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IIM SKILLS vs. SRM University Data Analytics Course 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.

IIM SKILLS vs. SRM University Data Analytics Course

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


  • 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.

Recommend Read,

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


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 – 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


    • 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.

Also Read,

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. 


  • 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.


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


  • 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/-

Recommend Read,

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.

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