Master
Data Science
With Industry Experts
Comprehensive training in Python, R, Statistics, Machine Learning, and Tableau with 3 months intensive training + 4 months LIVE project mentoring.
- 3+ Live Projects
- Skill AI Certificate
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Choose Your Learning Path
Blended Learning
Self Learning + Live Mentoring
₹27,005
- Self Learning + Live Mentoring
- NASSCOM® Certification
- 1 Year Access to E-learning
- 25 Capstone & 1 Client Project
- Job Assistance
- 24*7 Leaner assistance and support
Live Virtual
₹55,005
- 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
Classroom
₹63,005
- NASSCOM® Certification
- 8-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- Cloud Lab Access
- Internship + Job Assistance
Tools & Technologies You'll Master
Python Programming
My SQL
Power BI
Excel
Sea Born
Tableau
Agile
Matplotlib
Numpy
Pandas
Why Skill AI Institute?
Expert Trainers
Ph.ds And Industry Experts
Elite faculty from prestigious
Universities with deep research
And coaching expertise
Career Guidance
Expert Counselors
Personalized counselling for career Enhancement in managerial roles
Specialized Syllabus
Specialized Syllabus For Managers
Focused on data science for decision making, Managing data science projects with essential technical overview
5 Case Studies
Practical Decision-making Cases
Techniques for scenarios with certainty, Low uncertainty and high certainty from Decision tree to monte carlo simulation
Flexible Financing Options
We’re dedicated to making our programs accessible. Pay in easy installments at 0% interest with no hidden costs.
EMI Available
Bajaj Finserv & ShopSe
Admission Closing
31st December 2026
Course Syllabus
Data Science Foundation – 7 Modules
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
Python Foundation – 4 Modules
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
Statistics Essentials – 4 Modules
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
Machine Learning Associate – 9 Modules
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)
Machine Learning Expert – 6 Modules
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
Advanced Data Science – 8 Modules
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
Database: Sql And Mongodb – 6 Modules
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
Git – 4 Modules
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
Big Data Foundation – 3 Modules
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
Bi Analyst – 4 Modules
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
How Data Science Use In All Field?
Paytm
- Fraud detection in transactions
- Credit scoring for loans
- Personalized cashback offers
- Spending behavior analysis
Technologies used:
- Python
- Apache Spark
- TensorFlow
- SQL & Hadoop
- Machine Learning models for fraud detection
Swiggy
- Restaurant recommendations
- Delivery route optimization
- Demand forecasting
- Dynamic delivery pricing
Technologies used:
- Python
- Machine Learning algorithms
- Google Maps APIs
- Deep Learning
- Data visualization Power BI
Zomato
- Restaurant ranking algorithm
- Food recommendation system
- Customer sentiment analysis from reviews
- Delivery time prediction
Technologies used:
- Python
- Natural Language Processing (NLP)
- TensorFlow / PyTorch
- Big Data tools (Hadoop / Spark)
Flipkart
- Product recommendation system
- Dynamic pricing
- Customer behavior prediction
- Inventory forecasting
Technologies used:
- R
- Apache Spark
- Machine Learning algorithms
- Data pipelines (Kafka / Hadoop)
OLA
- Surge pricing algorithm
- Driver allocation optimization
- ETA prediction
- Demand & supply forecasting
Technologies used:
- Python
- Machine Learning
- Big Data analytics
- Real-time data processing (Spark Streaming)
TESTIMONIALS
What Our Students Have To Say
Because of this, I am attracting many companies for job interviews on LinkedIn.
Truly impressed with their support and guidance.
Because of this, I am attracting many companies for job interviews on LinkedIn.
Truly impressed with their support and guidance.