Data Science & Machine Learning
Complete Data Science Masterclass
From Raw Data to Production ML Models
Published October 2025
Flexible course duration
Duration depends on the student's background and pace. Beginners (kids / teens): typically 6 to 9 months. Adults with prior knowledge: often shorter, with an accelerated path.
Standard pace6 to 9 months
AcceleratedAdd class frequency to finish faster
For personalised duration planning, call +91 91233 66161 and we'll map a schedule to your goals.
Ready to Master Data Science Course: Zero to Job-Ready Data Scientist?
Choose your plan and start your journey into the future of technology today.
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International Students (Outside India)
Billed monthly in US dollars, the same price in every country. Contact us with any questions.
Program Overview
This is not just a course, it's a complete transformation into a professional data scientist. In the age of AI and big data, data scientists are the most sought-after professionals. This 12-month masterclass takes you from absolute beginner to a job-ready data scientist, capable of extracting insights from data, building predictive models, deploying ML solutions, and solving complex business problems. You'll master the complete data science pipeline: data collection, cleaning, exploration, feature engineering, model building, evaluation, and deployment.
What Makes This Program Different
- Starts from absolute zero - no prerequisites required
- Complete 12-month structured curriculum aligned with industry needs
- Covers entire data science stack: Python, ML, DL, Statistics, Big Data
- 50+ real-world projects and 5 Kaggle competitions
- Focus on both theory and practical implementation
- MLOps and production deployment included
- Interview preparation for FAANG and top companies
- Lifetime access with continuous updates
- Build a portfolio showcasing end-to-end ML projects
Your Learning Journey
Phase 1
Foundation (Months 1-3): Python, Statistics, Data Analysis, SQL
Phase 2
Machine Learning (Months 4-6): Algorithms, Feature Engineering, Model Evaluation
Phase 3
Deep Learning (Months 7-9): Neural Networks, Computer Vision, NLP
Phase 4
Production & Specialization (Months 10-12): MLOps, Big Data, Career Launch
Career Progression
1
Junior Data Scientist (after 3 months)
2
Data Scientist (after 6 months)
3
Senior Data Scientist (after 9 months)
4
ML Engineer / Lead Data Scientist (after 12 months)
Detailed Course Curriculum
Explore the complete week-by-week breakdown of what you'll learn in this comprehensive program.
Topics Covered
- Why Python for Data Science?
- Setting up the data science environment (Anaconda, Jupyter)
- Python basics: variables, data types, operators
- Data structures: lists, tuples, sets, dictionaries
- Control flow: if-else, loops (for, while)
- Functions and lambda expressions
- Object-oriented programming basics
- File handling and exception management
- Python libraries ecosystem for data science
- Git and version control for data science projects
- Jupyter notebooks best practices
- Google Colab for cloud computing
Projects You Build
- Python fundamentals practice notebook
- Building a data science toolkit with functions
- Automated data processing script
Practice & Assignments
Complete 100 Python coding challenges
Topics Covered
- Introduction to NumPy and its importance
- NumPy arrays: creation and properties
- Array indexing, slicing, and reshaping
- Broadcasting and vectorization
- Mathematical operations on arrays
- Statistical functions in NumPy
- Linear algebra operations
- Random number generation
- Array manipulation techniques
- Performance optimization with NumPy
- Memory management in NumPy
- NumPy for image processing basics
Projects You Build
- Image manipulation with NumPy arrays
- Statistical calculator using NumPy
- Matrix operations library
- Performance comparison: NumPy vs pure Python
Practice & Assignments
Solve 50 NumPy exercises
Topics Covered
- Introduction to Pandas DataFrames and Series
- Reading data from various formats (CSV, Excel, JSON, SQL)
- Data inspection and understanding
- Indexing and selecting data (.loc, .iloc)
- Filtering and boolean indexing
- Handling missing data strategies
- Data transformation and feature creation
- Groupby operations and aggregations
- Merging, joining, and concatenating data
- Pivot tables and cross-tabulation
- Time series data handling
- Performance optimization in Pandas
Projects You Build
- Complete EDA on retail sales dataset
- Data cleaning pipeline for messy dataset
- Time series analysis of stock prices
- Customer behavior analysis
Practice & Assignments
Clean and analyze 10 different datasets
Topics Covered
- Descriptive statistics: measures of central tendency and spread
- Probability theory and distributions
- Normal distribution and Central Limit Theorem
- Sampling and sampling distributions
- Hypothesis testing fundamentals
- T-tests, Chi-square tests, ANOVA
- P-values and statistical significance
- Confidence intervals
- Correlation and covariance
- Type I and Type II errors
- Power analysis and sample size determination
- Bayesian statistics introduction
Projects You Build
- Statistical analysis of A/B test results
- Hypothesis testing on real datasets
- Building a statistical testing framework
- Correlation analysis of multiple variables
Practice & Assignments
Perform statistical tests on 15 different scenarios
Topics Covered
- Principles of effective data visualization
- Matplotlib: creating publication-quality plots
- Seaborn for statistical visualizations
- Plotly for interactive visualizations
- Creating dashboards with Streamlit/Dash
- Geospatial visualization with Folium
- Time series visualization techniques
- Heatmaps and correlation matrices
- Network graphs and tree visualizations
- 3D visualizations
- Animation in data visualization
- Best practices for different chart types
Projects You Build
- Interactive COVID-19 dashboard
- Sales performance dashboard
- Geospatial analysis of crime data
- Animated visualization of algorithm performance
Practice & Assignments
Create 30 different types of visualizations
Topics Covered
- Relational database concepts
- SQL fundamentals: SELECT, WHERE, ORDER BY
- Aggregate functions and GROUP BY
- Joins: INNER, LEFT, RIGHT, FULL OUTER
- Subqueries and CTEs
- Window functions for advanced analytics
- Database design and normalization
- Performance optimization and indexing
- NoSQL databases introduction (MongoDB)
- Connecting Python to databases
- SQL vs Pandas: when to use what
- Big data SQL: Spark SQL, Presto
Projects You Build
- Building a data warehouse schema
- Complex business queries with window functions
- Python-SQL integration project
- Database performance optimization
Practice & Assignments
Write 100 SQL queries of increasing complexity
Topics Covered
- EDA methodology and workflow
- Univariate, bivariate, and multivariate analysis
- Identifying patterns and anomalies
- Feature engineering techniques
- Creating polynomial and interaction features
- Binning and discretization
- Encoding categorical variables
- Feature scaling and normalization
- Handling imbalanced datasets
- Feature selection methods
- Dimensionality reduction (PCA introduction)
- Domain-specific feature engineering
Projects You Build
- PHASE 1 CAPSTONE: End-to-End EDA Project
- Complete EDA and feature engineering on Kaggle dataset
- Build reusable feature engineering pipeline
- Create automated EDA report generator
Assessment
Phase 1 comprehensive assessment
Topics Covered
- Machine learning workflow and pipeline
- Linear regression from scratch
- Multiple linear regression
- Polynomial regression
- Ridge and Lasso regression
- Elastic Net
- Logistic regression for classification
- Evaluation metrics for regression
- Cross-validation strategies
- Bias-variance tradeoff
- Regularization techniques
- Feature importance analysis
Projects You Build
- House price prediction model
- Sales forecasting system
- Customer lifetime value prediction
- Build regression library from scratch
Practice & Assignments
Implement 5 regression algorithms from scratch
Topics Covered
- k-Nearest Neighbors (KNN)
- Decision trees and pruning
- Random Forests
- Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Support Vector Machines (SVM)
- Naive Bayes classifier
- Evaluation metrics for classification
- ROC curves and AUC
- Handling imbalanced classes
- Multi-class classification strategies
- Ensemble methods
- Stacking and blending
Projects You Build
- Credit default prediction
- Customer churn prediction
- Disease diagnosis system
- Fraud detection model
Practice & Assignments
Build 10 classification models on different datasets
Topics Covered
- Hyperparameter tuning: Grid Search, Random Search, Bayesian Optimization
- AutoML tools and techniques
- Feature selection algorithms
- Dimensionality reduction: PCA, t-SNE, UMAP
- Anomaly detection algorithms
- Time series forecasting: ARIMA, Prophet
- Recommendation systems: collaborative filtering, content-based
- Model interpretation: SHAP, LIME
- Handling missing data: advanced imputation
- Semi-supervised learning
- Active learning strategies
- Transfer learning in classical ML
Projects You Build
- Anomaly detection in network traffic
- Movie recommendation system
- Sales forecasting with Prophet
- Model interpretation dashboard
Practice & Assignments
Apply advanced techniques to improve previous models
Topics Covered
- Clustering algorithms: K-Means, DBSCAN, Hierarchical
- Gaussian Mixture Models
- Clustering evaluation metrics
- Market basket analysis
- Association rules: Apriori, FP-Growth
- Topic modeling: LDA, NMF
- Autoencoders for dimensionality reduction
- Self-organizing maps
- Isolation Forest for anomaly detection
- Applications in customer segmentation
- Image compression with clustering
- Text clustering and document similarity
Projects You Build
- Customer segmentation analysis
- Market basket analysis for retail
- Document clustering system
- Anomaly detection in IoT data
Practice & Assignments
Apply clustering to 5 different domains
Topics Covered
- Understanding Kaggle competitions
- Competition strategies and workflow
- Data augmentation techniques
- Advanced feature engineering
- Ensemble strategies for competitions
- Learning from kernels and discussions
- Leaderboard probing
- Cross-validation strategies
- Submission strategies
- Post-competition analysis
Projects You Build
- Participate in current Kaggle competition
- Achieve top 50% in beginner competition
- Write competition solution walkthrough
- Build reusable competition pipeline
Practice & Assignments
Complete 3 past Kaggle competitions
Topics Covered
- Problem formulation and scoping
- Data collection strategies
- Building data pipelines
- Feature store design
- Model versioning and experiment tracking
- A/B testing for ML models
- Model monitoring and drift detection
- Building ML APIs with Flask/FastAPI
- Containerization with Docker
- Cloud deployment (AWS, GCP, Azure)
- Cost optimization for ML
- Documentation and reporting
Projects You Build
- Build complete ML pipeline from scratch
- Deploy model as REST API
- Create model monitoring dashboard
- Implement A/B testing framework
Practice & Assignments
Deploy 3 models to production
Topics Covered
- Healthcare: disease prediction, drug discovery
- Finance: risk assessment, algorithmic trading
- Retail: demand forecasting, pricing optimization
- Marketing: customer segmentation, campaign optimization
- Manufacturing: predictive maintenance, quality control
- Transportation: route optimization, demand prediction
- Energy: consumption forecasting, grid optimization
- Agriculture: crop yield prediction, pest detection
- Real estate: price prediction, investment analysis
- Sports analytics: player performance, game prediction
Projects You Build
- Choose 2 industries and build domain-specific models
- Create industry-specific feature engineering
- Build domain knowledge documentation
Practice & Assignments
Analyze datasets from 5 different industries
Topics Covered
- Code organization for ML projects
- Testing ML code: unit tests, integration tests
- Continuous Integration/Continuous Deployment (CI/CD)
- MLflow for experiment tracking
- DVC for data versioning
- Weights & Biases for experiment management
- Model registry and governance
- Feature stores: Feast, Tecton
- Reproducibility in ML
- Debugging ML models
- Performance optimization
- Security in ML systems
Projects You Build
- Set up MLOps pipeline
- Implement CI/CD for ML project
- Build feature store
- Create model governance framework
Practice & Assignments
Refactor all projects with engineering best practices
Topics Covered
- Advanced competition strategies
- Reading research papers for techniques
- Implementing papers for competitions
- Advanced ensembling: stacking, blending
- Pseudo-labeling techniques
- Data augmentation for tabular data
- Feature engineering automation
- Hyperparameter optimization at scale
- GPU acceleration for tree models
- Competition code organization
Projects You Build
- Participate in intermediate Kaggle competition
- Achieve top 30% ranking
- Open-source competition solution
- Write detailed solution approach
Practice & Assignments
Review winning solutions from 10 competitions
Topics Covered
- Complex problem solving
- Multi-model systems
- Production-ready code
- Professional documentation
- Stakeholder presentation
Projects You Build
- PHASE 2 CAPSTONE: Production ML System
- Build end-to-end ML system for real business problem
- Include data pipeline, multiple models, API, monitoring
- Deploy to cloud with full MLOps pipeline
Assessment
Phase 2 comprehensive assessment
Topics Covered
- Introduction to deep learning
- Perceptron and multi-layer perceptrons
- Backpropagation algorithm
- Activation functions
- Weight initialization strategies
- Gradient descent variations
- Learning rate scheduling
- Batch normalization
- Dropout and regularization
- Building neural networks from scratch
- Introduction to TensorFlow and Keras
- PyTorch fundamentals
Projects You Build
- Neural network from scratch in NumPy
- MNIST digit classification
- Binary classification with deep learning
- Regression with neural networks
Practice & Assignments
Implement 5 different neural network architectures
Topics Covered
- CNN architecture and intuition
- Convolution and pooling layers
- Popular architectures: LeNet, AlexNet, VGG, ResNet
- Transfer learning and fine-tuning
- Data augmentation for images
- Object detection: YOLO, R-CNN
- Image segmentation: U-Net
- Face recognition systems
- Style transfer
- Generative models: VAE, GAN basics
- CNN applications beyond images
- Deploying CNN models
Projects You Build
- Image classification on custom dataset
- Object detection system
- Face recognition application
- Image segmentation for medical images
Practice & Assignments
Build 5 computer vision applications
Topics Covered
- RNN architecture and applications
- Vanishing gradient problem
- LSTM and GRU architectures
- Bidirectional RNNs
- Sequence-to-sequence models
- Attention mechanism
- Time series prediction with RNNs
- Text generation
- Sentiment analysis
- Named Entity Recognition
- Machine translation basics
- Speech recognition introduction
Projects You Build
- Stock price prediction with LSTM
- Text generation model
- Sentiment analysis system
- Time series anomaly detection
Practice & Assignments
Implement 5 sequence modeling tasks
Topics Covered
- Text preprocessing and tokenization
- Word embeddings: Word2Vec, GloVe
- Text classification techniques
- Named Entity Recognition (NER)
- Part-of-speech tagging
- Topic modeling with deep learning
- Transformer architecture
- BERT and GPT models
- Fine-tuning pre-trained models
- Question answering systems
- Text summarization
- Chatbot development
Projects You Build
- Build custom chatbot
- News article classifier
- Question answering system
- Text summarization tool
Practice & Assignments
Complete 5 NLP projects using transformers
Topics Covered
- Autoencoders and variational autoencoders
- Generative Adversarial Networks (GANs)
- Deep Reinforcement Learning basics
- Graph Neural Networks introduction
- Meta-learning and few-shot learning
- Neural Architecture Search
- Model compression and quantization
- Edge deployment of deep learning
- Adversarial machine learning
- Explainable AI for deep learning
- Multi-modal learning
- Self-supervised learning
Projects You Build
- Build and train a GAN
- Implement autoencoder for anomaly detection
- Model compression project
- Multi-modal classification system
Practice & Assignments
Experiment with 3 cutting-edge techniques
Topics Covered
- Medical image analysis
- Autonomous driving perception
- Facial emotion recognition
- Pose estimation
- Video analysis and action recognition
- 3D computer vision
- Document analysis and OCR
- Satellite image analysis
- Real-time vision systems
- Mobile and edge deployment
- Vision transformers
- Self-supervised learning in vision
Projects You Build
- Medical diagnosis from X-rays
- Real-time object tracking system
- Document scanner with OCR
- Pose estimation application
Practice & Assignments
Build portfolio of 5 vision projects
Topics Covered
- Large Language Models (LLMs)
- Prompt engineering
- Fine-tuning LLMs
- Retrieval Augmented Generation (RAG)
- Building with LangChain
- Vector databases
- Semantic search
- Document intelligence
- Code generation with AI
- Multimodal models
- Ethical considerations in NLP
- Production NLP systems
Projects You Build
- RAG-based question answering system
- Semantic search engine
- AI writing assistant
- Code generation tool
Practice & Assignments
Build 5 LLM-powered applications
Topics Covered
- RL fundamentals and Markov Decision Processes
- Q-Learning and Deep Q-Networks
- Policy gradient methods
- Actor-Critic methods
- Proximal Policy Optimization (PPO)
- Game playing agents
- Robotics applications
- RL for recommendation systems
- RL in finance
- Multi-agent RL
- Sim-to-real transfer
- OpenAI Gym environments
Projects You Build
- Game-playing AI agent
- Trading bot with RL
- Recommendation system with RL
- Robot control simulation
Practice & Assignments
Train agents in 5 different environments
Topics Covered
- Reading and understanding research papers
- Reproducing paper results
- Implementing novel architectures
- Benchmarking and evaluation
- Writing technical reports
- Contributing to open source
- Publishing your own research
- Staying updated with latest research
- Research tools and resources
- Building research portfolio
- Collaboration in research
- Ethics in AI research
Projects You Build
- Implement 2 recent research papers
- Write detailed implementation report
- Open source your implementations
- Create tutorial for community
Practice & Assignments
Read and summarize 20 research papers
Topics Covered
- Complex deep learning systems
- Multi-model architectures
- Production deployment
- Performance optimization
- Comprehensive evaluation
Projects You Build
- PHASE 3 CAPSTONE: State-of-the-art AI System
- Build cutting-edge AI application
- Combine multiple deep learning techniques
- Deploy with full production pipeline
- Achieve competitive performance metrics
Assessment
Phase 3 comprehensive assessment
Topics Covered
- MLOps principles and practices
- Model lifecycle management
- Continuous training pipelines
- Model versioning strategies
- A/B testing for ML
- Model monitoring and observability
- Drift detection and handling
- Feature stores at scale
- Model serving architectures
- Kubernetes for ML
- Kubeflow and MLflow
- Cost optimization in production
Projects You Build
- Build complete MLOps pipeline
- Implement model monitoring system
- Set up continuous training
- Deploy models on Kubernetes
Practice & Assignments
Deploy 5 models with full MLOps
Topics Covered
- Big data ecosystem overview
- Apache Spark fundamentals
- PySpark for data science
- Spark MLlib for machine learning
- Distributed computing concepts
- Hadoop and HDFS
- Apache Kafka for streaming
- Real-time ML with streaming data
- Data lakes and data warehouses
- Apache Airflow for orchestration
- Databricks platform
- Cloud big data services
Projects You Build
- Build Spark ML pipeline
- Real-time prediction system
- Data lake architecture design
- Streaming analytics dashboard
Practice & Assignments
Process 5 big data datasets
Topics Covered
- AWS for data science: SageMaker, EMR, Glue
- Google Cloud Platform: Vertex AI, BigQuery, Dataflow
- Azure ML and Azure Databricks
- Serverless ML deployments
- Auto-scaling ML services
- Cloud cost optimization
- Multi-cloud strategies
- Edge computing for ML
- Hybrid cloud architectures
- Security and compliance
- Infrastructure as Code
- Disaster recovery planning
Projects You Build
- Deploy models on 3 cloud platforms
- Build serverless ML pipeline
- Implement auto-scaling solution
- Create disaster recovery plan
Practice & Assignments
Master one cloud platform deeply
Topics Covered
- Data architecture patterns
- ETL vs ELT pipelines
- Data quality and validation
- Schema evolution and management
- CDC (Change Data Capture)
- Data mesh architecture
- Event-driven architectures
- Apache Beam for unified processing
- dbt for data transformation
- Data observability tools
- DataOps practices
- Building data products
Projects You Build
- Design data architecture for startup
- Build ETL pipeline with Airflow
- Implement data quality framework
- Create data product
Practice & Assignments
Build 5 different data pipelines
Topics Covered
- Communicating with stakeholders
- Translating business problems to ML
- Project management for data science
- Agile and Scrum for DS teams
- Technical documentation
- Presenting to executives
- Building data culture
- Ethics in data science
- Privacy and GDPR compliance
- Team collaboration tools
- Mentoring and leadership
- Consulting skills
Projects You Build
- Create executive presentation
- Write technical documentation
- Build project proposal
- Develop data strategy document
Practice & Assignments
Present 5 projects to different audiences
Topics Covered
- Healthcare AI: medical imaging, drug discovery, genomics
- Financial ML: risk modeling, fraud detection, trading
- Retail analytics: recommendation, pricing, inventory
- Computer Vision specialist: autonomous vehicles, robotics
- NLP specialist: conversational AI, document intelligence
- MLOps engineer: platform building, automation
- Research scientist: cutting-edge algorithms
- Choosing your path based on interests
- Building specialized portfolio
- Domain expertise development
- Networking in your chosen field
- Continuous learning strategies
Projects You Build
- Deep specialization project in chosen domain
- Build 3 projects in specialization area
- Create specialized portfolio website
Practice & Assignments
Complete specialized certification or course
Topics Covered
- Building impressive GitHub profile
- Creating portfolio website
- Writing technical blog posts
- Contributing to open source
- Building Kaggle profile
- LinkedIn optimization
- Personal branding strategies
- Public speaking and conferences
- Building online presence
- Networking strategies
- Mentorship and coaching
- Content creation
Projects You Build
- Launch portfolio website
- Write 5 technical blog posts
- Contribute to 3 open source projects
- Create video tutorials
Practice & Assignments
Build complete online presence
Topics Covered
- Data science interview process
- Technical interview preparation
- Coding challenges for DS
- ML system design interviews
- Case study interviews
- Behavioral interview questions
- STAR method for responses
- Salary negotiation
- Company research strategies
- Take-home assignments
- Presentation skills
- Mock interview practice
Projects You Build
- Complete 50 LeetCode problems
- Design 5 ML systems
- Prepare behavioral stories
- Record mock interviews
Practice & Assignments
Do 10 mock interviews
Topics Covered
- Job search strategies
- Resume optimization for ATS
- Cover letter writing
- Networking for jobs
- Working with recruiters
- Freelancing as data scientist
- Building consulting business
- Finding clients
- Pricing your services
- Contract negotiation
- Remote work best practices
- Career growth planning
Projects You Build
- Optimize resume for 5 job types
- Create freelance service offerings
- Build client proposal template
- Develop career roadmap
Practice & Assignments
Apply to 20 relevant positions
Topics Covered
- Capstone project planning
- End-to-end implementation
- Production deployment
- Documentation and presentation
- Peer review and feedback
- Final assessment
- Certification preparation
- Alumni network
- Continuous learning plan
- Career launch strategy
- Celebration and reflection
- Next steps planning
Projects You Build
- FINAL CAPSTONE: Industry-Ready Data Science Project
- Solve real business problem end-to-end
- Include all phases: data, ML, deployment, monitoring
- Present to panel of industry experts
- Open source the solution
Assessment
Final comprehensive examination and project defense
Topics Covered
- Following AI research
- Reading papers effectively
- Attending conferences
- Online communities
- Continuous experimentation
- Building side projects
- Teaching and mentoring
- Contributing to research
- Industry trends
- Emerging technologies
- Career pivots
- Leadership development
Projects You Build
- Create learning roadmap
- Join research reading group
- Start mentoring others
- Plan conference attendance
Practice & Assignments
Dedicate 5 hours/week to learning
Topics Covered
- Cloud certifications (AWS, GCP, Azure)
- Specialized ML certifications
- Domain certifications
- Academic courses and MOOCs
- Professional development
- Executive education
- PhD considerations
- Research opportunities
- Teaching opportunities
- Consulting certifications
- Project management
- Business analytics
Projects You Build
- Complete one advanced certification
- Plan certification roadmap
- Join professional organizations
Practice & Assignments
Pursue continuous credentials
Topics Covered
- Product thinking for DS
- Identifying opportunities
- MVP development
- User research
- Product metrics
- Growth strategies
- Monetization models
- B2B vs B2C products
- SaaS development
- API products
- Data marketplaces
- Entrepreneurship
Projects You Build
- Ideate 5 data products
- Build MVP of one product
- Create business plan
- Launch beta version
Practice & Assignments
Validate product ideas
Topics Covered
- Mentoring beginners
- Creating educational content
- Open source contributions
- Speaking at meetups
- Writing tutorials
- Answering questions online
- Building community
- Organizing events
- Pro bono work
- Teaching workshops
- Creating courses
- Industry advocacy
Projects You Build
- Mentor 3 beginners
- Create free educational resource
- Organize local meetup
- Contribute to major open source project
Practice & Assignments
Give back to community weekly
Projects You'll Build
Build a professional portfolio with 60+ data science projects across all domains real-world projects.
Phase 1 15+ foundation projects - EDA, visualization, SQL analytics
Phase 2 20+ ML projects - classification, regression, clustering, Kaggle
Phase 3 15+ DL projects - computer vision, NLP, reinforcement learning
Phase 4 10+ production projects - MLOps, big data, cloud deployment
Total 60+ projects from basics to production
Weekly Learning Structure
Theory Videos
5-7 hours
Hands On Coding
10-12 hours
Projects
4-6 hours
Practice Problems
3-4 hours
Total Per Week
20-25 hours
Certification & Recognition
Phase Certificates
Certificate after each phase (4 total)
Final Certificate
Professional Data Scientist Certification
Linkedin Badge
Verified LinkedIn badge
Industry Recognized
A course-completion certificate you can add to your resume and LinkedIn.
Portfolio Projects
60+ documented projects for portfolio
Kaggle Achievements
Support to reach Kaggle Expert level
Specialization Certificate
Certificate in chosen specialization area
Technologies & Skills You'll Master
Comprehensive coverage of the entire modern web development stack.
Programming
Python (expert), SQL (advanced), Spark, Git
Mathematics
Linear algebra, calculus, statistics, probability
Data Analysis
Pandas, NumPy, exploratory data analysis, feature engineering
Machine Learning
Scikit-learn, XGBoost, classification, regression, clustering
Deep Learning
TensorFlow, PyTorch, CNN, RNN, Transformers, GANs
Visualization
Matplotlib, Seaborn, Plotly, Tableau, dashboard creation
Big Data
Spark, Hadoop, Kafka, distributed computing
MLOps
Docker, Kubernetes, CI/CD, model monitoring, A/B testing
Cloud
AWS SageMaker, GCP Vertex AI, Azure ML
Databases
SQL, NoSQL, data warehouses, data lakes
Soft Skills
Communication, business acumen, project management
Domains
Healthcare, finance, retail, marketing analytics
Support & Resources
Live Sessions
Weekly problem-solving and doubt clearing sessions
Mentorship
1-on-1 guidance from senior data scientists
Community
Active Discord community with 10,000+ members
Code Review
Expert code reviews for all major projects
Career Support
Resume review, mock interviews, job referrals
Lifetime Access
All content, updates, and new modules forever
Kaggle Support
Kaggle competition teams and guidance
Cloud Credits
Free credits for AWS, GCP, Azure practice
Career Outcomes & Opportunities
Transform your career with industry-ready skills and job placement support.
Prerequisites
Education
No formal degree required - high school math helpful
Coding Experience
None required - we start from zero
Equipment
Computer with 8GB RAM minimum, internet connection
Time Commitment
20-25 hours per week consistently
English
Good reading and comprehension skills
Motivation
Strong curiosity about data and problem-solving mindset
Who Is This Course For?
Students
College students wanting high-paying tech careers
Working Professionals
Career switchers from any field to data science
Entrepreneurs
Build data-driven products and businesses
Freelancers
Offer data science and ML consulting services
Kids
Not suitable for children - recommended age 16+
Anyone
Anyone fascinated by AI and data-driven decision making
Career Paths After Completion
Data Scientist
Machine Learning Engineer
AI Engineer
Deep Learning Engineer
MLOps Engineer
Data Science Consultant
Research Scientist
Computer Vision Engineer
NLP Engineer
Business Intelligence Developer
Quantitative Analyst
Data Science Manager
Chief Data Officer (with experience)
Salary & Market Context
The ranges below are general market salary bands for these roles in India and abroad, drawn from public industry data. They are shown for career context only and are not a promise or guarantee of income. Actual pay depends on your skills, experience, location, and the job market.
Growing Roles
₹6-10 LPA (Junior Data Scientist)
Experienced Roles
₹10-20 LPA (Data Scientist); ₹15-30 LPA (Senior Data Scientist); ₹20-50+ LPA (Lead/Principal Data Scientist)
Freelance
₹3,000-10,000/hour based on expertise
International
$80k-200k USD based on location and experience
Course Guarantees
Live Classes
Live, interactive classes with a real instructor, never pre-recorded videos.
Small Batches
Small batches only: group classes are capped at 10 students, with mini-batch (3 to 4 students) and personal 1-on-1 options.
Structured Curriculum
A structured, well-paced curriculum taught step by step, with hands-on practice in every session.
Doubt Support
Doubt support between classes over WhatsApp, so you are never left stuck.
Certificate
A course-completion certificate you can share.
Free Demo
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What families say
Straight from the parents and students who learn with us. Rated 4.9 across 547 Google reviews.
“Mivaan enjoys the class. He understands the concepts and completes his tasks with excitement. He started taking interest in coding… truly amazing class.”
“I absolutely love it here! I made new friends and learned important valuable coding skills while having the fun of my life. It's not just coding here, it's outings, bonding and most importantly preparing you for your future. Definitely five stars.”
“What stands out most is how excited my son is before every class—he looks forward to learning, problem-solving, and sharing what he's built. I've noticed a big boost in his confidence!”
“Modern Age Coders has been a game-changer for me! I struggled to grasp IT concepts and coding before joining, but their classes transformed everything. I'm now the topper in my class and can confidently write complex programs with ease.”
“Modern Age Coder have wonderful teachers who teach in a clear, easy and practical way. The teacher boosts students' confidence, keeps them updated with technology, and inspires them to learn without hesitation.”
“The one step solution for my son. Modern Age Coders make learning coding so simple that kids love it.”
“Coding classes here make learning very interesting and conceptual. The teachers teach us in a very easy-to-understand and efficient manner.”
“One of the most wonderful education centres out there. Education is not limited to school syllabus but focuses on skill development. Learning here has been a wonderful journey and still continuing.”
“I highly recommend this computer coding class! The teachers are incredibly knowledgeable and passionate about coding. They make every session engaging and insightful.”
“My child Dhairya is really enjoying the Modern Age Coder IT classes. This is his first online class, and he eagerly looks forward to it.”
“Very good classes. Don't worry about coding—they teach the best, especially Shivam sir.”
“Very good classes. Makes learning very easy and interactive.”
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Real parents and students in their own words, on our public YouTube channel.
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