Post Graduate Program in Data Analytics and Data Science

A 6 month program which guides students on their journey into Data Science and Data Analytics. Laptops can be availed at an additional cost of 18k

  • Domain : DATA SCIENCE
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Program Overview

Nowadays, the world of IT is driven by data and the workforce behind it is made up of data scientists and analysts. To get acquainted well with the data and to drive their businesses to success, companies need data science and data analytics. Companies make data-driven decisions to reach their potential consumers/audience to improve their business.

Data Science emphasizes on finding actionable intel from the raw data whereas Data Analytics is the process of performing statistical analytics on the existing data sets. It puts more emphasis on the questions that are being asked at the moment. In a nutshell, Data Science produces broader insights that concentrates on the future outcomes and Data Analytics focuses more on the present applications.

On the other hand, in Machine Learning, we use data to test and train our model. It is all about creating models that can learn from the given data and provide results or predictions based on the recent trends.

Example: Recognizing fingerprints, Predicting stock prices, and self-driving cars.

Data Science and Machine Learning are blooming fields and their applications are numerous. The opportunities in this field have grown exponentially in recent years. The “PG Program in Data Science and Data Analytics” is designed for those who aspire to pursue their career and thrive in this domain. Through this course, you can get your basics strong and apply them in real-world applications. This course consists of projects where you can apply your learned theory practically. 

This program gives the student access to 9 different courses. The courses mentioned below can be taken up in this program:

  1. Math behind Machine Learning & Artificial Intelligence using Python
  2. Data Analysis and Visualization with Excel
  3. SQL for Data Science
  4. Core and Advanced Python Programming
  5. Effective Dashboards using Power BI 
  6. Business Intelligence and Visualisation with Tableau
  7. Data Structures and Algorithms
  8. Machine Learning & Artificial Intelligence for Mechanical, Civil & EE Engineers
  9. Advanced Deep Learning


Speak to our technical specialists to understand what is included in this program and how you can benefit from it.

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List of courses in this program

1Math behind Machine Learning & Artificial Intelligence using Python

This module is specially designed for  Engineering students and graduates. It helps you leverage the basic maths to understand the concepts of Machine Learning and Artificial Intelligence.

In this module, you will learn about

  • Basic Concepts of Set theory, Trigonometric functions, Straight lines, A.M, G.M & H.M, and the Concepts of Vectors
  • Permutations and Combinations
  • Statistics and Probability
  • Likelihood (for Logical Regression)
  • Gradient Descent (for Linear & Logistic Regression)
  • Linear Algebra (for PCA)
  • Derivatives (for Neural Network)
  • Backpropagation (for Deep Learning) 
  • Python

You will be working on projects related to Logistic Regression and Gradient Descent.

2SQL for DataScience

This module will cover the basics of SQL and how to perform data analysis on large datasets. 

In this module, you will learn about

  • Creating, Inserting, Updating and Querying of Relational Databases.
  • Method to understand Data Patterns.
  • Data cleaning and Computation.
  • Using SQL for Data Analysis & Data Visualization

In this module, you will extensively work on softwares like SQL & Relational Database Tools, which is widely used in the industry for database storage. It also helps students who are willing to pursue master’s or Phd in Data Analytics, Data Science and related fields.

3Core and Advanced Python Programming

Recently, Python has become one of the most popular programming languages because of its simplicity and ease of use. This module is designed in such a way that even students who don't have a programming background can excel in it.

In this module, you will learn about

  • Basics Of Python Programming
  • Strings, Decision Control Statements
  • Repetition Statements, Console Input-output
  • Lists, Tuples, Sets, Dictionary
  • Functions And Recursion, Functional Programming and Lambda Functions 
  • File Input-output, Modules
  • Classes and Objects
  • Exception Handling, Iterators and Generators
  • Data Analysis With Pandas
  • Numeric And Scientific Computing Using Numpy
  • Graphical User Interfaces with Tkinter
  • Interacting with Databases

Also, you will be working on exciting challenges at the end of each topic and two full time projects, which will help you solidify your basics and apply them in real world situations.

4Data Analysis and Visualization with Excel

This module covers the core concepts of Data Analysis and Visualization in relevance with MS Excel. The course is aimed at complete beginners or just familiar with the basics of MS Excel, and those who want to build a solid foundation to build their skills. 

In this module, you will learn about

  • Course Introduction and Fundamentals
  • Basic Excel Functions and Modification of Worksheet
  • Data Formatting, Working with Shapes & Images, Creating your first Chart
  • Introduction to Excel Templates, Options, and Tables
  • Conditional Functions and Other Functions
  • Pivot Tables and Lookup Functions
  • New Functions, Data Tab, and Introduction to Power Query
  • Advanced Pivot Table Functions and PowerPivot Tools
  • Working with Large Data sets, File Protection, Named Ranges
  • List Functions and Excel Automation using Macros
  • Basics of VBA
  • Email Automation

During the coursework, you will be working on two projects related to Report Creation in Excel.

5Effective Dashboards using Power BI

This module aims to provide a brief understanding from Basics to Advanced concepts of Power BI which will help in understanding the concepts of Data Modelling in an easy way. 

In this module, you will learn about

  • Introduction to PowerBI
  • PowerBI Query Editor
  • Relational Data Model
  • Basics of DAX
  • Advanced DAX Functions
  • Creating Interactive Reports
  • Introduction to Power BI Services
  • Connecting to Data in Power BI Service

6Business Intelligence and Visualisation with Tableau

This module is designed to provide you with a basic understanding of Tableau and its functionalities. It will help you understand how to logically solve questions and the best practices to follow while working with Tableau.

In this module, you will learn about

  • Introduction to Tableau
  • Connecting to Data
  • Shelves & Card and Analytics Pane
  • Ways of Sorting, Grouping and Filtering
  • Applying Calculated Fields and Parameters
  • Creating a Dashboard and Stories
  • Distributing and Publishing
  • Types of Joins and Blending
  • Introduction to Relationships and Tableau Data Models
  • Data Preparation before Loading
  • Parameter Creation and Set Actions
  • Advanced Calculation and Analytics in Tableau
  • Mapping Function and Dynamic Design in Tableau

Also, you will be working on a “Report and Dashboard Creation” project in Tableau.

7Data structures and Algorithms

Data-Structures and Algorithms are the building blocks of any well-designed piece of software. Students generally are very good at understanding the textbook definitions of Stack, Heap, or Linked-lists. However, when you put them in practice, that is when you start seeing gaps in the student's understanding. By learning this module from SKILL-LYNC, we will
  • Help you clock in 300+ hours of coding for a wide range of problems
  • Teach you how scalable web/desktop applications are built
  • Expose you to what top tech companies expect from developers

8Machine Learning & Artificial Intelligence for Mechanical, Civil & EE Engineers using Python

This module dwells deep into the concepts of Machine Learning Techniques. In this advanced module, you will be learning about

  • Basics of Probability and Statistics
  • Basics of ML & AI
  • Supervised Learning (Prediction, Classification)
  • Random Forest and Model Evaluation
  • Unsupervised Learning (K-Means, Hierarchical, Classification & PCA)

9Advanced Deep Learning

The last module of this course covers the basics and advanced concepts of Deep Learning. Deep Learning is a subset of Machine Learning in AI. Nowadays, Deep Learning is extensively used in Self Driving Cars, Natural Language Processing, Healthcare, Fraud Detection, Visual Recognition and Entertainment. Deep Learning is nothing but teaching computers to think using structures modelled on the human brain.

In this module, you will learn about

  • ANN - Artificial Neural Network (Feed Forward Neural Network)
  • Activation Functions in Neural Networks
  • Evaluation and Improvisation of Models
  • Optimizers
  • CNN & RNN and
  • Basics of NLP 

1. Math behind Machine Learning & Artificial Intelligence using Python Syllabus

1Basic concepts

  • Sets
  • Subsets
  • Power set
  • Venn Diagrams
  • Trigonometric functions
  • Straight lines
  • A.M, G.M and H.M
  • Concepts of Vectors

2Permutation & Combinations

  • Introduction & basics
  • Fundamental principle of counting
  • Permutations
  • Combinations

3Statistics - I

  • First business moment
  • Second business moment
  • Third business moment
  • Fourth business moment


  • Introduction
  • Random experiments
  • Conditional probability
  • Joint probability

5Statistics – II

  • Z Scores
  • Confidence interval
  • Correlation
  • Covariance

6Probability - II

  • Introduction
  • Uniform Distribution
  • Normal Distribution
  • Binomial Distribution
  • Poisson Distribution

7Likelihood (for Logistic regression)

  • Introduction
  • Odds
  • Log odds
  • Maximum likelihood vs probability
  • Logistic regression

8Gradient descent (for Linear & Logistic regression)

  • Loss function
  • Cost function
  • Gradient descent for linear regression
  • Gradient descent for logistic regression

9Linear Algebra (for PCA)

  • Matrices
  • Types of matrices
  • Operation on matrices
  • Eigen values
  • Eigen vectors

10Derivatives (for Neural network)

  • Derivatives
  • Intuitive idea of derivatives
  • Increasing & decreasing function

11Backpropagation (for Deep learning)

  • Chain rule
  • Maxima & minima
  • Back propagation
  • Cost function for deep learning


  • Basics of Python
  • If else
  • For loop
  • Data types

Projects Overview

Project 1


To perform a logistic regression algorithm taught in the course with simple random sampling and stratified sampling and identify the differences in the result (Data will be given in the class)

Project 2


With any data set, perform the Gradient descent using KERAS, create a single hidden layer with the activation sigmoid function and check the accuracy for this model. Validation of the same by creating a Log loss function with proper weights and apply the same on sigmoid function is to be carried out

2. Data Analysis and Visualization with Excel Syllabus

1 Course Introduction and Fundamentals

  • Introduction to the course 
  • Launching Excel and Version Check
  • Introduction to the Interface
  • Saving an Excel Document
  • Common Shortcuts
  • Entering and Editing Data
  • Relative and Absolute Cell references
  • Formatting 
  • Customizing the Quick Access Toolbar
  • Excel Self Help

2Basic Excel Functions and Modification of Worksheet

  • Basic Functions
  • Design of Excel Functions
  • Understanding SUM(), MIN() & MAX()
  • Understanding AVERAGE()
  • Understanding COUNT()
  • AutoSum and Autofill/Flashfill
  • Modification
  • Inserting New Sheets, Rows and Columns
  • Deleting Sheets, Rows and Columns
  • Moving or Copying Sheets
  • Sheet formatting
  • Excel Options
  • Customize Ribbon

3Data Formatting, Working with Shapes & Images, Creating your first Chart

  • Data Formatting
  • Cell formatting – Merge, Alignment etc.
  • Borders
  • Currencies and Percentages
  • Format Painter
  • Conditional Formatting
  • Find & Search
  • Shapes & Images
  • Inserting Shapes & Images
  • Formatting Excel Shapes
  • SmartArt
  • Creating your first Chart
  • Creating Chart
  • Chart Options
  • Types of Charts
  • Chart formatting
  • Date Functions

4Excel Templates, Excel Options and Printing your Excel Worksheets,Introduction to Tables

  • Excel Templates
  • Intro and import Excel Template
  • Custom Template
  • Printing Worksheets
  • Print Preview
  • Margins and Scaling
  • Page Layout
  • Headers and Footers
  • Specific Range
  • Tables
  • Introduction
  • Table Options
  • Table Formatting
  • Table References
  • Working with formulas
  • Benefits of Tables

5Conditional Functions, Other Functions

  • Conditional Functions
  • Understanding IF()
  • Understanding SUMIF() & SUMIFS()
  • Understanding COUNTIF()
  • Use of AND & OR with IF()
  • Understanding IFERROR()
  • Other Functions and Text Functions
  • Understanding COLUMN() & ROW()
  • Understanding TEXT()
  • Understanding LEFT(), RIGHT() & MID()
  • Understanding LEN(), CONCAT()
  • Understanding TRIM(), PROPER(),  FIND()

6Pivot Tables,Lookup Functions

  • Pivot Tables
  • Data source
  • Pivot Table Structuring
  • Pivot Table Options
  • Pivot Table formatting
  • Lookup Functions
  • Understanding VLOOKUP()
  • Understanding HLOOKUP()
  • Understanding INDEX & MATCH
  • Understanding XLOOKUP()
  • Understanding OFFSET()

7 New Functions , Data Tab & Intro to Power Query

  • New Functions
  • Understanding UNIQUE()
  • Understanding FILTER()
  • Understanding IFS()
  • Understanding SORT(), SORTBY()
  • Understanding SWITCH()
  • Data Tab
  • Data Import
  • Data Cleaning and Transform
  • Intro to Power Query
  • Use of Power Query

8Advanced Pivot Table Functions, PowerPivot Tools

  • Advanced Pivot Table Functions
  • Fields, Items & Sets
  • Pivot Charts
  • Slicers and Timeline
  • Group & Ungroup
  • PowerPivot Tools
  • Intro to Excel Power Pivot
  • More tables and Relationships
  • Creating Data Models
  • KPIs

9Large Data Sets, File Protection, Named Ranges, More in Data Tab

  • Working with Large Data sets
  • Freeze Panes
  • Grouping Data
  • Consolidating Data from Multiple Worksheets
  • File Protection
  • Protecting Specific Cells in a Worksheet 
  • Protecting the Structure of a Workbook 
  • Adding a Workbook Password
  • Data Tab
  • Custom Sorting 
  • What-If Analysis 
  • Text to Columns
  • Data Validation
  • Understanding Data Validation
  • Custom Data Validation Error
  • Dynamic Formulas in Validation

10List Functions, Excel Automation using Macros

  • List Functions
  • Understanding DSUM()
  • Understanding DSUM() with AND/OR()
  • Understanding DAVERAGE()
  • Understanding DCOUNT()
  • Understanding SUBTOTAL()
  • Excel Automation using Macros
  • Understanding Macros
  • Creating Macro with Macro Recorder
  • Editing Macros with VBA
  • Creating Buttons 

11VBA Basics

  • VBA Basics
  • Intro to VBA
  • VBA Editor
  • Adding Code to VBA procedure
  • VBA variables
  • IF Statements
  • Loops
  • Automate boring stuff
  • Input and User Messages
  • Handling Errors

12Email Automation, Finale

  • Email Automation
  • Intro to the Emailing Section
  • Understanding Email Routine
  • Email loop
  • Finale
  • Tips and tricks
  • Industry Standards
  • Course summary
  • Application of your learning

Projects Overview

Project 1


HHH Global is a private company providing cloud services to its clients. The sales team of the company is expecting more work to be coming in the following quarters and guided the capacity team to hire more people to fulfill the support demand. The organization is planning to hire more people in the company in view of the strong pipeline. You as a Business analyst of the company are required to build and present a full-fledged report in Excel using Pivot tables and Charts to showcase the need for more people in the organization. You are also required to fill the data in a predefined format in the Hiring Need Form

Project 2


CPG is a private company engaged in the business of consumer products. The company receives orders for its various products from wholesalers from all over the country. Wholesalers are provided fill-in details in a predefined excel template to buy products from the company. They save this excel file at an online location. You, as a Logistics Head, are required to gather the order details of products from these Excel files and give them to the Supply Management Team in an understandable format. You are also required to prepare a separate sheet for every state, summarizing their Orders by product to present the current status and growth of orders to State Heads.

3. SQL for Data Science Syllabus


  • Introduction to Data Science 
  • Applications of Data Science 
  • Why SQL is required for Data Science? 
  • Database Management System (DBMS) 
  • Relational Database Management System (RDBMS) 
  • Basic terminology in RDBMS 
  • Data Constraints 
  • Entity Relationship Model 
  • What is SQL 
  • Categories of SQL Commands 
  • Hands on executing simple SQL statements on RDBMS tool

2Database Creation, Manipulation

  • Detailed SQL Data types 
  • Creating database
  • Create Table
  • Using Constraints
  • Insert Table
  • Altering Table structure
  • Dropping Database and Table
  • Delete, Update
  • Hands on importing sample database schema

3Database Selection

  • Select statement
  • Removing Duplicates
  • Use of Alias
  • Use of Where
  • Use of Wildcards
  • Limit clause
  • Arithmetic Operators
  • Mathematical Functions  
  • Hands on creating backups and restore for large databases

4Database Selection

  • Generating Strings
  • String Functions
  • Date Functions
  • Conversion Functions 

5Database Selection

  • Comparison Operators
  • Logical Operators
  • Order By
  • Group By
  • Aggregate Functions
  • Using aggregate functions with Group by clause
  • Union Operator
  • Sub-query

6Querying Multiple Tables

  • Need to Join Multiple Tables
  • Cartesian Product
  • Inner Join 
  • Left Join
  • Right Join
  • Self Join
  • Delete Join
  • Update Join
  • Hands on Joining more than two tables in sample database

7Data Exploration

  • What is Data Exploration?
  • Structure of Data
  • Understanding E-R Diagram
  • How to Use SQL for Data Exploration
  • Significance of 
    1. Joins
    2. Sub queries
    3. Inbuilt functions
  • Other important capabilities of SQL for data exploration
  • Hands On 
  • Working with NULL values
  • Making trends in Data
  • Identifying Outliers
  • Creating Data Summary

8Index, View, Transaction

  • Creating Index
  • Use of Index
  • Type of Index and Index Strategies
  • Views
  • Views as Weapon for Data Analysis
  • Multi user database
  • What is Transaction
  • Save points
  • Hands on working on Multi user database environment

9Querying with Conditions

  • Querying with Conditions 
  • Searched Case Expression 
  • Simple Case Expression 
  • Applications of Case Expression 
  • Common Error Codes 
  • Hands on working with Json type data

10Stored Procedures

  • Stored Procedures as friend for Data Analysis
  • Creating Stored Procedures
  • Removing Stored Procedures
  • Altering Stored Procedures
  • Conditional Statements
  • Loops
  • Hands on working with Cursors

11Integrating SQL with Excel

  • Hands on
  • Accessing MySQL data with MS Excel
  • Running SQL statements with Excel
  • Combining Excel and SQL statements for data representation

12Integrating SQL with Python

  • Hands on 
  • Working with Python
  • Accessing SQL data with Python
  • Running basic SQL statements with Python 
  • Running inbuilt python functions on SQL data

Projects Overview

Project 1


  • Business model Customer to Customer (C2C) allows customers to do business with each other
  • Analyzing user’s database will lead to understand the business perspective
  • Behavior of the users can be traced in terms of business with exploration of the user’s database.

Project 2


  • In a given dataset, alumni’s career choice will be analysed based on his/her course completion date
  • For data analysis, add-on for Microsoft excel is to be used for data summarization and visualization
  • Data computation can be performed with integration of python with SQL
  • SQL script should be ready with procedures and cursors using conditional statements and loops.

4. Core and Advanced Python Programming Syllabus

1Introduction to Python, Python Basics

• Discussion about Features and uses of Python, Program execution, Installation of IDE.
• Identifiers and keywords, types of comments, data types, Variables, Arithmetic operators, Assignment operators
• Input and print statements

2Strings, Decision control statements

• Definition of string, operations accessing string elements
• Relational operators, Logical operators, Conditional expressions, If, If..else, If..elif

3Repetition Statements, Console input-output

• Usage of while and for, break and continue, pass and else statements
• Formatted input and output

4Lists, Tuples, Sets, Dictionary

• Accessing list elements, basic list operations, types of lists
• Accessing tuple elements, types of tuples, tuple operation
• Accessing set elements, set operations and methods, Mathematical set operations, updating set operations
• Accessing dictionary elements, dictionary operations and methods, nested dictionary

5Functions and Recursion, Functional Programming and Lambda functions

• Defining a function, types of arguments
• Global and local variables
• Functions as arguments, Implementing Lambda functions
• Map, Reduce, Filter functions

6File Input-Output, Modules

• Read-write operations, with keyword, file opening modes, moving within a file
• Serialization, file and directory operations
• Importing a module, variations of import, third-party packages

7Classes and objects

  • Class variables and methods, Operator overloading
  • Reuse, Containership, Inheritance

8Exception handling, Iterators and generators

  • Iterables and iterators
  • Syntax errors and exceptions, try-except, else, finally blocks

9Data Analysis with Pandas

  • Installing Pandas
  • Loading CSV files, JSON files
  • Dataframes

10Numeric and Scientific Computing using Numpy

  • Introduction to Numpy
  • OpenCV
  • Images and Numpy Arrays

11Graphical User Interfaces with Tkinter

  • Introduction to Tkinter
  • Setting up a GUI with widgets
  • Connecting GUI widgets with Callback functions

12Interacting with Databases

  • Introduction to SQLite
  • Connecting and inserting data to SQLite via Python
  • Selecting, deleting and updating SQLite records

Projects Overview

Project 1


This project uses the skills learnt from week 1 to week 6. The aim of this project is to create an English Dictionary app that returns definitions of English words. When the application is started, it displays a menu as follows:

Main Menu
1. Add a new word
2. Find the meaning
3. Update a word
4. Exit

Enter Choice:

If item 1 is selected, it should prompt for a word and then accept its meaning. This pair of word and its meaning should be stored in a file called ‘words.txt’. When item 2 is selected, it should prompt for a word, search that word in the file ‘words.txt’ and return the meaning if it is found. If the meaning is not found, it should display an appropriate message. Similarly, when choice 3 is entered, it should accept a word and update its meaning. In all the above three cases, the prompt should return to the main menu. The words and their meanings should be stored as a dictionary in words.txt, using the concept of serialization. If choice 4 is selected, a graceful exit should be performed.

Project 2


In this project, a Library Book Management System will be implemented with the front end GUI developed using Tkinter and the backend database implemented in SQLite. It is a single window tkinter GUI application which will implement CRUD( create, read, update,delete) operations on a ‘books’ database, in SQLite. The window will contain text widgets to accept/ display book title, author, year of publication, ISBN (optional) and a large text area/ list box to display the records which match the search criteria. It should contain buttons to perform the following operations on the ‘books’ database:

1. Display all the records
2. Search a book
3. Add a book
4. Issue a book
5. Delete a book
6. Exit the application

When option 1 is selected, all the books present in the library should be displayed. Option 2 should be able to search the book based on any of the criteria like author name, book title, year or ISBN. Similarly, it should be able to add the entry when option 3 is selected and issue a book when option 4 is selected. The book details should be deleted when option 5 is selected. Selecting option 6 should provide a graceful exit from the app.

5. Effective Dashboards using Power BI Syllabus

1 Introduction to PowerBI

  • Introduction to Power BI and Why Power BI
  • Introduction to Power BI Query Editor
  • Types of Power BI Data Connectors
  • Basic Table Transformations

2PowerBI Query Editor

  • Text, Number & Date Tools
  • Index & Conditional Columns 
  • Grouping & Aggregating Data
  • Pivoting & Unpivoting

3PowerBI Query Editor – Part 2

  • Modifying, Merging & Appending Queries
  • Connecting to Folders
  • Defining Hierarchies & Categories
  • Query Editing & Power BI Best Practices

4Relational Data Model

  • Intro to Database Normalization
  • Data ("Fact") Tables vs. Lookup ("Dimension") Tables
  • Creating Power BI Table Relationships
  • "Star" vs "Snowflake" Schemas
  • Active vs Inactive Relationships

5Relational Data Model – Part 2

  • Relationship Cardinality
  • Connecting Multiple Data Tables
  • Filtering & Cross-Filtering
  • Hiding Fields from the Power BI Report View
  • Data Modeling & Power BI best Practices

6 Basics of DAX

  • Intro to Data Analysis Expressions (DAX)
  • Calculated Columns vs Measures
  • DAX Syntax & Operators
  • Common Power BI Functions
  • Basic Date & Time Formulas

7Basics of DAX- Part 2

  • Logical & Conditional Statements
  • Text, Math & Stats Functions Joining Data with RELATED
  • DAX Iterators (SUMX, AVERAGEX)
  • Time Intelligence Formulas

8Advanced DAX Functions

  • Create calculated joins between tables (UNION, EXCEPT, INTERSECT)
  • Explore expanded tables, physical & virtual relationships, Common relationship functions, etc.
  • Build date tables with DAX, compare custom time periods, manage fiscal calendars, etc.
  • DAX & Power BI Best Practices

9Creating Interactive Reports – Part 1

  • Intro to the Power BI Report View
  • Adding Basic Charts to Power BI Reports
  • Formatting & Filtering Options
  • Matrix Visuals
  • Slicers & Timelines
  • Cards & KPIs
  • Power BI Map Visuals (Basic, Fill, ArcGIS)

10Creating Interactive Reports – Part 2

  • Treemaps, Lines, Areas & Gauges
  • Editing Report interactions
  • Adding Drillthrough Filters
  • Linking to Report Bookmarks
  • Using "What-If" Parameters
  • Managing & Viewing Roles

11Introduction to Power BI Services

  • Administration Tool in Power BI - Admin roles, tenant settings
  • Connecting to a Data Type - Connection types, gateways, dataflows, scheduled refresh, etc.

12 Connecting to Data in Power BI Service

  • Data lineage, bookmarks, data-driven alerts, Q&A, mobile, etc.
  • User roles, apps, publish to the web, usage reports, etc.
  • Static vs. dynamic RLS, testing roles, etc

Projects Overview

Project 1


In every Organisation, Attrition analysis contributes to the details generated by HR managers on employees leaving the company. Employee attrition can take place for a multitude of reasons. The reasons may include employees retiring, finding other job opportunities, or leaving due to unhappiness. Employee Attrition is the calculated number of employees left and divided by the average number of Employees.

For Example, consider a business that employs 100 individuals in 2020. The number of employees decreases throughout the year, and 15 employees leave for several reasons – voluntary and involuntary.

Since the business is not as busy as it used to be, the company decides not to refill the vacated positions to save money on labour costs.

To calculate the employee attrition rate, divide 15 by 100. The calculation will demonstrate that for 2020, the company recorded a 15% employee attrition rate since it is now with 85 employees.

Some of the benefits of attrition analysis are listed below:

  1. It brings to the fore the cause of employee disengagement.
  2. Enables HR managers to develop long-term strategies to reduce attrition
  3. Competitive measures to enhance company brand image
  4. Develops and shapes drills that benefit both the management and the employees
  5. Enhanced work culture

In this project, you have to Prepare a Dashboard for Employees Attrition

Project 2


This Project will be a Completely Independent Project. You will have multiple options of Data Sets to analyze the data and come up with a Report on your own. 

6. Business Intelligence and Visualisation with Tableau syllabus

1Introduction to Tableau

  • Introduction and Installation of Tableau Public                   
  • Tableau Interface and its workflow on how Tableau will create reports                    
  • Explains different types of Products in Tableau and also types of roles in Tableau
  • How is Tableau Dashboard most visual and dynamic compared to PPT

2Connecting to Data

  • Connecting to Data
  • Introduction on Tableau Data Source
  • Types of Data Sources we can connect from Tableau
  • Difference between Live Connections Vs Extract
  • How to Open & Save a Workbook

3Shelves & Card and Analytics Pane

  • Introduction to Marks How to add Quick Visualizations
  • Types of Marks 
  • Different Properties in Marks        
  • Overlapping Marks (Stacking Marks)      
  • Page Shelf and How to use it?  
  • Data Pane Menus and the Tool bar    
  • Analytics Pane and Data Forecasting

4Ways of Sorting, Grouping and Filtering

  • Detail info on Sheet Interface
  • Data Types
  • Dimensions & Measures
  • How to do Sorting, Grouping & Filtering
  • How to Create Sets?
  • Combined Sets
  • Defining Hierarchies in Tableau
  • Introduction to Filters
  • Types of Filters in Tableau
  • Tableau’s Filter order Process 
  • Usage of Context Filter and Sets to Highlight Data
  • Using Split and Custom Split
  • Custom Sort

5Applying Calculated Fields & Parameters

  • Introduction to Calculated Fields
  • Calculation Syntax
  • Creating a Calculated Field
  • Aggregation Calculations
  • Common Aggregation functions
  • Common Calculation functions 
  • Using Calculation functions to create groups
  • Table Calculation on Marks
  • Introduction to Level of Detail (LOD) Explanation
  • Create Basic Parameters and its common Use Cases
  • Creating bins and its uses
  • Customizing Parameters

6Creating a Dashboard and Stories

  • Tableau Dashboard
  • Types of Dashboard Layouts
  • Different Sizing and Objects in Dashboards
  • Formatting Options (Dashboard Level and Workbook Level)
  • Action Filters
  • Creating Stories
  • Best Practices for Data Visualization

7Distributing and Publishing

  • Ways to Distribute
  • Exporting Images, PDFs and Data
  • Workbook File Types
  • Publishing
  • Tableau Online and Tableau Server
  • Publishing Data sources
  • Replacing the Data Source

8Types of Joins and Blending

  • Introduction to Joins
  • Inner & Outer Join
  • Left & Right Join
  • Blending
  • Unions


  • Introduction to Relationships
  • Tableau Data Models (Physical, Logical Layers)
  • Difference between Relationship and Joins
  • Creating and Optimizing a Relationship
  • Types of Relationships
  • Smart Aggregations and its Type

10Prep your data before Loading

  • How to pivot your data before loading
  • How to custom split before loading
  • How to hide data fields which are not used while creating an extract
  • How to unhide data fields.
  • How to sample your data while extracting
  • How to insert a SQL query while loading data and how to aggregate the data in your SQL.

11Create Parameters and Set Actions

  • Interactivity
  • Intro to Parameter Actions
  • Types of Parameter Actions
  • Intro to Set Actions
  • Types of Set Actions

12Advanced Calculation and Analytics in Tableau

  • Into to Advanced Calculations
  • Regular Expressions (RegEx)
  • Looking back at Table Calculations
  • Use Case for Table calculations in a calculated field
  • Trend Lines
  • Clustering and Forecasting

13Continuation of Advance Calculations

  • What is a Nested LOD
  • Use case on Nested LOD
  • What is row wise security
  • How to Use Row wise Security with user filters

14Mapping Function in Tableau

  • Intro to Geo Mapping
  • Geospatial Data
  • Use Case for Geospatial mapping
  • Standard Maps
  • Creating Custom Territories
  • Map Hierarchies
  • Map Layers
  • Background Maps and possible ways
  • Mapping Data based on Background Image
  • Common Issues in Mapping

15Dynamic Designs in Tableau

  • Common Issues in Mapping
  • Introduction to Dynamic Designs
  • Ways to create a Dynamic design 
  • Interactivity Using Actions
  • Dashboard Extensions
  • Viz in Tooltip

Projects Overview

Project 1


The super sample store data set contains the data of orders and details of customers from a superstore in the US. This data includes, customer, order ID, Order date, Ship Date, Category, region etc., We can define some user cases for this dataset. This dashboard shows the KPIs for Quantity, Sales, Profit and Average discount. It depicts the profits/Sales over region YTD, QTD and MTD. The dashboard quickly shows which cities are on the top and least and even shows sales state wise.

The student is expected to create the dashboard based on the following requirements

Tableau Project Overview (Requirement)

General Requirements

  1. Dashboard size is 1250px wide by 900px tall.
  2. The Filter Pane
  3. Do proper formatting
  4. Each chart should have some padding (4) between them and other objects
  5. Each chart should have a grey border, slightly darker than the Pane background color.

Business Requirements

  1. Show four filters- Category, Sub-Category, Region, and Segment. These filters should have only relevant values.
  2. The dashboard should have the title “Executive sales”
  3. The first chart should have the title “YTS KPIs” and should show the following-
  4. Quantity
  5. Total Sales
  6. Total profit
  7. Average Discount

Project 2


This dashboard empowers mission driven organisations to harness the power of data visualisation for social change. Women are tracked away from science and mathematics throughout their education, limiting their training and options to go into these fields as adults. The data set contains the data of women graduated by years, employed persons, Stem Jobs by Education add Salary comparison. This dashboard addresses some major gaps between Men and Women in STEM (Science, Technology, Engineering & Mathematics) fields.

The student is expected to create the dashboard based on the following requirements and analyse the data.

General Requirements

The dashboard should look like an article predicting the basic issues.

Business Requirements

Title: Women in STEM Fields.

  • A pie graph of women in
    • Biological Scientists
    • Chemists & Materials Scientists
    • Computer & Mathematical Occupations
    • Engineers & Architects (Combine all the engineer and architects)
    • The trend of percent women graduating in Computer science and Engineering from 2000-2015

7. Machine Learning & Artificial Intelligence for Mechanical, Civil & EE Engineers using Python syllabus

1Basics of Probability and Statistics

 In this module, we will introduce the ideas of Statistics, machine learning and artificial intelligence. 

  • Basics of Probability
  • Basics of Statistics
  • What is ML & AI

2Basics of ML & AI

In this section, we start with supervised learning

  • Introduction to normal distribution & standard normal distribution
  • Introduction to business moments
  • Artificial Intelligence

3Supervised Learning - Prediction

In this section, we start with supervised learning

  • Introduction to supervised learning
  • What is linear regression
  • One hot encoding
  • Cost function and gradient descent

4Supervised Learning - Classification

In this section, we start with classification algorithm

  • Introduction to classification problems
  • What is logistic regression
  • Cost function and gradient descent

5Supervised Learning - Classification

In this module, we will introduce some more classification algorithms

  • Decision tree
  • Entropy
  • Information gain

6Random forest & Model Evaluation

In this section, we will assess the algorithms

  • Random Forest
  • Bootstrapping and majority rule
  • Evaluation of classifiers

7Supervised Learning - Classification

In this module, we will introduce SVM

  • Support Vector Machines
  • Mathematical intuition behind SVM
  • How SVM is different from other classifiers

8Supervised Learning - Classification

In this section, we will introduce knn

  • K-Nearest Neighbour
  • Lazy Algorithm
  • Single layer Neural Network

9Unsupervised Learning - Kmeans

In this section, we will introduce clustering

  • What is clustering
  • Why clustering is important
  • Kmeans and elbow curve

10Unsupervised Learning - Hierarchical

In this section, we will introduce another type of clustering

  • Hierarchical Clustering
  • Dendrogram
  • Evaluation of clustering algorithms

11Unsupervised Learning - PCA

In this module, we will introduce some feature selection techniques

  • Feature Selection
  • Principal Component Analysis
  • Mathematical intuition behind PCA

12Supervised Learning - Classification

In this section, we will introduce neural network

  • Artificial Neural Network
  • Deep learning
  • Different activation functions
  • Understanding back propagation

Projects Overview


  • Perform the Descriptive analytics to a Cleaned Data.
  • Classification of Cars based on the various parameters.
  • Analysing the Trends of Pricing for various parameters such as Engine size, Fuel Used, Power of the engine etc.


Project 2


Explain the types of various clustering that is Used in ML industry in details. Analyze the data set implement and This data set consists of three types of entities:

(a) The specification of an auto in terms of various characteristics,

(b) Its assigned insurance risk rating,

(c) Its normalized losses in use as compared to other cars.

The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symbolling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe. The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc…), and represents the average loss per car per year. Analyze the same using the Elbow curve 

Implement the Hierarchical Clustering by creating a Dendrogram for the Car data set create the scatter plot related to the same and compare both clustering obtained

7. Data Structures and Algorithm using Java Syllabus


  • Abstract data type and Data Structure
  • Complexity Analysis
  • Asymptotic analysis
  • Comparison of functions
  • Recurrence Relations
  • Time complexity
  • Space complexity
  • Iteration
  • Recursion





  • Arrays
    • Static arrays
    • Dynamic arrays
    • 2D arrays
  • Strings
  • Linked List
    • Singly Linked List and its operations
    • Doubly Linked List and its operations
    • Circular Linked List and its operations


3Stacks and Queues

  • Stacks
    • Implementations- using arrays, using linked lists
    • Operations
    • Applications
  • Queues
    • Implementations- using arrays, using linked list, using two stacks
    • Circular queues
    • Priority queues
  • Amortized Analysis
  • Stacks and Queues in Java




  • Trees
    • Binary Trees: representations.
    • Pre-order, In-order, Post-order traversals
    • Expression trees
    • Successor and Predecessor
    • Binary Search Trees and their operations
    • AVL Trees and their operations
    • Red Black trees, Interval trees, Segment trees, B-trees, B+ trees


5 Heaps and Tries

  • Heaps
    • Min Heap
    • Max Heap
    • Implementation
    • Operations
  • Priority Queues
    • Implementations
    • Uses
  • Tries
    • Implementations
    • Uses





  • Graphs
    • Representation
    • Implementation
    • Types of Graphs
  • Minimum cost spanning tree problem
  • Traversing Graph
    • Depth First Search
    • Breadth First Search
  • Single Source Shortest path problem
    • Dijkstra's algorithm
    • Bellman–Ford algorithm
  • Disjoint Sets
    • Union by rank
    • Path compression
    • Applications



  • Sorting
    • Types of sorting techniques
    • Bubble Sort
    • Insertion Sort
    • Selection Sort
    • Quick Sort
    • Merge Sort
    • Heap Sort
    • Count Sort
    • Bucket Sort
    • Radix Sort
    • Shell Sort
    • Topological Sort

8Searching and Hashing

  •  Searching
    • Linear Search
    • Binary Search
  • Hashing
    • Hash function
    • Collision handling
      • Chaining
      • Open addressing
      • Linear probing, primary clustering
      • Quadratic probing, secondary clustering
      • Double hashing
    • Hash Tables

9 Greedy Algorithms

  • Optimisation Problems
  • Types of algorithms
  • Greedy Algorithms
    • Strategy of Greedy Algorithms
    • Elements of Greedy Algorithms
    • Advantages of Greedy Algorithms
    • Disadvantages of Greedy Algorithms
    • Applications of Greedy Algorithms


10Divide and Conquer

  • Divide and Conquer Techniques
    • Strategy of Divide and Conquer Techniques
    • Advantages of Divide and Conquer Techniques
    • Disadvantages of Divide and Conquer Techniques
    • Master theorem of Divide and Conquer Techniques
    • Applications of Divide and Conquer Techniques
  • Special types of problems
    • Bit Manipulation problems
    • Two pointer problems
    • Sliding Window problems
    • Merge Intervals problems



  • Backtracking
    • Brute Force Approach
    • N Queens Problem
  • String matching Algorithms
    • Brute Force Method
    • KMP
    • Rabin Karp
  • Data Structures for storing strings







12Dynamic Programming

  • Dynamic Programming
    1. Approaches of Dynamic Programming
    2. Top down approach
    3. Bottom up approach
    4. Properties of Dynamic Programming
  • Comparison of Algorithmic Techniques learnt
  • Regular Expressions
  • Pattern matching algorithm
  • Complexity Classes
  • P, NP, NP Hard, NP Complete
  • Is P==NP?
  • Problem Solving Summary





Projects Overview

Project 1


 A set of 20 problems to solve in an optimised way with the given restrictions of time and space complexity.

8. Advanced Deep Learning Syllabus

1Artificial Neural Network (Feed Forward Neural Network)

The introductory week will look into forward feed neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction forward-from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network. We will also look into -

  • Neural Network
  • Different architecture of Neural Network
  • Importance of Neural Network
  • Hyperparameters in Neural Network
  • Different types of Gradient descent methods

2Activation functions in neural networks

In the second week, we will look into activation functions. Activation functions are mathematical equations that are used to determine the output of a neural network. The function is attached to each neuron in the network, and is used to determine whether it should be activated or not, based on whether each neuron's input is relevant for the model's prediction. In activation functions, we will look into- 

  • Conic sections
  • Hyperbolic trigonometric functions
  • Sigmoid activation function
  • Tanhx activation function
  • Relu activation function
  • Softmax activation function

3Deep Learning

Deep learning is a branch of machine learning which is based on artificial neural networks. A neural network is a mimic of the human brain, so in short deep learning is also a mimic of human learning techniques. In deep learning, the software engineer doesnt program everything, instead we focus on developing our neural network. In this week, we will be looking into-

  • Terminologies in deep learning
  • Nomenclature
  • Order of vectorized forms
  • Forward propagation derivation with 1 layer
  • Back propagation derivation with 1 layer
  • Batch size, Iteration and Epoch

4Evaluation of models

The performance of a machine learning model can be characterized in terms of the bias and the variance of the model. A model with high bias makes strong assumptions about the form of the unknown underlying function that maps inputs to outputs in the dataset, such as linear regression. A model with high variance is highly dependent upon the specifics of the training dataset, such as unpruned decision trees. So, what are bias and variance?- Bias is the simplifying assumptions made by the model to make the target function easier to approximate and variance is the amount that the estimate of the target function will change given different training data. So, in this week we will look into different topics related to evaluation of models-

  • Underfitting
  • Overfitting
  • Lasso regularization
  • Ridge regularization
  • Elastic Net regularization

5Improvise the model

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance, bias, or improve predictions.

  • Sparse and convex functions
  • Bagging to avoid overfitting
  • Boosting to avoid underfitting
  • Stacking to avoid underfitting


Regularization is a set of techniques that can prevent overfitting in neural networks and thus improve the accuracy of a Deep Learning model when facing completely new data from the problem domain. And Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. In this week we will look into the different types of regularisers and optimisers- 

  • Frobenius norm regularization
  • Data augmentation
  • Early stopping
  • Adam optimizer
  • Tensorflow 2.0


In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. In this week we will look into more topics related to CNN such as-

  • Basics of CNN
  • Edge detection
  • Padding
  • Stride
  • Simple CNN
  • Difference between CNN & ANN


This week is a continuation of the previous week’s topic. Here we will look into more topics related to CNN.

  • Pooling layers
  • Transfer learning
  • Examples on CNN architecture
  • Combination of different Neural network architecture
  • CNN in python


A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

  • RNN Model
  • Different types of RNN
  • Gradients in RNN
  • Back propagation
  • Difference between RNN & ANN


This week is a continuation of the previous week’s topic. Here we will look into more topics related to RNN.

  • Gated Recurrent Unit (RNN)
  • Long short term memory (LSTM)
  • Bidirectional RNN
  • RNN Implementation in Python

11Basics of NLP

Natural Language Processing(NLP) refers to computer systems designed to understand human language. Human language, like English or Hindi consists of words and sentences, and NLP attempts to extract information from these sentences. Word embedding is any of a set of language modeling and feature learning techniques in natural language processing where words or phrases from the vocabulary are mapped to vectors of real numbers.

  • Stop words
  • Stemming
  • Lemmatization
  • Word2vec
  • Implementation of word2vec in python

12End-to-End ML Project steps

In this week, we will be performing an end-to-end ML/DL project for machine failure.

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

Projects Overview

Project 1


The data set provided describes a Credit card fraud SMS . Load the data and regularise it. Once the data is organised, perform the EDA which includes

  • Data Cleaning,
  • Train and Test data set ,
  • Choosing a particular algorithm for building the Model,

Calculate the Accuracy for the Train and Test Data set individually and evaluate the same over the graphs as well

Expected Outcomes of the Project

  • In depth knowledge of EDA
  • Detailed knowhow on various Algorithms
  • Model creation and evaluation
  • Accuracy calculation and confusion matrix  

Flexible Course Fees

Choose the Master’s plan that’s right for you


9 Months Access


Per month for 10 months

  • Access Duration : 9 Months
  • Mode of Delivery : Online
  • Project Portfolio : Available
  • Certification : Available
  • Individual Video Support : 8/Month
  • Group Video Support : 8/Month
  • Email Support : Available
  • Forum Support : Available

Lifetime Access


Per month for 10 months

  • Master's Assistance : Lifetime
  • Access Duration : Lifetime
  • Mode of Delivery : Online
  • Project Portfolio : Available
  • Certification : Available
  • Individual Video Support : 24*7
  • Group Video Support : 24*7
  • Email Support : Available
  • Forum Support : Available
  • Telephone Support : Available
  • Dedicated Support Engineer : Available


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  • Top 5% of the class will get a merit certificate
  • Course completion certificates will be provided to all students
  • Build a professional portfolio
  • Automatically link your technical projects
  • E-verified profile that can be shared on LinkedIn


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1Who are the instructors and what is the learning process?

The instructors who have created the courses would have an average of 7 years experience in the field of Data science and the learning process would be an introduction to the Maths & Statistics involved behind Machine Learning & AI hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, programming in R,SQL, then followed with  data wrangling, data visualization, regression models and how to use the libraries. It will conclude with how you can view, visualize and work with the data you have to make data-driven decisions. By completing this you will have gained skills and help you prepare for the role of Data scientist.

2Are there any prerequisites for this course?

Programming in Python (Fundamentals)

3What kind of support I can expect? What if I have doubts?

Your doubts will be cleared by our in-house Technical support engineers.

4How is this different from what I learnt in college?

In your under graduation, you would have learnt the Mathematical concepts to a certain extent and a few related theoretical concepts on Data Science, but here you would be applying the concepts being covered in our courses in real-time & industry relevant projects.(Data sets would be similar to what is being used in the industry)

5What advantages will I gain by taking this course?

This course will cover topics and projects to give you extensive knowledge and gain hands-on experiences of what you can look forward to doing in the industry as a Data Analyst/Scientist.

6Will the software be provided?

Yes, Software will be provided.

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