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Master Data Science & Analytics with Data Science & Gen AI

The only Data Science program that covers it all — Python, Machine Learning, NLP, Big Data, Cloud Computing & Generative AI — in one structured 25-module curriculum. Built by industry experts, backed by hands-on projects and career support.

4.3
New Batch
₹20,000 /month
Save 33% ₹30,000

Batch starting
31 Mar 2026
Batch size
30
3 Months -22 Modules
1:1 Mentorship
Real-World Case Studies
Industry-Aligned curriculum

Mode

Online/Offline

Duration

3 Months

Schedule

Weekdays 3-4 PM

Advance Your Career with SkillSprintTech’s Data Science & Gen AI Curriculum in Pune

Are you looking to future-proof your career? Our Data Science and Gen AI Curriculum is strategically designed keeping in mind to prepare students as well as professionals for high-growth opportunities in data-driven industries of today’s time. No matter if you are a fresh graduate or an IT professional aiming to upskill and get access to better opportunities, SkillSprintTech is the right place to initiate your journey.

Why Should You Choose SkillSprintTech?

We are not just another data science training institute in Pune. At SkillSprintTech, our expert team puts in special efforts to blend practical learning with industry-ready tools to give you a real-world edge that makes all the difference. Our program is not merely about theories — it is about hands-on projects, simulative sessions, various live tools, & mentorship from data science practitioners.


What Makes Our Data Science Course in Pune Stand Out?

  • Industry-Aligned Curriculum: We offer the best data science course in Pune, combining the various core modules in Python, SQL, & Machine Learning with several advanced topics like NLP, Gen AI, & Deep Learning.
  • Hands-On Training: Learn by doing. Work on capstone projects, real-time datasets, & industry use cases to build a practical skillset.
  • Multiple Modes of Learning: Prefer flexibility? We also provide data science online training in Pune. Get the right access to the same quality instruction, resources, as well as full online support.

Effective Placement Support That Works

We thoroughly understand your end goal is not just learning—it is landing a job. That is why our program is also well-known as the best data science course in Pune with placement. We offer resume-building sessions, mock interviews, & direct placement assistance with top companies.


Affordable Course Fees

If you are concerned about data science course fees in Pune, do not worry. Our pricing is completely transparent & absolutely affordable with the option of paying in EMIs as well. We also provide early-bird discounts & group offers.


Certified & Recognized Training

Earn a data science certification course in Pune that is well-recognized by industry recruiters. Our certificate validates your skill level & helps you stand out in a crowded job market.


What Will You Learn in the Course?

  • Python Programming for Data Analysis
  • Statistics & Data Wrangling
  • SQL & Databases
  • Machine Learning Algorithms
  • Generative AI & Large Language Models (LLMs)
  • Power BI / Tableau for Data Visualization
  • Model Deployment & MLOps

Curriculum Highlights

What you will learn in the course

Data Science Basics

Data Science Basics

Advanced Data Science

Advanced Data Science

SQL Basics

SQL Basics

Advanced SQL

Advanced SQL

Course Curriculum

The only Data Science program that covers it all — Python, Machine Learning, NLP, Big Data, Cloud Computing & Generative AI — in one structured 25-module curriculum. Built by industry experts, backed by hands-on projects and career support.

01
Data Manipulation with Pandas
  • Using the Pandas library for data manipulation, cleaning, and analysis
  • Working with DataFrames, handling missing data, and performing data aggregation
  • Learning Outcome: Effectively manipulate and analyze data using Pandas
  • Real-World Application: Analyze sales data to identify top-performing products and customer segments
02
Data Visualization with Matplotlib and Seaborn
  • Creating informative charts and graphs using Matplotlib and Seaborn
  • Customizing plots, exploring different visualization techniques, and communicating data insights
  • Learning Outcome: Visualize data effectively to identify trends and patterns
  • Real-World Application: Create visualizations to present survey results to stakeholders
03
Statistical Analysis
  • Fundamentals of statistical analysis including descriptive statistics, probability distributions, hypothesis testing, and regression analysis
  • Learning Outcome: Apply statistical methods to analyze data and draw meaningful conclusions
  • Real-World Application: Perform A/B testing to determine the effectiveness of different marketing campaigns
04
SQL for Data Science
  • Introduction to SQL for querying and manipulating data in relational databases
  • Writing SQL queries to extract, filter, and aggregate data for analysis
  • Learning Outcome: Retrieve and manipulate data from databases using SQL
  • Real-World Application: Extract customer transaction data from a database for fraud detection analysis
05
Machine Learning Fundamentals
  • Overview of machine learning concepts including supervised, unsupervised, and reinforcement learning
  • Introduction to common machine learning algorithms and their applications
  • Learning Outcome: Understand the basics of machine learning and its different paradigms
  • Real-World Application: Identify use cases for machine learning in various industries
06
Linear Regression
  • Building and evaluating linear regression models for predicting continuous variables
  • Understanding model assumptions, interpreting coefficients, and assessing model performance
  • Learning Outcome: Build and interpret linear regression models
  • Real-World Application: Predict housing prices based on features like size, location, and amenities
07
Logistic Regression
  • Building and evaluating logistic regression models for binary classification problems
  • Understanding odds ratios, interpreting coefficients, and assessing model performance
  • Learning Outcome: Build and interpret logistic regression models
  • Real-World Application: Predict customer churn based on behavior and demographics
08
Decision Trees
  • Introduction to decision trees including building, visualizing, and interpreting models
  • Understanding concepts like entropy, information gain, and pruning
  • Learning Outcome: Build and interpret decision tree models
  • Real-World Application: Build a decision tree to classify loan applications as high-risk or low-risk
09
Random Forests
  • Ensemble learning with random forests
  • Building and tuning random forest models for classification and regression tasks
  • Learning Outcome: Build and optimize random forest models
  • Real-World Application: Predict customer purchase behavior using a random forest model
10
Support Vector Machines (SVM)
  • Introduction to SVM for classification and regression tasks
  • Understanding kernels, margins, and support vectors
  • Learning Outcome: Understand and apply Support Vector Machines
  • Real-World Application: Classify images using an SVM model
11
K-Means Clustering
  • Unsupervised learning with K-Means clustering
  • Understanding the K-Means algorithm, choosing optimal number of clusters, and interpreting results
  • Learning Outcome: Apply K-Means clustering to segment data
  • Real-World Application: Segment customers based on their purchasing patterns
12
Dimensionality Reduction with PCA
  • Reducing the dimensionality of data using Principal Component Analysis (PCA)
  • Understanding the PCA algorithm and interpreting principal components
  • Learning Outcome: Reduce the dimensionality of data using PCA
  • Real-World Application: Reduce features in an image dataset for faster processing
13
Model Evaluation and Selection
  • Metrics for evaluating ML models (accuracy, precision, recall, F1-score, AUC)
  • Techniques for model selection including cross-validation and hyperparameter tuning
  • Learning Outcome: Evaluate and select the best machine learning model for a given task
  • Real-World Application: Compare performance of different models for predicting customer churn
14
Natural Language Processing (NLP) Basics
  • Introduction to Natural Language Processing
  • Text preprocessing techniques, sentiment analysis, and topic modeling
  • Learning Outcome: Understand the basics of NLP and its applications
  • Real-World Application: Perform sentiment analysis on customer reviews
15
Time Series Analysis
  • Analyzing time series data, decomposition, forecasting techniques (ARIMA, Exponential Smoothing)
  • Learning Outcome: Analyze and forecast time series data
  • Real-World Application: Forecast sales for the next quarter based on historical data
16
Big Data Technologies
  • Introduction to Big Data technologies like Hadoop and Spark
  • Understanding the challenges of big data and how these technologies address them
  • Learning Outcome: Understand the basics of Big Data technologies
  • Real-World Application: Process large datasets using Spark
17
Cloud Computing for Data Science
  • Using cloud platforms (AWS, Azure, GCP) for data science tasks
  • Deploying machine learning models on the cloud
  • Learning Outcome: Deploy data science solutions on the cloud
  • Real-World Application: Deploy a machine learning model on AWS SageMaker
18
Data Ethics and Privacy
  • Ethical considerations in data science
  • Data privacy regulations (GDPR, CCPA) and bias in machine learning models
  • Learning Outcome: Understand ethical considerations in data science
  • Real-World Application: Identify and mitigate bias in a machine learning model
19
Data Storytelling and Communication
  • Communicating data insights effectively
  • Creating data visualizations for presentations and writing data-driven reports
  • Learning Outcome: Communicate data insights effectively
  • Real-World Application: Present data findings to a non-technical audience
20
Generative AI & Prompt Engineering
  • Fundamentals of Generative AI and Large Language Models like ChatGPT and Gemini
  • Prompt engineering techniques, hands-on usage of OpenAI APIs, ethical considerations (hallucinations, bias)
  • Learning Outcome: Understand and apply Gen-AI tools to real tasks with confidence
  • Real-World Application: Build an AI-powered Resume Generator, FAQ Bot, or Content Assistant
21
Building a Data Science Portfolio & Job Search Strategies
  • Creating a portfolio of data science projects to showcase skills
  • Building a personal website or GitHub repository
  • Resume writing, interview preparation, and networking
  • Learning Outcome: Prepare for a data science job search
  • Real-World Application: Practice answering common data science interview questions
22
Capstone Project
  • A comprehensive data science project integrating all skills and knowledge from the course
  • Learning Outcome: Apply data science skills to solve a real-world problem
  • Real-World Application: Complete a capstone project to showcase to potential employers
4.9 / 5 student rating
Certificate of completion
1-on-1 mentorship
Industry-grade live projects
30 LPA highest package
35,400+ students trained

Projects You'll Build

Practical Learning in Action

RAG-based PDF Chatbot

RAG-based PDF Chatbot

Master Data Science & Analytics with Data Science & Gen AI

AI Customer Support Ticket Resolver

AI Customer Support Ticket Resolver

Master Data Science & Analytics with Data Science & Gen AI

Intelligent Log Analyzer with GenAI

Intelligent Log Analyzer with GenAI

Master Data Science & Analytics with Data Science & Gen AI

Customer Churn Prediction Model

Customer Churn Prediction Model

Master Data Science & Analytics with Data Science & Gen AI

Retail Sales Forecasting System

Retail Sales Forecasting System

Master Data Science & Analytics with Data Science & Gen AI

Recommendation Engine

Recommendation Engine (Netflix/Amazon Use Case)

Master Data Science & Analytics with Data Science & Gen AI

What our Students Say

Real people. Real results.

  • I recently completed the Master Data Science & Analytics with Data Science & Gen AI course, and it was an excellent learning experience. The curriculum covered both data science fundamentals and modern generative AI tools with practical projects. It really helped me improve my analytical and AI skills. Highly recommended for anyone looking to build a career in data and AI.

    Aman mishra

    Student

  • "I recently completed the Master Data Science & Analytics with Data Science & Gen AI course, and it was an excellent learning experience. The curriculum perfectly balanced core data science fundamentals with modern generative AI tools like LLMs and Prompt Engineering. Working on practical projects really helped me improve my analytical skills and understand how AI is transforming the industry. Highly recommended for anyone looking to build a future-ready career in data and AI."

    Aryan Sharma

    Student

  • "Joining this Master Data Science program was the best decision for my career. The course content is very deep, covering everything from Python and SQL to advanced Predictive Analytics and Generative AI models. What I liked most was the hands-on approach to real-world datasets, which made complex AI concepts very easy to grasp. If you want to master both traditional analytics and the latest AI technologies, this is the perfect course for you."

    Rohan Verma

    Student

  • "This course provides a complete roadmap for becoming a modern Data Scientist. I was impressed by how the modules transition from basic statistics to building sophisticated Gen AI applications. The practical project development phase helped me create a strong portfolio that showcases both my data-driven insights and AI implementation skills. It is an outstanding program for anyone serious about staying ahead in the rapidly evolving tech landscape."

    Rahul Gariya

    Student

Frequently Asked Questions

You will learn data analysis, machine learning basics, and how to use Generative AI tools for real-world data projects.
Basic knowledge of Python is helpful, but the course also starts with fundamentals for beginners.
The course includes Python, data analysis libraries, machine learning concepts, and modern Generative AI tools.
Yes, the course provides hands-on projects and case studies to practice real data science workflows.
It is suitable for students, professionals, and anyone interested in building a career in data science and AI.
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