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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

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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.

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Fast income

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

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Freelance MasterTrack

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

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Present yourself in a powerful way

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

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Become an industry ready Data Scientist

Dedicated Industry Grade Expert Mentorship

Program Roadmap

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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
Deployment

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We would teach what industries would
want from you

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We focus on the
problem, & 
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 

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2X your salary with Data Science

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

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