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2X your salary with
Data Science

9 month program | Rs. 2,14,997 + 18% GST

Embark on an unprecedented journey towards data science mastery by joining us in the AI Mastercourse, where you'll discover the ultimate step-by-step system to propel your career to new heights! With me as your guide, you'll have exclusive access to real-time industry-grade experience, enabling you to upskill and gain invaluable real-world knowledge. Say goodbye to the fluff and nonsense and hello to a world-class education led by none other than the leading Data Scientist of 2023, Chirag. Join us on this

ground-breaking journey and revolutionize your approach to data science forever.

Program Structure


Intensive 9 Month Data Science Mastery

Our 9 month comprehensive learning program is designed to equip you with the core concepts and skills required to succeed as a Data Scientist in the field of AI. From foundational concepts to industry-ready skills, this program covers everything you need to know to make your mark in the field.

Dynamic 9 Month Real-World Action Course

During the 9 month Real World Action phase, you will gain practical experience by working on domain-specific projects. This hands-on approach will equip you with actionable knowledge, enabling you to confidently handle real-world use cases

Results-Driven 9 Month Placement and Freelance Support

Our commitment to you doesn't end with the completion of the course. During the 2-month Placement/Freelance Assistance phase, we will continue working with you until you secure a job that meets your expectations or high-paying freelance projects.

Innovative 9 Month Personalized Data Project Accelerator

The 9 month Working with Your Personal Data phase is an opportunity to apply the knowledge and skills you've acquired in a personal project. You will have the chance to explore your interests and work on a project that aligns with your goals.


Fast income

You have an option to put your lessons in practice and start your income from as early as 3rd month!


Freelance MasterTrack

Learn the skills to earn on platforms like Upwork, Jooble , Toptal etc.


Present yourself in a powerful way

Custom designed portfolio to present yourself as an Outstanding Data Scientist.


Become an industry ready Data Scientist

Dedicated Industry Grade Expert Mentorship

Program Roadmap


Program Curriculam

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Month 1

(Week 1)

Introduction to
Data Science

  • Introduction to Data Science and its Applications.

  • The Data Science lifecycle and workflow.

  • Understanding Data Science projects and use cases.

  • Overview of the tools and technologies used in Data Science.

  • Minor Project 1

  • Introduction to Python and its data structures.

  • Data wrangling and cleaning with Python.

  • Working with Numpy and Pandas libraries.

  • Basic data visualization using Matplotlib and Seaborn libraries.

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(Week 2)

Python and DSA (Part 1)

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(Week 3)

Python and DSA
(Part 2)

  • Introduction to Scikit-learn for Machine Learning.

  • Supervised and unsupervised learning with Scikit-learn.

  • Cross-validation and evaluation metrics.

  • Building and tuning models with Scikit-learn.

  • Minor Project 3

  • Major Project 1- Predictive Analysis for Customer Churn 

  • Descriptive statistics and probability theory.

  • Statistical inference and hypothesis testing.

  • Linear regression analysis.

  • Multivariate regression analysis.

  • Minor Project 4

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(Week 4)

Statistics for Data Science (Part 1)

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Month 2

(Week 1)

Statistics for Data Science (Part 2)

  • Non-parametric statistics

  • Time series analysis and forecasting

  • Survival analysis

  • Experimental design and analysis

  • Data Visualization and Storytelling

  • Minor Project 5

  • Major Project 2 - Analysing Factors Affecting Customer Churn  in a Telecom Company

  • Introduction to Machine Learning and its types.

  • Feature engineering and feature selection.

  • Data preprocessing and normalization.

  • Classification algorithms: k-Nearest Neighbors, Naive Bayes, Decision Trees. 

  • Minor Project 6

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(Week 2)

Machine Learning Fundamentals (Part 1)

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(Week 3)

Machine Learning Fundamentals (Part 2)

  • Regression algorithms: Linear Regression, Ridge Regression, Lasso Regression.

  • Clustering algorithms: K-Means Clustering, Hierarchical Clustering.

  • Dimensionality Reduction techniques: PCA, t-SNE.

  • Ensemble Learning techniques: Bagging, Boosting, Random Forests.

  • Minor Project 7

  • Introduction to Deep Learning and Neural Networks.

  • Building Neural Networks with Keras and TensorFlow.

  • Convolutional Neural Networks for image recognition.

  • Recurrent Neural Networks for sequential data analysis.

  • Minor Project 8 

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(Week 4)

Machine Learning Fundamentals (Part 3)

  • Introduction to Reinforcement Learning.

  • Q-learning algorithm and its applications.

  • Policy-based Learning.

  • Actor-Critic methods. 

  • Minor Project 9

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Month 3

(Week 1)

Advanced Machine Learning (Part 1)

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(Week 2)

Advanced Machine Learning (Part 2)

  • Introduction to Bayesian Learning.

  • Probabilistic Graphical Models.

  • Gaussian Processes.

  • Variational Inference. 

  • Minor Project 10

  • Major Project 3 - Predictive Model for Parkinson's Disease Diagnosis and Progression

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  • Introduction to Big Data and its challenges.

  • Working with Hadoop and MapReduce.

  • Distributed data processing with Spark.

  • Distributed data storage with HDFS. 

  • Minor Project 11

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(Week 3)

Big Data and Distributed Computing ( Part 1)

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(Week 4)

Big Data and Distributed Computing (Part 2)

  • Working with NoSQL databases

  • Introduction to Graph Databases

  • Graph Processing with Apache Giraph

  • Introduction to Stream Processing

  • Minor Project 12 

  • Major Project  4 - Building a Scalable Data Processing Platform

  • Introduction to Cloud Computing and its services.

  • Working with Amazon Web Services (AWS) for Data Science.

  • Creating and launching EC2 instances.

  • Managing data storage with Amazon S3. 

  • Minor Project 13

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Month 4

(Week 1)

Data Science in the Cloud (Part 1)

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(Week 2)

Data Science in the Cloud (Part 2)

  • Data processing with Amazon EMR.

  • Working with Amazon Athena and Redshift.

  • Minor Project 14

  • Major Project 5 - Design and Implementation of a Scalable Data Analytics Platform on AWS

  • Introduction to data ethics and responsible AI.

  • Understanding bias and fairness in machine learning models.

  • Data privacy and security.

  • Effective communication of data insights and results.

  • Minor Project 15

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(Week 3)

Data Science Ethics and Communication

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(Week 2)

Capstone Project

  • Applying all the concepts and skills learned in the previous weekends to a real-world data science project.

  • Data acquisition, preparation, and analysis.

  • Building and testing machine learning models.

  • Communicating project findings and insights effectively.

  • Introduction to data visualisation and storytelling

  • Choosing the right charts and graphs for effective data communication

  • Design principles for data visualisation

  • Best practices for creating and presenting data visualisations

  • Minor Project 16

  • Minor Project 6 - Enhancing Data Communication Through Interactive Data Visualization and Storytelling Techniques

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Month 5

(Week 1)

Data Visualisation and Storytelling

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(Week 2)

Natural Language Processing (Part 1)

  • Introduction to Natural Language Processing (NLP)

  • Text preprocessing and tokenization

  • Stemming and Lemmatization

  • Building a Bag-of-words model

  • Minor project 17

  • Introduction to Text classification

  • Feature extraction and Text Representation

  • Training and Evaluating Text classifiers

  • Sentiment Analysis and Topic modeling

  • Minor Project 18

  • Major Project 7 - Development of a Text Classification System for Sentiment Analysis and Topic Modeling using NLP Techniques

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(Week 3)

Natural Language Processing (Part 2)

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(Week 4)

Time Series Analysis

  • Introduction to Time Series Analysis.

  • Stationarity and Differencing.

  • ARIMA and SARIMA models.

  • Prophet for Time Series Forecasting.

  • Minor Project 19

  • Major Project 8 - Predictive Modeling and Forecasting of Financial Time Series Data using Arima and Prophet Models

  • Advanced GANs: CGANs, StyleGANs, BigGANs. (Stable diffusion)

  • Text generation with GPT (Generative Pretrained Transformer)

  • Music and Art generation with Generative AI

  • Auto GPT and LangChain ( Basics and getting started)

  • Using LangChain with StreamLit

LangChain: Taught by no other institute in India 

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Generative AI

Exclusive Content

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Month 6

(Week 1)

Optimization and Simulation

  • Introduction to Optimization and Simulation.

  • Linear Programming.

  • Non-Linear Programming.

  • Monte Carlo Simulation.

  • Introduction to Recommender Systems.

  • Content-Based Recommender Systems.

  • Collaborative Filtering.

  • Hybrid Recommender Systems.

  • Minor Project 21

  • Major Project 9 - Design and Implementation of a Hybrid Recommender System for E-Commerce Platforms

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(Week 2)

Recommender Systems

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(Week 3)

 Web Development

  • Back-end development: Introduction to Node.js.

  • Creating and consuming APIs with Node.js.

  • Database connectivity with Node.js.

  • Django Rest framework

  • Minor Project 22

  • Major Project 10 - Building a Data-Driven Web Application with NODE.JS and DJANGO REST FRAMEWORK)

  • Introduction to Deep Learning.

  • Feedforward Neural Networks.

  • Backpropagation and Gradient Descent.

  • Hyperparameter tuning and optimization.

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Month 7

(Week 1)

Deep Learning (Part 1)

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(Week 2)

Deep Learning (Part 2)

  • Convolutional Neural Networks.

  • Transfer Learning and Fine-tuning.

  • Recurrent Neural Networks.

  • Sequence-to-Sequence models.

  • LSTM

  • Minor Project 24

  • Major Project 11 - Development and Optimization of  a Deep Learning Model for Image Recognition and Captioning using Convolutional Neural Networks and LSTM Networks

  • Introduction to Data Engineering.

  • Working with SQL databases: MySQL and PostgreSQL.

  • Data Warehousing and ETL processes.

  • Data Integration and Data Pipelines.

  • Minor Project 25 

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(Week 3)

Data Engineering (Part 1)

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(Week 4)

Data Engineering (Part 2)

  • Working with NoSQL databases: MongoDB and Cassandra.

  • Data streaming with Apache Kafka.

  • Batch processing with Apache Flink.

  • Big Data processing with Apache Beam.

  • Minor Project 26

  • Major Project 12 - Design and Implementation of a Scalable Data Engineering Solution for real-time analytics and reporting.

  • Policy Gradient methods.

  • Actor-Critic methods.

  • Multi-Agent Reinforcement Learning.

  • Simulation-based Reinforcement Learning.

  • Minor Project 27

  • Major Project 13 - Teaching an AI to play the snake game using Reinforcement Learning

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Month 8

(Week 1)

Reinforcement Learning 

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(Week 2)

Data Science Project Management

  • Introduction to Project Management for Data Science.

  • Agile methodologies for Data Science projects.

  • Managing project scope and timelines.

  • Collaboration and Communication in Data Science teams.

  • Minor Project 28

  • Building and deploying Machine Learning models in production.

  • Monitoring and maintaining deployed models.

  • Testing and debugging Machine Learning models.

  • Continuous Integration and Delivery (CI/CD) for Data Science.

  • Recommender System

  • Minor Project 29

  • Major Project 14 - Predictive Maintenance for Industrial Equipment: Building, Deploying, and Maintaining ML Models in Production, Including Monitoring, Testing , Debugging and CI/CD

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(Week 3

Data Science in Production

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(Week 4)

Final Project

  • Working on a Capstone project in a team.

  • Designing and implementing end-to-end Machine Learning pipelines.

  • Preparing and presenting project findings to stakeholders

Why does Black Elephant Guarantee Success?

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We Focus on

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We would teach what industries would
want from you

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We focus on the
, & 
not the tools

Stay on top of the Data Science Trends, and always have an edge for better career opportunities

  • Weekly Industry Grade Assignments

  • Personal Dedicated Accountability Coach 

  • One on one call once in every 15 days (once in 2 weeks) 

  • 2 Project Hackathons driven by industry experts 

  • 3 Main Critical Projects to enhance your Portfolio 

  • Monthly Town Hall Meets (online) to get Key Industry Insights and opportunities to network 

  • Access to the community of Industry Grade Data Scientist 


2X your salary with Data Science

9 month program | Rs. 2,14,997 + 18% GST

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