learning.

After completing a data analytics degree, candidates can apply for professions such as data engineer, data scientist, data architect, database administrator, and data analyst, with beginning salaries of Rs 4-5 per lakh annum or more.

The Advantages of Enrolling in A Data Analytics Course 

Data has the potential to provide significant value to enterprises, but the analytics component is required to unlock that power. As the importance of data analytics in the business world grows, so does the need for organizations to understand how to use it.

Simplifying Operations 

Data analytics can assist organizations in improving operational efficiency. Data collection and analysis of the supply chain might show manufacturing delays or bottlenecks and help identify potential future problems. If a demand forecast suggests that a specific vendor will be unable to handle the volume needed for the Christmas season, a company may supplement or replace this source to avoid production delays. Furthermore, many businesses, particularly those in the retail industry, struggle to optimize inventory levels. Data analytics can help determine the optimal supply for all of an enterprise’s services based on factors such as seasonality, vacations, and secular tendencies.  

Better Decision-making Based on Information

Companies work curiously for better reasoning based on information and these are dependent on accurate forecasts.  Data is a combined decision of artificial intelligence, machines, and mining study. Predictive analytics can forecast what reaction happens to changes in the business and recommend how the organization should respond to dynamic changes. Furthermore, organizations can utilize data analytics tools to measure the success of changes and visualize the results, It allows in crucial decision-making and implementing the possible changes across the organization.

Satisfy the Customer Experience

Customer satisfaction is part of the business. Businesses can forecast their marketing pattern after data collection through a variety of digital methods. It includes physical retail, e-commerce, and social networking. By using data analytics to construct entire customer profiles from this information, businesses may get insights into consumer behavior and provide a more personalized experience. Consider a retail clothes store that has a physical locatDesign well as an online presence. The corporation might analyze its sales data with data from its social media pages, and then create targeted social media campaigns to increase e-commerce purchases for product categories in which customers are already interested. Organizations can improve the customer experience even further by running behavioral analytics models on customer data.  

 Stronger and Effective Marketing

What distinguishes firms is the distinct method they take to market their products. Companies can use data analytics to ascertain customers need. Data allows organizations to do in-depth research in market trends for targeted marketing. 

Reduce Risk and Deal With Setbacks

There are risks everywhere in business. Among them are customer or employee theft, uncollected receivables, worker safety, and legal liability. Data analytics can assist a company in evaluating risks and taking preventative steps. A retail chain, for example, may use a propensity model, which is a statistical tool for forecasting future behaviors or occurrences, to determine which stores are most prone to theft. The firm may then use this information to assess the level of protection required at the stores, as well as if it should divest from any locations.  

Increase Security

Data security concerns affect all businesses. The IT department, for example, can use data analytics programs to parse, process, and visualize audit logs to determine the path and origins of an incident.  Furthermore, IT departments can use statistical models to prevent future attacks. Attacks usually involve anomalous access behavior, particularly in load-based attacks such as a distributed denial-of-service (DDoS) attack. Organizations can set these models to run indefinitely, with monitoring and notification systems built on top to detect and report anomalies so that security professionals can respond quickly.

Qualifications for Data Analytics

Qualifications are an important component of laying a firm basis for any career. The more qualified a candidate is, the more likely they are to be employed by top organizations and receive a competitive salary. To become a Data Analyst, candidates can pursue undergraduate or postgraduate degrees in fields such as Economics, Statistics, Data Science, and Computer Science.

However, students from any educational background can enroll themselves in data analytics diploma or certification courses. A Bachelor’s degree with at least 50% marks in aggregate or equivalent, preferably in Science or Computer Science, is required for admission to any Data Analytics course.

Data Analytics Skill Set Requirements

A good data analytics practitioner must have strong analytical and numerical skills. Aside from that, a complete understanding of computer software(s) such as Querying Language (SQL, Hive, Pig), programming Language ( Matlab, Python, etc.), Statistical Language (R, SAS, SPSS), and Excel is required. Data analytics experts must also have strong interpretive approaches and problem-solving abilities to explain the data analysis reasoning, process and its results.

Job Description and Profiles for Data Analyst

A Data Analyst is a professional who gathers and analyzes data across the organization to make educated judgments or to assist other team members and leadership in making sound decisions. Responsibilities include:

• Data analysts are responsible for interpreting data and analyzing outcomes using statistical techniques.
• Creating and putting into action data analysis, data collection systems, and other initiatives to improve statistical efficiency and quality.
• Obtaining information from primary or secondary sources and keeping databases

Category or Specific Industry in Data Analytics Jobs-

Analysts can obtain jobs in a variety of fields, including:

• Medical and health care
• Market research
• Business

• Market reasoning
• Operations research

Responsibilities

• Create records management policies and procedures
• Discover areas for process improvement and automation
• Development and oversight of automated data processes
• Locate, evaluate, and apply external data validation and cleansing services and technologies
• Create and monitor key performance indicators
• Create and support reporting procedures
• Inspect and audit data quality
• Communicate with internal and external clients to completely comprehend data content
• Using suitable tools and procedures, collect, comprehend, and document precise business requirements.
• Survey design and execution, as well as survey data analysis
• Manipulate, analyze, and comprehend complex data sets related to the employer’s company use of business analytics reporting technologies to generate reports for internal and external
• Design data dashboards, graphs, and visualizations
• Mine and analyze massive datasets, derive valid inferences, and successfully communicate them to management using a reporting tool.

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IIM SKILLS vs. SRM University Data Analytics Course 

IIM SKILLS – details

IIM SKILLS is a well-known and one of the highest result-oriented online educational platforms. It offers 6 monthly data analytics course in online study mode under the supervision of well-qualified teachers (working or retired industry experts) with lifetime study access to study content.

Other Professional Courses from IIM SKILLS

• Financial Modeling Course
• Digital Marketing Course
• SEO Course
• Technical Writing Course
• GST Course
• Content Writing Course
• Business Accounting And Taxation Course
• CAT Coaching
• Investment Banking Course

Modules

Excel in detail: Basics and Advanced

•  Introduction to Excel setup, Setting up, and Conditional Formatting
•  Data Sorting, Understand, and Validation
•  Understalinesg Name Ranges
•  Chart layout
•  Creating and personalizing advanced charts – thermometer charts, waterfall charts, population pyramids
• Data analysis with pivot tables – usage of row and column shelves, values, and filters
•  Differences between data layering and cross-tabulation, summary reports, benefits and drawbacks
•  Change the aggregation kinds and summarization.
•  Making groups and bins in pivot data
•  Calculated field concepts, applications, and restrictions
•  Changing report layouts – outline, condensed, and tabular forms
•  Creating summary reports 

The Operating Pattern and Components of a Visual Basic Application

• Visual Basic Application Components and Workflow
•  What is Logic in VBA?
•  Exposure to Macro Recordings, IDE
• Fundamental of VBA Language Elements
•  Phrases and syntax, Programming assertions, Variables and Data types
•  Working with Range Objects
•  Programming & Control Structures, 
• The looping process, Structures, 
• Spreadsheet & The workbook Functions, Automatic Procedures, and Event
•  Key Components and Memory Arrangement and Management in VBA
• Communicating with Your Users- Basic Dialog Boxes and User Forms
•  Using User Controls and extensions
•  Accessing Macros via the User Interface
•  Using VBA, retrieve information from an Access database in Excel.

SQL

• SQL – A Quick Introduction
•  Installation of MS SQL Server 
•  Introduction to Server Management (SQL)
•  Comprehend basic database concepts
•  Getting knowledge of Insertion, Updation, and deleted statements
•  Introduction of statement option– Subsetting, Sorting, Removing Duplicates, grouping and aggregations, etc
•  SQL Functions (Number, Text, Date, etc)
•  SQL Keywords Introduction
•  SQL Operators – Relational
•  Basics comprehension of SSIS
•  Through learning of Packages
•  Parameters and Visualisation
•  SSRS Visualisation 

Power BI

• Power BI Introduction and Power BI Installation
• When working on a project, introduce several options in the Power BI Desktop Task pipeline.
• Connect to and retrieve data from many sources (CSV, Excel, and so on).
• Data cleansing with Power Query
• Text, Date, and Numeric Power Query Functions
• Conditional Columns in Power Query
• Query Editor can be used to clean and manipulate data.
• Define the granularity of your data.
• Data Combination – Merging and Appending
• Fill Down, Grouping, Transpose, Unpivot, Data Types, Replace Errors and Values in Power BI
• DAX Calculated Tables, Columns, and Measures Overview
• DAX Time Intelligence
• DAX functions that are frequently used in real-time (Calendar functions, Filter functions, Information functions, Text functions, Logical functions, Math functions, Parent and child functions, and so on).
• Power BI Reports Evolution
• Working with a Variety of Visuals/Charts
• Formatting Options in Reports
• Filter visualizations with a slicer.
• Working with Filters 
• Download and use Custom Visuals from the Gallery.
• Include an R or Python visualization.
• Work with key performance indicators

Python

• • Statistics Basics
  •  Central tendencies measurements
  •  Variance measures
  •  Frequency measurements
  • Matrices Operations
  •  Calculus Fundamentals
  •  Integration and Derivatives
  •  Maxima and minima
  •  The area behind the curvature
  •  Optimization theory
  •  Python pre-installed Text, numeric, date, and utility functions
  •  Lambda functions are user-defined functions.
  •  The concept of applying functions

•  Python Objects

NumPy (Numerical Python) Processing

• NumPy functions and methods overview
•  NumPy data structures
•  Array creation and initialization
•  Arrays reading from files.
•  Functions for special initialization
•  Indexing and slicing
•  Array reshaping
•  NumPy Mathematics

Data operations in Pandas (Series and Data structures)

• What is Pandas, and what are its purposes and methods?
•  Data Structures in Pandas (Series and Data Frames)
•  Designing Data Structures

Tableau Product, Measurement, and Dimensions

• Architecture by Tableau
• Connecting to Data and an Overview of Data Source Concepts
• Understanding the Dimensions and Measures of the Tableau Workspace
• Default Data Types and Properties
• Shelves and Signs Tour Card with Show Me
• Save and distribute your work overview
• Describe the scope and direction.
• Running / Cumulative calculations, % of Total
• LOD (Level of Detail) Expressions: An Overview
• Table calculation user applications
• Date Parts and Aggregations
• Tabular and cross-tab charts
• Subtotals and totals
• Stacked Bars and Bar Charts
• Forecasting, Scatter Plots, Edit Axis, Reference Lines, Filters, Context Filter
• Individual axes, blended axes, dual axes, and combination charts are all available.
• Working with aggregate data as opposed to disaggregate data

R for Data Science is introduced, and data can be exported in a variety of formats.

• Introduction R Package Concept – Useful Packages (Base and Other Packages)
•  Data Types and Structures (Vectors, Matrices, Factors, Data Frames, and Lists)
•  R Built-in Features
•  Text, number, date, and utility
•  R Functions Defined by the User
•  Aggregation/Summarization
•  Data Import from Various Sources
•  Data Exporting to Various Formats
•  Viewing Data (Viewing Partial and Complete Data)
•  Date Values – Variable & Value Labels
• Adding New Variables (Calculations and Binning)
•  Creating a dummy variable Using transforms
•  Handling duplicates and missing items
•  Filtering and Sorting
•  Submenu (Rows/Columns)
•  Appending (adding a row or column)
•  Merging/Joining (Left, Right, Inner, Full, Outside)
•  Type conversions of data
•  Formatting and renaming

The Data Analysis course at IIM Skills establishes abilities with the help of a detailed curriculum in its students to build industry-relevant professionals which allows them to find lucrative job opportunities in the market.

Course Fee:  INR 49900 (inclusive Taxes)

Placement: 100% Job & Interview Support.

Study material access: Lifetime access to study materials (all live and recorded Sessions)

Tools Access:  Lifetime access to all the tools

Faculty support: Faculty support in doubt clearing and other guidance

Refund policy: Money back guarantee if not satisfied with teaching pattern.

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SRM University Chennai

SRM Institute of Science and Technology is one of the top-rated universities in India, with over 52,000 full-time students and more than 3200 staff across all campuses. The University positions itself as a leading academic institution with global influence and is committed to being a leading player in the academic world through excellence in teaching and research while valuing academic freedom and adhering to the highest academic integrity standards.

SRM offers data analytics course in the certification category. 

Curriculum

• Introduction to Big Data
• Data Analysis using R
• Machine Learning
• Python for Data Science
• Hadoop and Map Reduce

Program Name – Big Data Analytics

Course Duration: 6 Months

Fee:  1,00,000/-

Apart from certification, SRM offers a BCA in data science. It is a three-year undergraduate degree in data science, machine learning, and artificial intelligence. The program gives students a solid foundation in computer science, statistics, and mathematics while also teaching them how to use these principles to address real-world problems using data-driven techniques. Graduates of the program can go on to study data science, machine learning, artificial intelligence, or other related subjects, or they can work in industries such as finance, healthcare, marketing, and technology. Other job options include research and development, project management, and teaching.

Eligibility

• A pass in 10 + 2 (Higher Secondary) with one of the core topics being Mathematics/Business Mathematics/Statistics/Allied Mathematics.

(OR)

• A diploma in engineering (10+3 / 10+2+2 Pattern) from a government/competent authority recognized. (Aggregate Minimum of 50%) MCA and MSc ADS degrees.

Annual Fees: 95,000/-

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Conclusion of This Read- IIM SKILLS vs. SRM University Data Analytics Course

Analytics specialists in the field require knowledge of much more than just how to deal with data and solve problems.  Communication abilities are essential for any career because analytics experts play key responsible to an organization’s decision-making. Such processes can frequently develop strong leadership qualities as well. 

Finally, there is little doubt that analytics will play a significant role in enterprise in the future learning analytics in all aspects of life.

FAQs on IIM SKILLS vs. SRM University Data Analytics Course

Question 1) What kind of job posts are available in the data analytics field?

Answer: Data analytics various job posts, some of them are the following:

• Operations analyst
• System analyst
• Senior business analyst
• Associate analyst
• Business intelligence analyst
• Analytics data architect
• Marketing analyst and strategist
• Technology Information analyst

Question 2) What is a dashboard in data analytics?

Answer: A data analytics dashboard is a tool for multitasking, organizing, visualizing, analyzing, and tracking data. A data analytics dashboard’s ultimate goal is to make it easier for data analysts, decision-makers, and ordinary users to understand their data, get deeper insights, and make better data-driven decisions. 

Data analytics dashboards are classified into three types: operational, strategic, and analytical:

• Operations: Operational analytics dashboards are associated with the progress of work and the present status of operations. Operational dashboards are frequently used to understand where issues exist and resolve the issue to go further into data.
• Strategy: Strategic analytics dashboards display key performance information and data visualizations. The demonstrating straightforward trends monitors and comprehend decisions based on key performance pieces of information.
• Data dashboards are intended to integrate and extract critical information from a wide range of data sources, services, and APIs. Visuals like charts, maps, figures, scatter diagrams, bar charts, and tables are used to compile all information in a cohesive manner. A company deals with different programmable dashboards for each section and each project. It allows a more granular monitoring system of extremely specific KPIs.
• Analytical: Analytical dashboards place a premium on flexibility, allowing users to investigate data points in as many different ways as possible, using filters and comparisons to uncover hidden insights. Drill-downs, prediction models, and what-if parameters are all common aspects.

Question 3) What are some serious classifications of data analytics?

Answer: Data analytics is divided into four fundamental sorts or groups. While their focus and goals may differ, they are all steps toward analytical maturity, which is a measure of an organization’s capacity to extract value from relevant data. The four types are as follows:

Descriptive analytics

Descriptive analytics is concerned with describing historical data, whereas diagnostic analytics is concerned with determining the reason for a specific outcome or event in the past. It uses data to find trends that can help a business understand its product development process.

Predictive analytics

Predictive analytics employs data to forecast future outcomes or events to assist businesses in meeting their objectives. To accomplish this, data analysts frequently construct optimal simulation models to assist them in making accurate forecasts.

Diagnostic analytics

Diagnostic analytics is concerned with determining the reason for a specific outcome or event in the past. It employs data to detect trends that can help a firm learn about its most recent buyers and select and optimize its sales strategy.

Prescriptive analytics

The most advanced sort of data analytics is prescriptive analytics. It is comparable to predictive analytics in that it provides enterprises with valuable insight. It differs in that it focuses on advising the optimal course of action or solution based on predictive analytics. Prescriptive analytics frequently necessitates the use of machine learning algorithms and other sophisticated techniques. This means that in many circumstances, only firms with significant resources and an advanced analytics methodology can perform it.

IIM SKILLS vs. Loyola College Chennai Data Analytics Course

by Priyanka Sharma | Jul 2, 2025 | 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. 

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.