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Program Rating
Program Duration
Post-Program Job Support
Learn Data Science, Machine Learning and AI With Live Classes and Real Projects
65%
Average Salary Hike
15000+
Career Transitions
300+
Hiring Partners
65%
Average Salary Hike
15000+
Career Transitions
300+
Hiring Partners
What You Get in This Program
Live classes with industry practitioners
Mock technical and HR interviews
No prior coding experience required
Machine learning, deep learning and NLP
1:1 mentorship
Agentic AI and AI tools included
A project at the end of every section
LMS access any time
Dedicated program manager
Capstone: build your own AI data scientist agent
Regular assessments
Fingertips certificate of completion
Skills You'll Learn
Statistics
SQL
Data Visualization
Python
Machine Learning
Deep Learning
NLP
Agentic AI
Web Scraping
About the Data Science and AI Masters Program
The Data Science and AI Masters Program is an eight-month, live course that covers the full data science stack. You'll start with statistics, Excel, SQL and BI dashboards, learn Python and its data libraries, then build machine learning, deep learning and NLP models. The final sections teach you to use AI agents to automate analysis and model pipelines. It's built so that people from non-technical backgrounds can follow it from the first class.
You'll be able to clean and analyse data with SQL and Python, build dashboards in Power BI and Tableau, train and evaluate machine learning models, build basic neural networks and NLP models, and use AI agents to speed up your work. You'll also leave with a portfolio of projects.
Excel, MySQL, Power BI, Tableau, Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow or PyTorch, Google Colab, BeautifulSoup, Selenium, Playwright, ChatGPT, Claude, Gemini, LangChain and LlamaIndex.
Eight months of live, instructor-led training, including hands-on projects in every section.
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1
Curriculum
Statistics & Data Science Foundation
- Module 1: Data Science & Analytics Foundation
- Module 2: Data Understanding & Exploration
- Module 3: Descriptive Statistics
- Module 4: Probability Fundamentals
- Module 5: Probability Distributions
- Module 6: Inferential Statistics
- Module 7: Hypothesis Testing
- Module 8: Correlation & Regression Analysis
- Module 9: Data Visualization Fundamentals
Database Management with SQL
- Module 1: Database & SQL Foundation
- Module 2: Data Definition & Table Operations
- Module 3: SQL Querying Basics
- Module 4: Aggregate Functions & Grouping
- Module 5: SQL Joins & Relationships
- Module 6: Subqueries & Nested Queries
- Module 7: Advanced SQL Functions
- Module 8: Views, Indexes & Stored Procedures
- Module 9: SQL Projects & Business Applications
Data Visualization with Tableau
- Module 1: Tableau Foundation & Interface
- Module 2: Data Connection & Preparation
- Module 3: Basic Visualizations
- Module 4: Advanced Visualizations
- Module 5: Filters & Interactivity
- Module 6: LOD Expressions & Calculations
- Module 7: Dashboard Designing
- Module 8: Story Points & Presentation
- Module 9: Performance Optimization
- Module 10: Tableau Projects & Capstone
Business Analytics with Power BI
- Module 1: Power BI Foundation & Architecture
- Module 2: Data Connection & Loading
- Module 3: Power Query (Data Transformation)
- Module 4: Data Modeling
- Module 5: DAX (Data Analysis Expressions)
- Module 6: Data Visualization in Power BI
- Module 7: Filters & Interactivity
- Module 8: Advanced Power BI Features
Python Programming Foundation
- Module 1: Python Foundation & Setup
- Module 2: Data Types & Variables
- Module 3: Operators & Expressions
- Module 4: Control Statements
- Module 5: Data Structures in Python
- Module 6: Functions & Lambda Expressions
- Module 7: Python for Data Analysis (Basics)
Python Libraries (NumPy, Pandas, Visualization)
- Module 1: NumPy Fundamentals
- Module 2: Advanced NumPy Operations
- Module 3: Pandas Foundation
- Module 4: Data Manipulation with Pandas
- Module 5: Data Visualization (Matplotlib & Seaborn)
- Module 6: Exploratory Data Analysis (EDA)
- Module 7: Data Cleaning & Preprocessing
- Module 8: Advanced Analysis (Time Series Basics)
- Module 9: Performance Optimization
- Module 10: Projects & Capstone
Predictive Modeling using Machine Learning
- Module 1: Machine Learning Foundation
- Module 2: Feature Engineering & Data Preparation
- Module 3: Linear & Logistic Regression
- Module 4: K-Nearest Neighbors (KNN)
- Module 5: Support Vector Machine (SVM)
- Module 6: Decision Tree
- Module 7: Random Forest
- Module 8: Naive Bayes
Unsupervised Learning (Clustering)
- Module 1: Introduction to Unsupervised Learning
- Module 2: K-Means Clustering
- Module 3: Hierarchical Clustering
Deep Learning for Intelligent Systems
- Module 1: Introduction to Deep Learning
- Module 2: Artificial Neural Networks (ANN)
- Module 3: Convolutional Neural Networks (CNN)
- Module 4: Recurrent Neural Networks (RNN & LSTM)
- Module 5: Deep Learning Models & Applications
Natural Language Processing (NLP)
- Module 1: Introduction to NLP
- Module 2: Text Preprocessing Techniques
- Module 3: Feature Extraction in NLP
- Module 4: NLP Machine Learning Models
- Module 5: Advanced NLP (Deep Learning NLP)
- Module 6: NLP Model Evaluation & Applications
Basic Agentic AI for Data Analytics
- Module 1: AI for Data Analysts
- Module 2: Prompt Engineering for Analytics
- Module 3: AI for Reporting & Communication
- Module 4: Introduction to AI Agents
- Module 5: Mini Project: AI Data Analyst
Advanced Agentic AI Program
- Module 1: Basics of Agentic AI & Modern Data Systems
- Module 2: Agentic AI for Machine Learning Workflows
- Module 3: Agentic AI for Deep Learning Systems
- Module 4: AI for Data Analytics & Business Intelligence
- Module 5: Prompt Engineering for Data, ML & DL Systems
- Module 6: AI Agents for Automation & Model Pipelines
- Module 7: Multi-Modal AI Systems (Text, Data & Vision)
- Module 8: Capstone Project: Full Agentic AI System
Advanced Excel for Data Analytics
- Module 1: Excel Foundation & Interface
- Module 2: Formulas & Functions
- Module 3: Advanced Functions & Lookups
- Module 4: Data Cleaning & Preparation
- Module 5: Pivot Tables & Data Analysis
- Module 6: Data Visualization in Excel
- Module 7: Excel Dashboarding
- Module 8: Excel Projects & Capstone
Web Scraping & Data Automation
- Module 1: Web & Data Extraction Foundations
- Module 2: Data Extraction using Python
- Module 3: HTML Parsing & Data Extraction
- Module 4: Dynamic Web Scraping & Automation
- Module 5: Data Cleaning & Storage Pipelines
- Module 6: Advanced Scraping Techniques
- Module 7: Automation & Scheduling
- Module 8: Business Use Cases
- Module 9: End-to-End Data Pipeline
- Module 10: Industry Projects & Portfolio
2
ME
M
T
PB
P
JN
GC
T
C
GG
Tools Covered
Microsoft Excel
Data analysis
MMySQL
Database
Tableau
BI & visualization
Power BI
BI & visualization
PPython
Programming
JNJupyter Notebook
Programming
GCGoogle Colab
Model training
TTensorFlow
Deep learning
ChatGPT
AI tools
GGGoogle Gemini
AI tools
3
Tools Specialization
Data Analysis & Business Intelligence
- Advanced Excel (Lookups, Pivot Tables, Dashboards)
- MySQL (Joins, Window Functions, Stored Procedures)
- Tableau (LOD Expressions, Dashboards, Story Points)
- Power BI (Power Query, DAX, Power BI Service)
Python, Machine Learning & Deep Learning
- Python, NumPy, Pandas (Data Cleaning, EDA)
- Matplotlib, Seaborn (Visualization)
- Scikit-learn (Regression, Classification, Clustering)
- TensorFlow / PyTorch, Google Colab (ANN, CNN, RNN, LSTM)
- NLP (TF-IDF, Word2Vec, Transformers, BERT overview)
Agentic AI & Automation
- ChatGPT, Claude, Gemini (AI Reasoning & Prompting)
- LangChain, LlamaIndex (AI Agents & Workflows)
- OpenAI / Gemini APIs (Multimodal AI)
- BeautifulSoup, Selenium, Playwright, Postman (Web Scraping)
4
Building Your Career Foundation
Topics
- The 7 Cs of Effective Business Communication
- Communication English
- Grammar Mastery: Tenses, Adjectives, and Verbs
- Body Language & Modals
- Ace Your Business Presentations
- Effective Email Writing
- Telephonic Communication and Business Vocab
- Networking and Interpersonal Skills Development
Topics
- Resume Writing for Data Analyst Roles
- LinkedIn Profile Optimization
- Building a Project Portfolio on GitHub and Tableau Public
- Writing Clear Project Case Studies
- Personal Branding and Professional Networking
Topics
- HR Interview Preparation
- Mock Technical Interviews with Feedback
- Common Data Analyst Interview Questions
- Explaining Your Projects with Confidence
- Salary Discussion Basics
Topics
- SQL Interview Questions and Practice
- Excel and Power BI Practical Tests
- Business Case Studies and Problem Solving
- Statistics and Python Revision
- Take-home Assignment Practice
Ready to Start Your
Career in Data Science?
Projects You'll Build
Recruiters don't just want to hear what you learned. They want to see something you made. Every section of this course ends with a project on real datasets: SQL and Excel business analysis, Tableau and Power BI dashboards, an end-to-end Python data analysis, machine learning models for problems like churn and sales prediction, customer segmentation, an e-commerce data scraper, and a final agentic AI system that cleans data, builds models and writes the report. By the end, you'll have a portfolio that shows exactly what you can do.
Uber
Zomato
Amazon
Netflix
Walmart
Projects use public or sample datasets. Company names are shown for context only and don't imply a partnership.
Fingertips Certificate of Completion
When you complete the program and pass the final evaluation, you receive a certificate of completion from Fingertips Data Intelligence Solutions Pvt. Ltd. It shows employers you've worked through the full syllabus and its projects, not just watched videos.
Once you complete the course, you can:
Add your certificate to the Licenses & Certifications section of your LinkedIn profile, and share your capstone projects alongside it.
Help With the Job Hunt, Not Just the Course
Career support starts once you've completed 80% of the course and continues for four months after you finish.
Job Assistance
Interview Preparation
Profile Building
After 80% of the course completion
Why Learners Choose Fingertips
Learn by Doing
Classes are built around activities and exercises, not long lectures, so the skills employers look for actually stick.
Real-World Projects
Every section ends with a project on real datasets, the same kind of messy data you'll meet in your first job.
Doubt-Solving Sessions
Regular doubt-clearing sessions, with extra support for learners who haven't coded before.
Dedicated Program Manager
One person who tracks your progress, checks in if you start slipping, and helps you catch up when you miss a class.
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Learn by Doing
Classes are built around activities and exercises, not long lectures, so the skills employers look for actually stick.
Real-World Projects
Every section ends with a project on real datasets, the same kind of messy data you'll meet in your first job.
Doubt-Solving Sessions
Regular doubt-clearing sessions, with extra support for learners who haven't coded before.
Dedicated Program Manager
One person who tracks your progress, checks in if you start slipping, and helps you catch up when you miss a class.
View All
Hear It From Our Learners
Some of our learners came from commerce. Others studied physics or mechanical engineering. Here's what they said about the program.
Where Our Alumni Work
Frequently Asked Questions
Yes. The course starts with statistics, Excel and SQL, and only introduces Python once you're comfortable. Many of our learners had never written code before they joined.
Eight months of live, instructor-led training, with a project at the end of every section.
Yes. Career support starts once you've completed 80% of the course and continues after you finish. It includes resume and LinkedIn reviews, mock technical and HR interviews, access to our job portal, and referrals to companies in our hiring network.
No, and you should be wary of anyone who does. We prepare you well and help you get in front of employers. Your projects and how you perform in interviews do the rest.
Yes. There are two AI sections. The first covers using ChatGPT and Gemini for analysis, prompt engineering and an introduction to AI agents. The advanced section covers AI agents for machine learning and deep learning workflows, multi-agent systems, LangChain, LlamaIndex and multimodal AI, ending with a capstone where you build your own AI data scientist agent.
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