DATA SCIENCE | SKILL AI

Course covers Python/R, Statistics, Machine Learning algorithms, Business aspects and Tableau.
Intensive 3 months weekdays & weekends Classroom/LVC Training and 4 months LIVE Project mentoring.
Unlimited access to Data Science Cloud Lab for practice.

DATA SCIENCE COURSES

4.9 (56,150) reviews

Accredited by

3+ Live
Projects

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Skill AI
Certificate

DATA SCIENCE CERTIFICATION AUTHORITIES

Live Virtual

Instructor Led Live Online

₹55,221

NASSCOM® Certification

8-Month | 700 Learning Hours

120-Hour Live Online Training

25 Capstone & 1 Client Project

365 Days Flexi Pass + Cloud Lab

Internship + Job Assistance

Enquire Now

DATA SCIENCE COURSE FEE

Blended Learning

Self Learning + Live Mentoring

₹32,505

Self Learning + Live Mentoring

NASSCOM® Certification

1 Year Access To Elearning

25 Capstone & 1 Client Project

Job Assistance

24*7 Leaner assistance and support

Enquire Now

Classroom

In - Person Classroom Training

₹60,432

NASSCOM® Certification

8-Month | 700 Learning Hours

120-Hour Classroom Sessions

25 Capstone & 1 Client Project

Cloud Lab Access

Internship + Job Assistance

Enquire Now

Most Advanced Data Science Training Course That Cover All In-demand Tools & Technologies

Financing Options

We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.

Pay In Installments, as low as

We have partnered with the following financing companies to provide competitive finance options at as low as
0% interest rates with no hidden cost.

Admission Closes On : 31st December 2026

Apply Now

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WHY SKILL AI INSTITUTE FOR DATA SCIENCE COURSES

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Expert Trainers

PH.Ds AND INDUSTRY EXPERTS

ELITE FACULTY FROM PRESTIGIOUS
UNIVERSITIES WITH DEEP RESEARCH
AND COACHING EXPERTISE

Specialized Syllabus

SPECIALIZED SYLLABUS FOR MANAGERS

PERSONALIZED COUNSELLING FOR CAREER
ENHANCEMENT IN MANAGERIAL ROLES

FOCUSED ON DATA SCIENCE FOR DECISION MAKING,
MANAGING DATA SCIENCE PROJECTS WITH ESSENTIAL TECHNICAL OVERVIEW

Career Guidance

EXPERT COUNSELORS

TECHNIQUES FOR SCENARIOS WITH CERTAINTY,
LOW UNCERTAINTY AND HIGH CERTAINTY FROM
DECISION TREE TO MONTE CARLO SIMULATION

5 Case Studies

PRACTICAL DECISION-MAKING CASES

SYLLABUS OF DATA SCIENCE COURSES

MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Evolution of Data Science
• Big Data Vs Data Science
• Data Science Terminologies
• Data Science vs AI/Machine Learning
• Data Science vs Analytics
MODULE 2: DATA SCIENCE DEMO
• Business Requirement: Use Case
• Data Preparation
• Machine learning Model building
• Prediction with ML model
• Delivering Business Value.
MODULE 3: ANALYTICS CLASSIFICATION
• Types of Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• EDA and insight gathering demo in Tableau
MODULE 4: DATA SCIENCE AND RELATED FIELDS
• Introduction to AI
• Introduction to Computer Vision
• Introduction to Natural Language Processing
• Introduction to Reinforcement Learning
• Introduction to GAN
• Introduction to Generative Passive Models
MODULE 5: DATA SCIENCE ROLES & WORKFLOW
• Data Science Project workflow
• Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
• Data Science Project stages.
MODULE 6: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS
• Data Science in Finance and Banking
• Data Science in Retail
• Data Science in Health Care
• Data Science in Logistics and Supply Chain
• Data Science in Technology Industry
• Data Science in Manufacturing
• Data Science in Agriculture
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1: OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
MODULE 2: HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3: EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empirical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
MODULE 4: HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Review
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner Join, Outer Join
• Left Join, Right Join
• Self Join, Cross join
• Windows function: Over, Partition, Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7 : DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code Commits
• Pull, Fetch and Conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
MODULE 1: BIG DATA INTRODUCTION
• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction
MODULE 2 : HDFS AND MAP REDUCE
• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners,
• Combiners, Shuffle, and Sort
MODULE 3: PYSPARK FOUNDATION
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
MODULE 4: SPARK SQL and HADOOP HIVE
• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3 : DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4: CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

DATA SCIENCE COURSES

4.9 (56,150) reviews

Accredited by

3+ Live Projects

Skill AI Certificate

DATA SCIENCE CERTIFICATION AUTHORITIES

Live Virtual

Instructor Led Live Online

₹55,221

DATA SCIENCE COURSE FEE

Blended Learning

Self Learning + Live Mentoring

₹32,505

Classroom

In - Person Classroom Training

₹60,432

Most Advanced Data Science Training Course That Cover All In-demand Tools & Technologies

Financing Options

Pay In Installments, as low as

We have partnered with the following financing companies to provide competitive finance options at as low as 0% interest rates with no hidden cost.

Admission Closes On : 31st December 2026

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

WHY SKILL AI INSTITUTE FOR DATA SCIENCE COURSES

Expert Trainers

PH.Ds AND INDUSTRY EXPERTS

ELITE FACULTY FROM PRESTIGIOUS UNIVERSITIES WITH DEEP RESEARCH AND COACHING EXPERTISE

Specialized Syllabus

SPECIALIZED SYLLABUS FOR MANAGERS

PERSONALIZED COUNSELLING FOR CAREER​ ENHANCEMENT IN MANAGERIAL ROLES

FOCUSED ON DATA SCIENCE FOR DECISION MAKING, MANAGING DATA SCIENCE PROJECTS WITH ESSENTIAL TECHNICAL OVERVIEW

Career Guidance

EXPERT COUNSELORS

TECHNIQUES FOR SCENARIOS WITH CERTAINTY, LOW UNCERTAINTY AND HIGH CERTAINTY FROM DECISION TREE TO MONTE CARLO SIMULATION

5 Case Studies

PRACTICAL DECISION-MAKING CASES

SYLLABUS OF DATA SCIENCE COURSES

MODULE 1: DATA SCIENCE ESSENTIALS

MODULE 2: DATA SCIENCE DEMO

MODULE 3: ANALYTICS CLASSIFICATION

MODULE 4: DATA SCIENCE AND RELATED FIELDS

MODULE 5: DATA SCIENCE ROLES & WORKFLOW

MODULE 6: MACHINE LEARNING INTRODUCTION

MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS

MODULE 1: PYTHON BASICS

MODULE 2: PYTHON CONTROL STATEMENTS

MODULE 3: PYTHON DATA STRUCTURES

MODULE 4: PYTHON FUNCTIONS

MODULE 1: OVERVIEW OF STATISTICS

MODULE 2: HARNESSING DATA

MODULE 3: EXPLORATORY DATA ANALYSIS

MODULE 4: HYPOTHESIS TESTING

MODULE 1: MACHINE LEARNING INTRODUCTION

MODULE 2: PYTHON NUMPY PACKAGE

MODULE 3: PYTHON PANDAS PACKAGE

MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

MODULE 6: ML ALGO: LINEAR REGRESSSION

MODULE 7: ML ALGO: LOGISTIC REGRESSION

MODULE 8: ML ALGO: K MEANS CLUSTERING

MODULE 9: ML ALGO: KNN

MODULE 1: FEATURE ENGINEERING

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

MODULE 4: ML ALGO: DECISION TREE

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING

MODULE 6: ML ALGO: NAÏVE BAYES

MODULE 7: GRADIENT BOOSTING, XGBOOST

MODULE 1: TIME SERIES FORECASTING - ARIMA

MODULE 2: SENTIMENT ANALYSIS

MODULE 3: REGULAR EXPRESSIONS WITH PYTHON

MODULE 4: ML MODEL DEPLOYMENT WITH FLASK

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

MODULE 6: AWS CLOUD FOR DATA SCIENCE

MODULE 7: AZURE FOR DATA SCIENCE

MODULE 8: INTRODUCTION TO DEEP LEARNING

MODULE 1: DATABASE INTRODUCTION

MODULE 2: SQL BASICS

MODULE 3: DATA TYPES AND CONSTRAINTS

MODULE 4: DATABASES AND TABLES (MySQL)

MODULE 5: SQL JOINS

3+ Live
Projects

Skill AI
Certificate

We have partnered with the following financing companies to provide competitive finance options at as low as
0% interest rates with no hidden cost.

ELITE FACULTY FROM PRESTIGIOUS
UNIVERSITIES WITH DEEP RESEARCH
AND COACHING EXPERTISE

PERSONALIZED COUNSELLING FOR CAREER
ENHANCEMENT IN MANAGERIAL ROLES

FOCUSED ON DATA SCIENCE FOR DECISION MAKING,
MANAGING DATA SCIENCE PROJECTS WITH ESSENTIAL TECHNICAL OVERVIEW

TECHNIQUES FOR SCENARIOS WITH CERTAINTY,
LOW UNCERTAINTY AND HIGH CERTAINTY FROM
DECISION TREE TO MONTE CARLO SIMULATION