• 110+ Hours of Live Online Sessions
  • 24/7 Access to LMS Portal
  • Periodical Mock Test
  • One to One Performance Assessment
  • 360o support through Digital Learning Centres across Tamilnadu

Unique highlights of the programme

In this practical, hands-on course, the main objective is to help students understand the ins and outs of data science. This online training course takes you from the basics to mastery and will equip you with the skills required to create software programs, scrape websites, data extraction, and build automation.

  • Experienced corporate trainer onboard
  • Real-Time industrial teaching
  • Flexible self-paced learning
  • Comprehensive curriculum and hands-on learning
  • Industry oriented Capstone Project
  • Learn in English or in a regional language of your choice

Academic curriculum

Module 1 - Data Science Fundamentals

Data Science Overview
Define Data Science
Data Science Importance
Python and Its importance
Roles and responsibilities of Data Scientist
Various Applications of Data Science
Data Analytics Overview
Data Analytics Process and Its steps
Skills and Tools Required for Data Analysis
Challenges in Data Analytic Processes
Data Visualization Technique
Exploratory Data Analysis Technique
Hypothesis Testing

Module 2 - Statistical Analysis and Business Applications

Statistical vs Non-Statistical Analysis
Major categories of Statistics
Statistical Analysis Process
Bell Curve
Hypothesis Testing
Data Types and Variable types
Types of Frequencies
Chi- Square test
Correlation matrix
Inferential Statistics
Use of range() function

Module 3 - Numeric & Scientific Computing with Python

Numeric Computing with Python
Difference between Numpy and Scipy Package
Numpy Fundamentals
Numpy DataTypes
Working with Arrays
Boolean and fancy Indexing
Generating Data With Numpy
Scientific Computing with Python
SciPy and its characteristics
ScipPy Sub-Package
Sub-package Optimization
Sub-package Linear Algebra
Sub-package Statistics
Sub-package Weave and IO
Sample Programs

Madule 4 - Data Manipulation, Getting & Cleaning Data

Data Manipulation
Pandas and its Features
Data Structures
Data Operations
Data Standardization
Pandas SQL Operations
Getting Data
Understanding the domain and the dataset
Importing and Exporting Data
Basic Insights from dataset
Cleaning Data
Missing values and Formatting data
Normalization, Indicator Variables and Binning

Module 5 - Encapsulating the data & Development of Model

Encapsulating the data
Descriptive Statistics
Development of Model
Simple and Multiple Linear Regression
R-squared and MSE for sample Evaluation
Polynomial Regression nd Pipelines
Prediction and Decision Making

Module 6 - Evaluation of Model

Model Evaluation
Over-fitting and Under-fitting
Model Selection
Ridge Regression
Grid Search
Refining the model

Module 7 - Data Visualization & Types of Plots

Data Visualization in Python using Matplotlib
Data Visualization
Considerations of Data Visualization
Factors of Data Visualization
Python libraries for Data Visualization
Steps to create a plot
Line Properties
Alpha and Annotation
Multiple Plots
Types of Plots
Seaborn module
Pandas built in functions for data visualization
Basic Visualization Tools
Specialized Visualization Tools
Advanced Visualization Tools
Plotly and Cufflinks
Maps and Geospatial Data
Netflix Project Discussion

Module 8 - Hadoop & Apache Spark

Python Integration with Hadoop Map Reduce and Spark
Integrating python with Hadoop
Hadoop Components and system architecture
Map Reduce
Cloudera Quickstart Virtual Machine
VMware Image
Word Count Program
Apache Spark
Advantages of Spark
Python API for Spark
Spark Tools
Setting up Apache Spark

Module 9 - Web Scraping & Natural Language Processing

Web Scraping with Beautiful Soup
Web Scraping and its advantages
Web Scraping process
Beautiful Soup and its Features
Searching Tree
Navigating Options
Output : Printing and Formatting
Natural Language Processing with Scikit Learn
NLP – Benefits, Applications
Modules to Load content and category
Implementation of Extraction
Feature Extraction Techniques

Module 10 - Regression Cluster Analysis

Linear regression
Logistic regression
Lasso regression
Bayesian Linear regression
Cluster Analysis
K- means

Module 11 - Principal Component Analysis & Recommender Systems

Principal Component Analysis
Eigen values and Vectors
Recommender Systems
Simple Recommender
Content Based Recommender
Colloborative Filtering Engines

Module 12 - Neural Nets and Deep Learning

Neural Networks

MODULE – 13 Supervised & Unsupervised Learning

Supervised Learning
Classification Problems
Fine-tuning the model
Preprocessing and Pipelines
Unsupervised Learning
Clustering data
Decorerelating and Dimension Reduction
Interpretation – NMF
K-means and Hierarchical clustering
Agglomerative and Divisive Hierarchical Clustering
Density Based Clustering
Principal Component Analysis
Singular Value Decomposition
Independent Component Analysis
Analysis with the Real Life Projects

Capstone Project

Project Discussion 1
Project Discussion 2

Check your eligibility

Anyone interested in learning Data science to advance their job role and designation. This Data science course is also well-suited for:

  • Engineering students & Graduates
  • Software developers
  • Software engineers
  • Technical leads
  • Architect
  • Programming enthusiasts


Basic programming knowledge or experience is necessary to take this online course.

Potential recruiters

Post your programme, you will become an expert in Data science using python who will create programs, apps, scripts, games and so much more. These traits qualify you to get hired by the best names in the business.

  • recruit
  • recruit
  • recruit
  • recruit

Admission Process

Sign UP To Get The
3-step easy admission process

  • Step 01

    Fill the application form

    Apply and get admitted in your favourite course.

  • Step 02

    Admissions Test & Interview

    Complete the training and get certfied by Industry and Leading Institutions

  • Step 03

    Join program

    Attain Placement Assistance and have a fruitful career