Only 60 seats left! Don't miss out

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

Download Brochure

New Batch

₹20,000 /month

Save 33% ₹30,000

Batch starting

31 Mar 2026

Batch size

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

Advanced Data Science

SQL Basics

Advanced SQL

Course Curriculum

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 •