Data Science

Program Overview

Eligibility Criteria

  • BE, B. Tech, ME, M. Tech in ECE / CSE / IT/ EEE / EIE / Electronics / Biomedical/Instrumentation/ Mechatronics / M.Sc. Electronics and other relevant streams.

 

  • Note: Working candidates who are working in relevant streams can take the direct admission.

 

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Career Accelerator Programs

Month Programs

Month Program

Interactive Training on Techniques of Data Science with R and Python

Month Program

Interactive Training on Techniques of Data Science with R and Python

Program Module

1 Month Program
No.of weeksCourse Overview
Week 1Editing in the cells.
Finding the Minimum and Maximum
Calculating the percentage
Payroll deduction, Total Salary, Net Salary
Working with Goal Seak
Aligning cells, using wrap text, Bold, Centre
Working with Charts, 2D &3D
Typing simple formulas
Filling a series
Inserting and Deleting a worksheet
Copying a worksheet
Renaming a worksheet
Moving or copying a sheet to another work book
Chasing worksheet tab colour
Grouping worksheetsFreezing Rows and Columns
Selecting Ranges, Selecting Rows & Selecting Columns
Formatting : Applying General Formatting, Chasing fonts, font size
Borders: Applying Border to a Range, Wrapping and Merging text.
Understanding Function: SUM, AVERAGE, MINIMUM VALUE
Formulas and Functions:
Week 2Quick Analysis
Quick Formatting
Quick Charting
Quick Totals
Quick Spark lines
Quick Tables
Quick Analysis tools
Printing a Worksheet
The Charting Process:
Choosing the Right Chart
Using a Recommended Chart
Creating a new chart from scratch
Working with an Embedded chart
Resizing a Chart
Repositioning a chart
Printing an Embedded chart
Creating a chart sheet.
Changing the chart type
Changing the chart layout.
Changing the chart style.
Week 3Printing a chart sheet.
Embedding a chart into A work sheet.
Deleting a chart.
Practise( eg: creating a charts).
V LOOKUP
COUNT IFS:
COUNT
IF
COUNTIF
COUNTIFS
Fillers:
Quick filtering
Filtering by Multiple criteria
Saving the filtered date.
Performing calculation on filtered data.
Pivot table:
Defined.
Basic Pivot table data.
Inserting Pivot table
Pivot table Geography.
Week 4Building a Pivot table Report – Point one
Adding row labels, adding column data.
Changing formulas in columns, changing headers and number formats
Building a pivot table report – Point two
Adding multiple row lables,
Collapsing and expanding, drill down to data
Sorting and refreshing.
Building a Pivot table report – Point three
Grouping by dates,
Grouping by ranges,
Show item with no details
Show item with no details
Assignment, Test, Mini project
Main project
3 Month Program
No.of weeksCourse Overview
Week 1Editing in the cells.
Finding the Minimum and Maximum
Calculating the percentage
Payroll deduction, Total Salary, Net Salary
Working with Goal Seak
Aligning cells, using wrap text, Bold, Centre
Working with Charts, 2D &3D
Typing simple formulas
Filling a series
Inserting and Deleting a worksheet
Copying a worksheet
Renaming a worksheet
Moving or copying a sheet to another work book
Chasing worksheet tab colour
Grouping worksheetsFreezing Rows and Columns
Selecting Ranges, Selecting Rows & Selecting Columns
Formatting : Applying General Formatting, Chasing fonts, font size
Borders: Applying Border to a Range, Wrapping and Merging text.
Understanding Function: SUM, AVERAGE, MINIMUM VALUE
Formulas and Functions: 
Week 2Quick Analysis
Quick Formatting
Quick Charting 
Quick Totals 
Quick Spark lines
Quick Tables
Quick Analysis tools
Printing a Worksheet
The Charting Process:
Choosing the Right Chart
Using a Recommended Chart
Creating a new chart from scratch
Working with an Embedded chart
Resizing a Chart
Repositioning a chart
Printing an Embedded chart
Creating a chart sheet.
Changing the chart type
Changing the chart layout.
Changing the chart style.
Week 3Printing a chart sheet.
Embedding a chart into A work sheet.
Deleting a chart.
Practise( eg: creating a charts).
V LOOKUP 
COUNT IFS:
COUNT 
IF
COUNTIF
COUNTIFS
Fillers:
Quick filtering
Filtering by Multiple criteria
Saving the filtered date.
Performing calculation on filtered data.
Pivot table:
Defined.
Basic Pivot table data.
Inserting Pivot table
Pivot table Geography.
Week 4Building a Pivot table Report – Point one 
Adding row labels, adding column data.
Changing formulas in columns, changing headers and number formats
Building a pivot table report – Point two
Adding multiple row lables,
Collapsing and expanding, drill down to data
Sorting and refreshing.
Building a Pivot table report – Point three
Grouping by dates,
Grouping by ranges, 
Show item with no details
Show item with no details
Assignment, Test, Mini project
Main project
Week 5Introduction to Python & Setup
Data types
variables
control structures
Functions, modules, error handling
Week 6Functions, modules, error handling
Working with NumPy
Working with NumPy
Data handling with Pandas
Data handling with Pandas
Week 7Data handling with Pandas
Data visualization with Matplotlib & Seaborn
Data visualization with Matplotlib & Seaborn
Mini project: Exploratory Data Analysis
Mini project: Exploratory Data Analysis
Week 8Descriptive Statistics
Descriptive Statistics
Probability Theory & Distributions
Probability Theory & Distributions
Probability Theory & Distributions
Week 9Inferential Statistics & Hypothesis Testing
Inferential Statistics & Hypothesis Testing
Inferential Statistics & Hypothesis Testing
Confidence Intervals & t-tests
Confidence Intervals & t-tests
Week 10ANOVA, Chi-Square tests
ANOVA, Chi-Square tests
Correlation & Regression
Correlation & Regression
Hands-on statistical analysis in Python
Week 11Hands-on statistical analysis in Python
Introduction to Databases & SQL
Introduction to Databases & SQL
SELECT, WHERE, GROUP BY, ORDER BY
SELECT, WHERE, GROUP BY, ORDER BY
Week 12Joins, Subqueries, CTEs
Joins, Subqueries, CTEs
Window Functions & Aggregations
Data wrangling & practice scenarios
Handling missing data
Week 13Encoding categorical data
Feature scaling & transformation
Data pipelines using Pandas & Scikit-learn
Data pipelines using Pandas & Scikit-learn
Outlier detection & handling

 

6 Month Program
No.of weeksCourse Overview
Week 1Editing in the cells.
Finding the Minimum and Maximum
Calculating the percentage
Payroll deduction, Total Salary, Net Salary
Working with Goal Seak
Aligning cells, using wrap text, Bold, Centre
Working with Charts, 2D &3D
Typing simple formulas
Filling a series
Inserting and Deleting a worksheet
Copying a worksheet
Renaming a worksheet
Moving or copying a sheet to another work book
Chasing worksheet tab colour
Grouping worksheetsFreezing Rows and Columns
Selecting Ranges, Selecting Rows & Selecting Columns
Formatting : Applying General Formatting, Chasing fonts, font size
Borders: Applying Border to a Range, Wrapping and Merging text.
Understanding Function: SUM, AVERAGE, MINIMUM VALUE
Formulas and Functions: 
Week 2Quick Analysis
Quick Formatting
Quick Charting 
Quick Totals 
Quick Spark lines
Quick Tables
Quick Analysis tools
Printing a Worksheet
The Charting Process:
Choosing the Right Chart
Using a Recommended Chart
Creating a new chart from scratch
Working with an Embedded chart
Resizing a Chart
Repositioning a chart
Printing an Embedded chart
Creating a chart sheet.
Changing the chart type
Changing the chart layout.
Changing the chart style.
Week 3Printing a chart sheet.
Embedding a chart into A work sheet.
Deleting a chart.
Practise( eg: creating a charts).
V LOOKUP 
COUNT IFS:
COUNT 
IF
COUNTIF
COUNTIFS
Fillers:
Quick filtering
Filtering by Multiple criteria
Saving the filtered date.
Performing calculation on filtered data.
Pivot table:
Defined.
Basic Pivot table data.
Inserting Pivot table
Pivot table Geography.
Week 4Building a Pivot table Report – Point one 
Adding row labels, adding column data.
Changing formulas in columns, changing headers and number formats
Building a pivot table report – Point two
Adding multiple row lables,
Collapsing and expanding, drill down to data
Sorting and refreshing.
Building a Pivot table report – Point three
Grouping by dates,
Grouping by ranges, 
Show item with no details
Show item with no details
Assignment, Test, Mini project
Main project
Week 5Introduction to Python & Setup
Data types
variables
control structures
Functions, modules, error handling
Week 6Functions, modules, error handling
Working with NumPy
Working with NumPy
Data handling with Pandas
Data handling with Pandas
Week 7Data handling with Pandas
Data visualization with Matplotlib & Seaborn
Data visualization with Matplotlib & Seaborn
Mini project: Exploratory Data Analysis
Mini project: Exploratory Data Analysis
Week 8Descriptive Statistics
Descriptive Statistics
Probability Theory & Distributions
Probability Theory & Distributions
Probability Theory & Distributions
Week 9Inferential Statistics & Hypothesis Testing
Inferential Statistics & Hypothesis Testing
Inferential Statistics & Hypothesis Testing
Confidence Intervals & t-tests
Confidence Intervals & t-tests
Week 10ANOVA, Chi-Square tests
ANOVA, Chi-Square tests
Correlation & Regression
Correlation & Regression
Hands-on statistical analysis in Python
Week 11Hands-on statistical analysis in Python
Introduction to Databases & SQL
Introduction to Databases & SQL
SELECT, WHERE, GROUP BY, ORDER BY
SELECT, WHERE, GROUP BY, ORDER BY
Week 12Joins, Subqueries, CTEs
Joins, Subqueries, CTEs
Window Functions & Aggregations
Data wrangling & practice scenarios
Handling missing data
Week 13Encoding categorical data
Feature scaling & transformation
Data pipelines using Pandas & Scikit-learn
Data pipelines using Pandas & Scikit-learn
Outlier detection & handling
Week 14Data quality assessment
Mini project: Clean real-world dataset
ML basics & workflows
Supervised Learning: Linear, Logistic Regression
Supervised Learning: Linear, Logistic Regression
Week 15Supervised Learning: Linear, Logistic Regression
Supervised Learning: Linear, Logistic Regression
Decision Trees, Random Forest
Decision Trees, Random Forest
Decision Trees, Random Forest
Week 16Decision Trees, Random Forest
Decision Trees, Random Forest
Unsupervised Learning: K-Means, PCA
Unsupervised Learning: K-Means, PCA
Unsupervised Learning: K-Means, PCA
Week 17Unsupervised Learning: K-Means, PCA
Unsupervised Learning: K-Means, PCA
Model Evaluation & Validation
Model Evaluation & Validation
Model Evaluation & Validation
Week 18Feature Engineering & Selection
Feature Engineering & Selection
Hyperparameter tuning
Hyperparameter tuning
Final Project 1: Predictive Analytics
Week 19Final Project 1: Predictive Analytics
Principles of Data Viz
Tableau/Power BI Basics
Tableau/Power BI Basics
Dashboards & storytelling
Week 20Dashboards & storytelling
Dashboards & storytelling
Visualizations in Plotly
Dynamic dashboards in Python
Final Project 2: Dashboard for business insights
Week 21Final Project 2: Dashboard for business insights
Final Project 2: Dashboard for business insights
Final Project 2: Dashboard for business insights
Time Series Forecasting
Time Series Forecasting
Week 22Time Series Forecasting
Time Series Forecasting
Text Analytics (NLP basics)
Text Analytics (NLP basics)
Text Analytics (NLP basics)
Week 23Real-world Case Study 1: Retail Sales
Real-world Case Study 1: Retail Sales
Real-world Case Study 1: Retail Sales
Real-world Case Study 2: Customer Churn
Real-world Case Study 2: Customer Churn
Week 24Real-world Case Study 2: Customer Churn
Real-world Case Study 3: Healthcare Analytics
Real-world Case Study 3: Healthcare Analytics
Real-world Case Study 3: Healthcare Analytics
Capstone Project Planning
Week 25Capstone Project Planning
Guided Capstone Development
Guided Capstone Development
Guided Capstone Development
Guided Capstone Development
Week 26Guided Capstone Development
Project Presentation & Peer Review
Project Presentation & Peer Review
Resume, GitHub Portfolio, Mock Interviews
Resume, GitHub Portfolio, Mock Interviews

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