PG Program in Data Science

Master in-demand Data Science skills with guidance from top industry experts. Get hands-on training, real-world projects, and 100% Placement Assurance to kickstart your career in data science.

90% Placement Success

8 Months

★★★★★ 4.7 (1,500+ Ratings) | 2,450+ Learners

8 Months

Program Duration

Online Live

Learning Format

100% Assurance

Placement Assistance

Join the Most In-Demand PG Data Science Program

Master the real-world application of Data Science and Artificial Intelligence to build smart, scalable models that drive business success. This program is designed for fresh graduates and working professionals aiming to launch a high-impact career in the AI and Data Science space.

Gain hands-on experience with AI tools, machine learning algorithms, and deep learning techniques while solving real industry problems. Prepare to step confidently into roles like Data Scientist, AI Engineer, ML Engineer, and more—all backed by practical project work and full placement support.

Course Curriculum

Comprehensive Course Curriculum

Our industry-aligned curriculum is designed by experts to help you master the most in-demand data science skills and prepare you for a successful career.

Data Foundations & Business Tools

This foundational module sets the tone for your entire Data Science journey. The goal is to ensure that every learner is confident in working with raw data—structuring it, cleaning it, transforming it, and deriving meaningful insights. You’ll begin with spreadsheet tools like Excel and Google Sheets, progress into structured querying with SQL, and learn to work with relational databases. From there, you’ll explore modern data visualization tools like Tableau and Power BI, which are critical for communicating data-driven findings.

What you'll learn:

Data Analysis & Excel
SQL & Database Management
Visualization & Data Wrangling

Excel

Learn Excel including formulas, pivot tables, charts, statistical functions, dashboards, and What-if analysis, developing practical skills for analyzing, visualizing, and presenting data efficiently, including collaborative work in Google Sheets. Read More

Opportunities it opens

SQL

Gain a strong understanding of databases, covering CRUD operations, joins, aggregations, filtering, subqueries, window functions, and CTEs, learning to structure, query, and manipulate data effectively for real-world business and analytics scenarios. Read More

Opportunities it opens

Databases

Understand relational database concepts including MySQL/PostgreSQL, indexing, and normalization techniques, developing expertise in managing data integrity, optimizing performance, and designing scalable database solutions. Read More

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Power BI/Tableau

Master Power BI and Tableau, connecting data sources, creating calculated fields, dashboards, KPIs, and using LOD expressions to craft visually compelling reports that communicate actionable business insights. Read More

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Data Wrangling Basics

Perform data wrangling including handling missing values, reshaping data, column transformations, and type conversions to ensure datasets are clean, structured, and ready for downstream analysis, reporting, and modeling. Read More

Opportunities it opens

Programming for Data Science

Python is the most widely used programming language in the data world—and for good reason. In this module, you’ll gain a strong grip on Python fundamentals along with practical exposure to key libraries used in data science.

You’ll start with the basics—data types, loops, functions—and move into advanced concepts like OOP, list comprehensions, and file handling. From there, we’ll explore data-focused libraries like NumPy and Pandas for efficient computation and data manipulation, and data visualization with Matplotlib and Seaborn. You’ll also gain hands-on experience with Git and GitHub for version control—an essential skill for collaborative data science projects.

What you'll learn:

Python Programming & Advanced Concepts
Data Manipulation with NumPy & Pandas
Visualization & Version Control

Python Basics

Build a foundation in Python, learning variables, loops, conditionals, functions, and core data structures, such as lists, tuples, dictionaries, and sets, to write modular, maintainable code for analytical and automation tasks. Read More

Opportunities it opens

Advanced Python

Advance Python skills with object-oriented programming, file I/O, error handling, and comprehensions, developing robust solutions by applying best practices in coding and automation workflows. Read More

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NumPy & Pandas

Perform numerical and data analysis using NumPy arrays and Pandas DataFrames, including filtering, merging, grouping, and reshaping to manipulate large datasets efficiently and prepare them for analytics or machine learning projects. Read More

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Visualization Librarie

Visualize data using Matplotlib and Seaborn, including plots, axes, subplots, heatmaps, pairplots, and boxplots, developing compelling visualizations to communicate trends, distributions, and patterns effectively. Read More

Opportunities it opens

Version Control

Work with Git and GitHub, creating repositories, branching, pull requests, and collaborating on version-controlled projects, gaining practical experience in team workflows and best practices for code management and collaboration. Read More

Opportunities it opens

Statistics, Probability & EDA

This module focuses on the statistical thinking behind data science. It’s where you’ll learn to analyze and interpret data distributions, draw inferences, and test hypotheses. You’ll also conduct in-depth exploratory data analysis (EDA) to uncover trends, patterns, and insights hidden in the data.

Statistical literacy is key to becoming a successful data scientist. You’ll learn about descriptive and inferential statistics, common probability distributions, and how to validate assumptions with hypothesis testing. The module concludes with hands-on EDA using Python, where you’ll apply visual and statistical techniques to real datasets.

What you'll learn:

Statistics & Probability Concepts
Hypothesis Testing & Inference
Exploratory Data Analysis (EDA)

Descriptive Statistics

Apply descriptive statistics to summarize data distributions, calculate central tendency, and understand variability, building a foundation for interpreting data and making informed analytical decisions. Read More

Opportunities it opens

Inferential Statistics

Understand inferential statistics, including hypothesis testing, confidence intervals, and probability distributions, learning to draw conclusions about populations from sample data using robust statistical methods. Read More

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Probability

Build probability knowledge including discrete and continuous distributions, conditional probability, and Bayes theorem, developing skills to quantify uncertainty and make informed predictions in data-driven applications. Read More

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Hypothesis Testing

Perform exploratory data analysis using Python, NumPy, and Pandas, including data cleaning and preprocessing techniques, identifying patterns, outliers, and relationships in datasets to guide decision-making and modeling. Read More

Opportunities it opens

Exploratory Data Analysis

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Machine Learning & Model Building

In this module, you’ll begin building real predictive models. You’ll learn the theory and intuition behind key algorithms, and more importantly, how to implement them from scratch using Python libraries like Scikit-learn.

You’ll explore supervised and unsupervised learning, develop your first classification and regression models, and understand how to evaluate performance. The module also includes tuning methods like grid search and cross-validation and ensemble learning techniques like bagging and boosting.

What you'll learn:

Supervised & Unsupervised Learning
Model Evaluation & Tuning
Ensemble Techniques

Supervised Learning

Learn supervised learning techniques, including regression and classification, to model relationships and predict outcomes, applying practical machine learning algorithms to solve real-world business and analytical problems. Read More

Opportunities it opens

Unsupervised Learning

Explore unsupervised learning methods such as clustering and dimensionality reduction for pattern discovery, identifying hidden structures in data to support segmentation, anomaly detection, and insights generation. Read More

Opportunities it opens

Model Evaluation

Evaluate and tune models using metrics such as accuracy, precision, recall, and F1 score, refining algorithms to improve prediction quality and ensure robust, reliable machine learning solutions. Read More

Opportunities it opens

Model Tuning

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Ensemble Techniques

Apply ensemble techniques like Random Forest, AdaBoost, and XGBoost to improve predictive performance, learning model interpretability, aggregation methods, and strategies to reduce bias and variance in predictions. Read More

Opportunities it opens

AI, Deep Learning & NLP

This module takes you deeper into artificial intelligence, focusing on neural networks, deep learning, and natural language processing (NLP). You’ll explore real-world use cases such as image classification, sentiment analysis, and chatbot development.

Starting from the basics of neural networks and deep learning frameworks like TensorFlow and Keras, you’ll build up to advanced concepts like RNNs, transformers, and BERT for NLP tasks. You’ll also cover time series forecasting and recommendation systems used widely in tech companies.

What you'll learn:

Neural Networks & NLP
Transformers & Time Series Forecasting
Recommendation Systems

Neural Networks

Understand neural networks and their application to predictive modeling and classification tasks, building and training feedforward, convolutional, and recurrent networks for structured, sequential, and image data. Read More

Opportunities it opens

Natural Language Processing

Learn natural language processing techniques including tokenization, vectorization, sentiment analysis, and text summarization, developing AI models capable of extracting insights and understanding textual data for business applications. Read More

Opportunities it opens

Transformers

Work with transformer architectures like BERT and GPT, applying them to NLP and time-series tasks, understanding large language models, embeddings, and prompt engineering for advanced AI applications. Read More

Opportunities it opens

Time Series Forecasting

Perform time-series forecasting using ARIMA, exponential smoothing, and Facebook Prophet techniques, predicting future trends and values for business metrics using historical data and statistical models. Read More

Opportunities it opens

Recommendation Systems

Build recommendation systems using collaborative filtering, content-based, and hybrid approaches, developing personalized solutions for product suggestions, content recommendations, and customer engagement. Read More

Opportunities it opens

Deployment, Projects & Career Readiness

In the final module, you’ll bring everything together—deploying models, building machine learning pipelines, and presenting your capstone project. You’ll also receive career support to help you stand out in the job market.

You’ll learn how to wrap your ML models into APIs using Flask, host them using platforms like Streamlit or AWS, and understand the basics of MLOps. Finally, we’ll help you craft your resume, build a strong LinkedIn profile, and prepare with mock interviews for data science roles.

What you'll learn:

Model Deployment & MLOps
Capstone Projects
Career Preparation & Mock Interviews

Deployment

Deploy machine learning and AI models using Flask APIs, Streamlit dashboards, and cloud platforms like AWS, learning to productionize models, create interactive applications, and ensure reliable operation. Read More

Opportunities it opens

MLOps & Pipelines

Understand MLOps pipelines including batch vs. real-time processing, orchestration, and model monitoring, developing automated, maintainable workflows for deploying, tracking, and updating machine learning solutions. Read More

Opportunities it opens

Capstone Projects

Work on capstone projects applying AI/ML techniques to BFSI, healthcare, retail, and e-commerce scenarios, demonstrating practical problem-solving skills and end-to-end solution delivery in real-world applications. Read More

Opportunities it opens

Career Preparation

Prepare for careers with resume and LinkedIn optimization, mock interviews, and placement support, learning to effectively showcase skills, projects, and achievements to attract recruiters and hiring partners. Read More

Opportunities it opens