Data Science & Machine Learning

MASTER'S IN DATA SCIENCE WITH

MACHINE LEARNING

Set a new high in your career by mastering in Data Science with Generative AI

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1:1 Guided Mentorship

46% Average Raise

MASTER'S IN DATA SCIENCE WITH
MACHINE LEARNING

Data science with generative Al leverages machine learning models to analyze and synthesi new data, making it possible to generate realistic images, text, and other content. This combination enables advancements in areas like personalization, creativity, and automation Generative Al models, such as GANs and transformers, help solve complex problems by learning patterns from vast datasets and generating innovative outputs.

Unlock a World of Opportunities in

1.5 Million Job Opportunities in India

By 2025, the indian job market is expected. to see over 1.5 million openings in Data Science & Generative Al, driven by the rapid adoption of Al across industries.

Future-Proof Your Career

With 75% of Indian companies investing in Al, mastering Data Science & Generative Al ensures job security and career growth in sectors like IT, BFSI, and e-commerce.

A 1.4 Lakh Crore Industry in India

The Indian Al market is projected to grow. at an incredible CAGR of 25%, reaching 1.4 lakh crore by 2027, making it a major hub for Data Science & Al innovation.

Transforming Indian Industries

From personalized healthcare and fintech innovations to Al-driven governance and automation, Generative Al is reshaping industries, making India a global Al leader.

Preferred Qualification for High-Paving Roles

A master's degree or certification in Data Science & Al boosts career prospects, with top Indian professionals earning 20-40 LPA, while freshers start at 6-10 LPA.

Unprecedented Demand for Al Talent

India is home to the third-largest Al talent pool, with a 50% surge in Al-related hiring as companies seek skilled Data Scientists & Al specialists to drive digital transformation.

Our Credentials

23 Years Old Educational institute

24+ Project Presentations

N+ IITians Exposures

66+ City’s students

40,000 Satisfied students

46% Average Raise

Our Courses

Data Science and Machine Learning are essential components of modern technology and decision-making. Data Science involves extracting meaningful insights from structured and unstructured data, while Machine Learning enables systems to learn patterns and make intelligent predictions. Professionals in this field use statistical techniques, algorithms, and AI models to analyze complex datasets, optimize processes, and drive innovation across various industries, from healthcare to finance.

Classes Format

online|offline

8 Months

Live Session

23 Years Old

Institution

Keyfeatures

Expert Instructions-

Hours of live classes led by renowned IITians.

Master Classes-

Exclusive faculty-led sessions for in depth learning.

Personalized Mentorship-

One-on-one guidance from industry experts.

Dedicated Assistance

24*7 support to ensure your success.

Interview Preparation-

1:1 Mock Interview to boods your confidence.

Placement assistance -

100% Placement support upon course completion.

Career Guidance -

Free Career Counseling to help you navigate your path.

Career Enhancement -

Receive Resume preparation and LinkedIn profile reviews.

Immersive Experience

2 Days Campus immersion at location, place for hands-on Check.

Flexible Schedule-

Weekend batches available for convenient learning.

Dedicated Support -

Access our learning management team for personalized guidance

ProgramPedagogy

Expert-Led Training by Industry Leaders

Learn from top Al & Data Science professio nals with real-world experience in Big Data, Machine Learning, Deep Learning, and Generative Al. Get insights from leaders. at top tech firms and research institutions.

Al-Driven Simulation-Based Learning

Tackle real-world business challenges using Al-powered simulations, applying data-driven decision-making, predictive analytics, and Generative Al modeling.

Dedicated Personalized Support

A 24/7 learning management team ensures timely assistance in understanding complex Al and Data Science concepts, providing one -on-one mentorship when needed..

Al Hackathons & Innovation Challenges

Compete in industry-led Al & Data Science hackathons, where you'll build generative Al models, optimize deep learning networks, and develop real-world Al solutions from scratch.

Flexible & Adaptive Learning

Access self-paced, Al-driven interactive courses tailored to your learning speed. Leverage cutting-edge tools and real-world datasets. for hands-on learning at your convenience.

Collaborative Group Learning & Peer Networking

Engage with a global community of data sci-entists and Al engineers to collaborate on projects, exchange ideas, and build connections with professionals in top tech firms

Industry-Ready Practical Experience

Work on live industry projects, Kaggle com-petitions, and real-world Al applications, gaining expertise in data engineering, model deployment, and Al automation.

Personalized Mentoring & Career Acceleration

Get exclusive career guidance, Al interview preparation, and resume-building support from Al experts and hiring managers at leading tech companies.

Who are the eligible Aspirants for the Course?

The one who is qualified from any state Board
The one who may have/may not have any exposure to computers
IT Aspirants
The one who has the interest in Data Science & Machine Learing Programme.

Enrollment Procedure

We are excited to welcome you to our community! The admission process is designed to be simple and transparent. Here’s a step-by-step guide to help you through it:

Counselling

Unlock your future today—let's find the right path for your academic and career success!

Application Submission

Begin by filling out our online application form. Ensure all details are accurate and submit necessary documents (e.g., transcripts, ID proof).

Entrance Test/Interview

Depending on the program, you may be required to take an entrance test or attend an interview.

Offer of Admission

Successful candidates will receive an offer letter with further instructions on confirming their admission.

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.

However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a Data Scientist.

And how can you do that?

Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)

Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture.

The Solution

Data science is a multidisciplinary field. It encompasses a wide range of topics.

Understanding of the data science field and the type of analysis carried out
Excel
SQL
Power BI
Tableau
Mathematics
Statistics
Python
Applying advanced statistical techniques in Python
Data Visualization
Machine Learning
Deep Learning

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don't acquire these skills in the right order.

For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.

So, in an effort to create the most effective, time-efficient, and structured data science training, we created The Mastering Data Science Course 2025.

We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs.

The Skills

1. Intro to Data and Data Science

Big data, business intelligence, business analytics, machine learning, and artificial intelligence. We know these buzzwords belong to the field of data science, but what do they all mean?

Why learn it?

As a candidate data scientist, you must understand the ins and outs of each of these areas and recognize the appropriate approach to solving a problem. This 'Intro to Data and Data Science' section will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.

2. Mathematics

Learning the tools is the first step to doing data science. You must first see the big picture, then examine the parts in detail.

We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.

Why learn it?

Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.

3. Statistics

You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.

Why learn it?

This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.

4. Python

Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games, and data science are among many of its capabilities.

That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualization. Where Python really shines, however, is when it deals with machine and deep learning.

Why learn it?

When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as Scikit-learn, TensorFlow, etc., Python is a must-have programming language.

5. Tableau

Data scientists don't just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data's story in a way they will understand.

That's where Tableau comes in and we will help you become an expert story teller usin visualisation software in business intelligence and data science

Why learn it?

A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.

6. Advanced Statistics

Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning.

However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.

Why learn it?

Data science is all about predictive modelling and you can become an expert in these methods through this 'advance statistics' section.

7. Machine Learning

The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst.

This section covers all common machine learning techniques and deep learning methods with TensorFlow

Why learn it?

Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.

What You Get

A data science training program
All the knowledge to get hired as a data scientist
A community of data science learners A certificate of completion
Solve real-life business cases that will get you the job

Who this course is for:

You should take this course if you want to become a Data Scientist or if you want to learn about the field
This course is for you if you want a great career
The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

Module 1: Introduction to Data Science

Overview of Data Science
Big Data, Business Intelligence, Business Analytics
Machine Learning and Artificial Intelligence
Data Science Process and Lifecycle

Module 2: Statistics and Probability

Descriptive Statistics
Inferential Statistics
Probability Theory and Distributions
Hypothesis Testing and Confidence Intervals

Module 3: Excel for Data Analysis:

Excel remains a fundamental tool for data analysis, and in this course, you'll harness its power.
Explore common features used by data analysts such as formulas, pivot tables, data cleaning, and conditional formatting to efficiently analyse and present data.

Module 4: Python Programming

Introduction to Python
Data Structures and Algorithms
Data Manipulation with Pandas
Data Visualization with Matplotlib and Seaborn Advanced Python for Data Science

Module 5: SQL Fundamentals:

Dive into the world of structured query language (SQL) and learn how to write powerful queries to extract and manipulate data from databases.
From basic SELECT statements to advanced JOINS, sub queries and aggregate functions, you'll gain a comprehensive understanding of SQL.

Module 6: Data Visualization with Tableau

Introduction to Data Visualization.
Creating Visualizations in Tableau
Dashboards and Storytelling
Case Studies and Practical Applications

Module 7: Power BI Essentials:

Explore the capabilities of Power BI, Microsoft's powerful business intelligence tool.
Discover how to import, transform, and model data from various sources, create interactive visualizations, and design compelling reports and dashboards

Module 8: Advanced Statistical Techniques

Regression Analysis (Linear and Logistic) Clustering (K-means, Hierarchical)
Factor Analysis and Principal Component
Analysis (PCA)
Time Series Analysis

Module 9: Mathematics for Data Science

Calculus: Derivatives, Integrals, and their Applications
Linear Algebra: Vectors, Matrices, and Eigenvalues
Mathematical Foundations for Machine Learning

Module 10: Machine Learning

Supervised Learning: Algorithms and Techniques
Decision Trees, Random Forests, Gradient Boosting
Unsupervised Learning: Clustering and Dimensionality Reduction
Model Evaluation and Validation
Ensemble Methods

Module 11: Deep Learning

Introduction to Neural Networks
Deep Learning Frameworks (TensorFlow, Keras)
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)

Module 12: Data Engineering

Data Warehousing Concepts
ETL (Extract, Transform, Load) Processes
Building Data Pipelines
Real-time Data Processing

Module 13: Big Data Technologies

Introduction to Big Data and Hadoop Ecosystem
Apache Spark for Large Scale Data Processing
NoSQL Databases (MongoDB, Cassandra)

Module 14: Capstone Project

Defining a Data Science Project
Data Collection, Cleaning, and Preparation
Model Building and Evaluation
Project Presentation and Deployment

Module 15: Soft Skills and Career Development

Effective Communication for Data Scientists
Resume and Portfolio Building
Interview Preparation and Mock Interviews
Industry Case Studies and Best Practices

What You Get:

A comprehensive data science training program
All the knowledge needed to get hired as a data scientist
A community of data science learners
A certificate of completion

This syllabus is designed to provide a well-rounded education in data science, equipping learners with the skills needed to excel in the industry. The structured approach ensures that it prepares students for real-world data science challenges.

MachineLearning

Python:-

Introduction of python
Variables
Mutable and Immutable Objects.
Operators
Conditions

Data Structures:-

List, Tuples and Set
Dictionary

NumPy

Arrays
Matrix
Form Function
nDimensional Array
Random Data Array generation
Rolling Function
Sorting, Searching, Counting Function
Copies & Views
Numpy Matrix-Library

Functions:-

Functions
Generator Functions
Lambda Functions.
Map, Reduce and Filter Function

Visualization-

MatPlotLib
Seahorn

Engineering Mathematics

Probability.
Linear Algebra
Statistics

Machine Learning-

Linear and Logistic Regression.
Decision Tree & Support Vectore
Machine
Native Bayes and Ensemble Techniques and its Types
K-Nearest Neighbors
Clustering Techniques

Pandas:-

Read Data set, Retrieve Form Dataset, Basic Data Set Function
Specific Dataset retrieval Functions and Specific row and column selection, Slicing Techniques, loc and iLoc Functions
Joins in Data Frames
Null Value Handling
Concatenation Operator
Rolling and Window function

Machine Learning:-

Introduction to Machine Learning.
Al vs ML vs DL vs DS
Train, Test and Validation
Bias, Variance, Overfitting and Underfitting
Handling missing Values.
Handling Imbalanced Dataset.
SMOTE
Handling Outliers.
Data Interpolation.
Feature Selection.
PCA(Principal Component analysis.).
Data Encoding- Uses and It's types.
Exploratory Data Analysis
Covariance and Correlation
Covariance and Correlation with Python.

Machine Learning Projects:-

Credit Card Fraud Detection.
Cement Strength Prediction.
Phishing Classifier.
Diamond Price Prediction

SYLLABUS

DATA SCIENCE

PYTHON FUNDAMENTALS - duration 48h

Python Fundamentals

Discovery of the different variables, lists, and Tuples
Initiation to the concept of a programming loop and its different types
Introduction to functions and their documentation
Creation of classes and use of modules

NumPy

Creation and manipulation of a NumPy Array
Presentation of matrix operations and management of a NumPy Array
Creation of a statistical indicator and operations on a NumPy Array

Pandas

Introduction to Pandas library
Loading and first exploration of a dataset
Introduction to Data Cleaning
Introduction to Data Processing

Skills acquired at the end

Reading and understanding a Python code, the most used language in Data Science
Handling and managing data tables
Querying, managing, ordering, and modifying a dataset with Python
Mastering the software libraries NumPy and Pandas

DATA VISUALIZATIONS - Duration 33h

Matplotlib

Presentation of different types of graphs
Curves
Charts
Point clouds
Histograms
Introduction to graph customization

(Optional Bokeh)

Training in all types of interactive graphics that can be integrated into a Web page
Visualization of geographical data
Discovery and creation of Widgets

Seaborn

Control of distribution analysis
Implementation of statistical
Analysis Introduction to multivariate analysis

Skills acquired at the end

Control and customization of a large set of graph types, which is fundamental for Data Visualization
Being able to use Data Visualization for Data Analysis
Creating simple statistical graphs that mix Data Visualization and Data Analysis
Mastering of data visualization best practices and data storytelling techniques

MACHINE LEARNING - Duration 45h

Classification models and algorithms

Introduction to Scikit-learn
Presentation of classic algorithms: Logistic regression, KNN, SVM...
Bagging and Boosting techniques

Skills acquired at the end

Mastery of the main Machine Learning algorithms
Preparing data for modeling and prediction
Identifying the right algorithm for a given problem
Knowing and mastering the evaluation metrics of Machine Learning algorithms

Advanced classification of models

Selection of models.
Semi-supervised
Classification Anomaly detection

Regression methods

Simple and multiple linear regression
Regularized linear regression

Clustering methods

Unsupervised classification models (K-Means, CAH, Mean Shift...)
Evaluation metrics for clustering

ADVANCE MACHINE LEARNING - Duration 52h

Statsmodels

Discovery of ARIMA and SARIMA models
Analysis and decomposition of a temporal signal

Skills acquired at the end

Mastering data management and text data pre-treatment
Reading and using regular expressions
Using a Machine Learning model on text data, an area which is continuously growing in Data Analysis
Understanding the structure of an Inte rnet website
Automatizing the extraction of data coming from diverse webpages

Machine Learning and Graph Theory with Network X (optional)

Introduction to graph theory
Application of fundamental algorithms: Krustal and Dijkstra
Detection of communities
Application of the PageRank algorithm (classify webpages)

Time Series withSize reduction methods

Feature selection process
Introduction to principal component analysis
Application of the Manifold Learning approach

Text mining

Introduction to regular expressions
Management of text data
Creation of a WordCloud

DATA ENGINEERING - Duration 25h

Language SQL

Discover Relational Databases
Implementation of SQL queries

Skills acquired at the end

Reading and interrogating relational databases
Mastery of SQL queries' syntax
Trainingtotreatmassivedatabases thanks to distributed computing
Applying efficiently Machine Learning models to big databases

PySpark

Discovery of PySpark's different functio-nalities
Large databases management
Machine Learning
Optimization

DEEP LEARNING - Duration 60h

Deep Learning with Keras framework

Discovery of fundamental concepts:
Dense Neural Networks
Convolutional Neural Networks.
Transfer Learning
LeNet Architecture.

Skills acquired at the end

Linking TensorFlow with Keras
Application of Word Embedding with Word2vec
Presentation of the Recurent Neural Networks (GRU, LSTM...)
Presentation of the Generative adversial. Network

COMPLEX SYSTEM AND AI- Duration 22h

Introduction to Reinforcement Learning

Development of mathematics for Reinforcement Learning
Application of the Monte-Carlo method
Discovery of the Temporal Difference
Comparison of learnings: SARSA and Q-Learning

Skills acquired at the end

Understanding the mathematical funda-mentals of Reinforcement Learning
Understanding the main algorithms used in Reinforcement Learning
Knowing how to choose a Reinforcement Learning algorithm according to the task at hand

Deep Reinforcement Learning

Presentation of Deep Q Learning
Introduction to the Policy Gradient

Artificial Intelligence

Al Introduction
Perceptron
Multi-Layer perceptron
Markov Decision Process
Logical Agent & First Order Logical application

Predictive Modeling for Housing Prices

In this project, students use machine learning algorithms to predict house prices based on various features like the number of bedrooms, location, squ are footage, etc. They often work with datasets like the Boston Housing Dataset or Kaggle's House Pr-ice Prediction Dataset. The goal is to build a model (such as linear regression, decision trees, or random forests) that can predict house prices for new listings

Diomand Price Prediction

For a student machine learning project predicting diamond price is something where they learn the structured approach of doing the same.Objective: Build a machine learning model to predict diam-ond prices based on features like carat, cut, color, clarity, depth, table, etc.

Phishing Classifier

Creating a phishing classifier for a machine learning project is an excellent way for students to apply their knowledge of classification algorithms, data. preprocessing, and model evaluation.

Objective: Create a model that classifies whether a given email or URL is phishing or not (Le, it's either malicious or safe).

Air Quality Prediction and Analysis

Collect and analyze data from air quality moni-toring stations. Use machine learning to predict pollution levels based on factors like weather, traffic, and industrial activity. Visualize patterns and identify pollution hotspots in specific regions.

Cement Strength Prediction

A cement strength prediction project using machine learning can be a great student project, as it comb-ines real-world engineering problems with data analysis techniques.

1. Project Overview Objective:

Predict the compressive strength of cement (a key property of cement) based on factors like ingredients, curing time, and temperature.

Credit Card Fraud Detection

Credit card fraud detection is a critical application of machine learning, and it involves developing algorithms that can identify suspicious activities or transactions that may indicate fraudulent behavior.

The data science and machine learning sectors in India are experiencing significant growth, offering a multitude of job opportunities across various industries. As of February 2025, there are over 32,000 positions available in these fields, reflecting the expanding demand for skilled professionals.