Data Analytics



Data Analytics

Learn Data Analytics without any upfront course fee!


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Give IKIGAI Aptitude Test (IAT)

Aptitude Test (IAT)
Attend Counselling Call

Counselling Call
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Course Overview

Course Overview

8 Months Live Online Program
1st Jan 2022
1 Month Capstone Project
12 hours per week
✓ 6 Minor Projects
500+ Hours of Learning
200+ Hours Live Interactive Sessions
✓ 15+ Faculty, Practitioners, and Guest Speakers
✓ 1 Major Capstone
✓ 5+ Mock Interviews


Data Analytics Bootcamp

The data analytics course provides fundamental concepts of data analytics through real world case studies and examples and gives insights into how to apply data and analytics principles in your business. You’ll learn about project lifecycles, the difference between data analytics, data science, and machine learning; building an analytics framework, and using analytics tools to draw business insights.

Why Data Analytics?


Analytics Professionals having experience of less than 5 years.


Jobs are open for candidates with 0-5 years’ experience


Indian Data Science industry is growing at this rate.


India to become one of the top three markets for Big Data by 2020

* Source: PWC, NASSCOM, Analytics India Magazine


Placement Assistance

Dedicated Placement Assistance to get you placed in the top companies
Career Guidance and mentorship by Ikigai Industry Partners
Resume Building and Interview Preparation Sessions
Multiple interviews with multiple companies to increase your chances of getting placed

Capstone Project and Assignments

6 Minor Projects

These projects will be at the end of every module, the concepts you are learning you will be implementing in real industrial case studies.

1 Major Capstone Project

This is a 4 week project at the end of the program to make you apply all the learnings to real life business problems.


There will be weekly assignments you need to complete in every module. This will make sure that you are on track throughout the program.

Roles you can apply for after this program:

  • Analytics Consultant
  • Data Analyst
  • Data Scientist
  • Data Engineer
  • ML Engineer
  • AI Engineer
  • Research Analyst
  • Statistician

    Data Analytics Overview
  • Introduction
  • Data Analytics: Importance
  • Digital Analytics: Impact on Accounting
  • Data Analytics Overview
  • Types of Data Analytics
  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Data Analytics: Amazon Example
  • Data Analytics Benefits: Decision-making
  • Data Analytics Benefits: Cost Reduction
  • Data Analytics Benefits: Amazon Example
  • Data Analytics: Other Benefits
  • Key Takeaways
  • Dealing with Different Types of Data
  • Introduction
  • Terminologies in Data Analytics – Part One
  • Terminologies in Data Analytics – Part Two
  • Types of Data
  • Qualitative and Quantitative Data
  • Data Levels of Measurement
  • Normal Distribution of Data
  • Statistical Parameters
  • Key Takeaways
  • Business Analytics with Excel
    1.Functions and Formulas

  • Formulas with Multiple Operators
  • Inserting and Editing a Function
  • Auto Calculate and Manual Calculation
  • Defining Names
  • Using and Managing Defined Names
  • Displaying and Tracing Formulas
  • Understanding Formula Errors
  • Using Logical Functions (IF)
  • Using Financial Functions (PMT)
  • Using Database Functions (DSUM)
  • Using Lookup Functions (VLOOKUP)
  • User Defined and Compatibility Functions
  • Financial Functions
  • Date & Time Functions
  • Math & Trig Functions
  • Statistical Functions
  • Lookup & Reference Functions
  • Database Functions
  • Text Functions
  • Logical Functions
  • Information Functions
  • Engineering and Cube Functions
  • 2. Working with Data Ranges

  • Sorting by One Column
  • Sorting by Colors or Icons
  • Sorting by Multiple Columns
  • Sorting by a Custom List
  • Filtering Data
  • Creating a Custom AutoFilter
  • Using an Advanced Filter
  • 3. Working with PivotTables

  • Creating a PivotTable
  • Specifying PivotTable Data
  • Changing a PivotTable’s Calculation
  • Filtering and Sorting a PivotTable
  • Working with PivotTable Layout
  • Grouping PivotTable Items
  • Updating a PivotTable
  • Formatting a PivotTable
  • Creating a PivotChart
  • Using Slicers
  • Sharing Slicers Between PivotTables
  • 4. Analyzing and Organizing Data

  • Creating Scenarios
  • Creating a Scenario Report
  • Working with Data Tables
  • Using Goal Seek
  • Using Solver
  • Using Text to Columns
  • Grouping and Outlining Data
  • Using Subtotals
  • Consolidating Data by Position or Category
  • Consolidating Data Using Formulas
  • 5. Working with the Web and External Data

  • Inserting a Hyperlink
  • Importing Data from an Access Database or Text File
  • Importing Data from the Web and Other Sources
  • Working with Existing Data Connection
  • 6. Customizing Excel

  • Customizing the Ribbon
  • Customizing the Quick Access Toolbar
  • Using and Customizing AutoCorrect
  • Changing Excel’s Default Options
  • Creating a Custom AutoFill List
  • Creating a Custom Number Format
  • 7. Working on Live Data and Dashboards

  • Creating dashboards on company specific data
  • Working on Live data
  • Dashboards with the help of Developer Ribbon.
  • Working with critical & Complex formulas
  • Tableau
  • Understand how Tableau Desktop fits within the Tableau family of products
  • Combine data sources for use by Tableau
  • Connect to a variety of sources including flat files and databases
  • Understand data types and roles
  • Use key operations in Tableau – filtering, sorting, grouping and creating sets
  • Work with extracts (file formats used by Tableau)
  • Build and format data visualizations
  • Work with maps and location-based data
  • Create interactive dashboards by using parameters, calculations and actions
  • Publish dashboards and visualizations
  • Working with bins, groups and parameters
  • Working with folders
  • Creating story
  • SPSS
  • Making data visualizations
  • Creating regression variable plots
  • Importing data and recoding variables
  • Computing frequencies and correlations
  • Reliability analysis
  • k-means clustering
  • Decision tree classification
  • Analyzing data
  • Building predictive models
  • Exporting your work
  • Programming Basics and Data Analytics with Python
    1. Introduction

  • Variables
  • Data Types with Python
  • Assisted Practice: Data Types in Python
  • Keywords and Identifiers
  • Expressions
  • Basic Operators
  • Operators in Python
  • Functions
  • Search for a Specific Element from a Sorted List
  • Create a Banking System Using Functions
  • String Operations
  • String Operations in Python
  • Tuples
  • Tuples in Python
  • Lists
  • Lists in Python
  • Sets
  • Sets in Python
  • Dictionaries
  • Dictionary in Python
  • Dictionary and its Operations
  • Conditions and Branching
  • Check the Scores of a Course
  • While Loop
  • Find Even Digit Numbers
  • Fibonacci Series Using While Loop
  • For Loop
  • Calculate the Number of Letters and Digits
  • Create a Pyramid of Stars
  • Break and Continue Statements
  • 2. File handling, Exception handling, and Package handling

  • Learning Objectives
  • File Handling
  • File Opening and Closing
  • Reading and Writing Files
  • Directories in File Handling
  • Assisted Practice: File Handling
  • Errors and Exceptions
  • Assisted Practice: Exception Handling
  • Modules and Packages
  • Assisted Practice: Package Handling
  • 3. Mathematical Computing using NumPy

  • Learning objectives
  • NumPy
  • Create and Print Numpy Arrays
  • Operations
  • Executing Basic Operations in Numpy Array
  • Performing Operations Using Numpy Array
  • Demonstrate the Use of Copy and Use
  • Manipulate the Shape of an Array
  • 4. Data Manipulation with Pandas

  • Learning Objectives
  • Introduction to Pandas
  • Data Structures
  • Create Pandas Series
  • Dataframe
  • Create Pandas DataFrames
  • Create Pandas DataFrames
  • Missing Values
  • Handle Missing Values
  • Data Operation
  • Data Operations in Pandas DataFrame
  • Data Operations in Pandas DataFrame
  • Data Standardization
  • Pandas SQL Operations
  • Pandas SQL Operations
  • 5. Data visualization with Python

  • Learning objectives
  • Data Visualization
  • Considerations of Data Visualization
  • Factors of Data Visualization
  • Python Libraries
  • Create Your First Plot Using Matplotlib
  • Line Properties
  • Create a Line Plot for Football Analytics
  • Multiple Plots and Subplots
  • Create a Plot with Annotation
  • Create Multiple Plots to Analyze the Skills of the Players
  • Create Multiple Subplots Using plt.subplots
  • Types of plots
  • Create a Stacked Histogram
  • Create a Scatter Plot of Pretest scores and Posttest Scores
  • Create a Heat Map to Analyze the Sepal Width, Petal Length, and Petal Width of an Iris Dataset
  • Create a Pie Chart
  • Create an Error Bar
  • Area Chart to Display the Skills of the Players
  • Create a Word Cloud of a Random Data
  • Create a Bar Chart
  • Create Box Plots
  • Create a Waffle Chart
  • Seaborn and Regression Plots
  • Introduction to Folium
  • Maps with Markers
  • Kernel Density Estimate Plots
  • Analyzing Variables Individually
  • Key Takeaways
  • Visualize the Sales Data
  • Data Science, Data Analytics and Machine Learning
  • Introduction
  • The Data Science Domain
  • Data Science, Data Analytics, and Machine Learning – Overlaps
  • Data Science Demystified
  • Data Science and Business Strategy
  • Successful Companies Using Data Science
  • Travel Industry
  • Retail
  • E-commerce and Crime Agencies
  • Analytical Platforms Across Industries
  • Key Takeaways.
  • Data Science Methodology
  • Introduction
  • Data Science Methodology
  • From Business Understanding to Analytic Approach
  • From Requirements to Collection
  • From Understanding to Preparation
  • From Modeling to Evaluation
  • From Deployment
  • Key Takeaways.
  • Data Analytics in Different Sectors
  • Introduction
  • Analytics for Products or Services
  • How Google Uses Analytics
  • How Linkedin Uses Analytics
  • How Amazon Uses Analytics
  • Netflix: Using Analytics to Drive Engagement
  • Netflix: Using Analytics to Drive Success
  • Media and Entertainment Industry
  • Education Industry
  • Healthcare Industry
  • Government
  • Weather Forecasting
  • Key Takeaways
  • Analytics Framework and Latest trends
  • Introduction
  • Case Study: EY
  • Customer Analytics Framework
  • Data Understanding
  • Data Preparation
  • Modelling
  • Model Monitoring
  • Latest Trends in Data Analytics
  • Graph Analytics
  • Automated Machine Learning
  • Open Source AI
  • Key Takeaways
  • Introduction to SAS (Statistics Analysis System)
  • Program Structure, Various screens, PDV process
  • Automatic Variables
  • Input of the Data, Handling dates in SAS
  • Types of Text file inputs in SAS
  • Proc Import and Export procedures
  • Various data handling options
  • Subsetting of Data
  • Controlling input and outputs
  • Functions in SAS – Arithmetic, Character/String Functions and Date Functions
  • Retain Usages, Conditional Statements and Looping in SAS
  • Procedures in SAS
  • Merging the data, Working with Multiple data files
  • Handling the ODS – Output Delivery System
  • Debugging in SAS
  • Introduction to PROC SQL
  • Advance SAS
  • MACRO Programming
  • Few Advance Statistical Procedures
  • Apply Now