Data Science & Machine Learning
Complete Data Science Masterclass
From Raw Data to Production ML Models
Ready to Master Complete Data Science Masterclass - Zero to Data Scientist?
Choose your plan and start your journey into the future of technology today.
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
🚀 Projects
- Python fundamentals practice notebook
- Building a data science toolkit with functions
- Automated data processing script
💪 Practice
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
🚀 Projects
- Image manipulation with NumPy arrays
- Statistical calculator using NumPy
- Matrix operations library
- Performance comparison: NumPy vs pure Python
💪 Practice
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
🚀 Projects
- Complete EDA on retail sales dataset
- Data cleaning pipeline for messy dataset
- Time series analysis of stock prices
- Customer behavior analysis
💪 Practice
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
🚀 Projects
- Statistical analysis of A/B test results
- Hypothesis testing on real datasets
- Building a statistical testing framework
- Correlation analysis of multiple variables
💪 Practice
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
🚀 Projects
- Interactive COVID-19 dashboard
- Sales performance dashboard
- Geospatial analysis of crime data
- Animated visualization of algorithm performance
💪 Practice
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
🚀 Projects
- Building a data warehouse schema
- Complex business queries with window functions
- Python-SQL integration project
- Database performance optimization
💪 Practice
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
🚀 Projects
- 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
🚀 Projects
- House price prediction model
- Sales forecasting system
- Customer lifetime value prediction
- Build regression library from scratch
💪 Practice
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
🚀 Projects
- Credit default prediction
- Customer churn prediction
- Disease diagnosis system
- Fraud detection model
💪 Practice
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
🚀 Projects
- Anomaly detection in network traffic
- Movie recommendation system
- Sales forecasting with Prophet
- Model interpretation dashboard
💪 Practice
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
🚀 Projects
- Customer segmentation analysis
- Market basket analysis for retail
- Document clustering system
- Anomaly detection in IoT data
💪 Practice
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
- Participate in current Kaggle competition
- Achieve top 50% in beginner competition
- Write competition solution walkthrough
- Build reusable competition pipeline
💪 Practice
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)
🚀 Projects
- Build complete ML pipeline from scratch
- Deploy model as REST API
- Create model monitoring dashboard
- Implement A/B testing framework
💪 Practice
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
- Choose 2 industries and build domain-specific models
- Create industry-specific feature engineering
- Build domain knowledge documentation
💪 Practice
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
🚀 Projects
- Set up MLOps pipeline
- Implement CI/CD for ML project
- Build feature store
- Create model governance framework
💪 Practice
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
- Participate in intermediate Kaggle competition
- Achieve top 30% ranking
- Open-source competition solution
- Write detailed solution approach
💪 Practice
Review winning solutions from 10 competitions
📚 Topics Covered
- Complex problem solving
- Multi-model systems
- Production-ready code
- Professional documentation
- Stakeholder presentation
🚀 Projects
- 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
🚀 Projects
- Neural network from scratch in NumPy
- MNIST digit classification
- Binary classification with deep learning
- Regression with neural networks
💪 Practice
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
🚀 Projects
- Image classification on custom dataset
- Object detection system
- Face recognition application
- Image segmentation for medical images
💪 Practice
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
🚀 Projects
- Stock price prediction with LSTM
- Text generation model
- Sentiment analysis system
- Time series anomaly detection
💪 Practice
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
🚀 Projects
- Build custom chatbot
- News article classifier
- Question answering system
- Text summarization tool
💪 Practice
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
🚀 Projects
- Build and train a GAN
- Implement autoencoder for anomaly detection
- Model compression project
- Multi-modal classification system
💪 Practice
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
🚀 Projects
- Medical diagnosis from X-rays
- Real-time object tracking system
- Document scanner with OCR
- Pose estimation application
💪 Practice
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
🚀 Projects
- RAG-based question answering system
- Semantic search engine
- AI writing assistant
- Code generation tool
💪 Practice
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
🚀 Projects
- Game-playing AI agent
- Trading bot with RL
- Recommendation system with RL
- Robot control simulation
💪 Practice
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
🚀 Projects
- Implement 2 recent research papers
- Write detailed implementation report
- Open source your implementations
- Create tutorial for community
💪 Practice
Read and summarize 20 research papers
📚 Topics Covered
- Complex deep learning systems
- Multi-model architectures
- Production deployment
- Performance optimization
- Comprehensive evaluation
🚀 Projects
- 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
🚀 Projects
- Build complete MLOps pipeline
- Implement model monitoring system
- Set up continuous training
- Deploy models on Kubernetes
💪 Practice
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
🚀 Projects
- Build Spark ML pipeline
- Real-time prediction system
- Data lake architecture design
- Streaming analytics dashboard
💪 Practice
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
🚀 Projects
- Deploy models on 3 cloud platforms
- Build serverless ML pipeline
- Implement auto-scaling solution
- Create disaster recovery plan
💪 Practice
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
🚀 Projects
- Design data architecture for startup
- Build ETL pipeline with Airflow
- Implement data quality framework
- Create data product
💪 Practice
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
🚀 Projects
- Create executive presentation
- Write technical documentation
- Build project proposal
- Develop data strategy document
💪 Practice
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
🚀 Projects
- Deep specialization project in chosen domain
- Build 3 projects in specialization area
- Create specialized portfolio website
💪 Practice
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
🚀 Projects
- Launch portfolio website
- Write 5 technical blog posts
- Contribute to 3 open source projects
- Create video tutorials
💪 Practice
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
🚀 Projects
- Complete 50 LeetCode problems
- Design 5 ML systems
- Prepare behavioral stories
- Record mock interviews
💪 Practice
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
🚀 Projects
- Optimize resume for 5 job types
- Create freelance service offerings
- Build client proposal template
- Develop career roadmap
💪 Practice
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
🚀 Projects
- 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
🚀 Projects
- Create learning roadmap
- Join research reading group
- Start mentoring others
- Plan conference attendance
💪 Practice
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
🚀 Projects
- Complete one advanced certification
- Plan certification roadmap
- Join professional organizations
💪 Practice
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
🚀 Projects
- Ideate 5 data products
- Build MVP of one product
- Create business plan
- Launch beta version
💪 Practice
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
🚀 Projects
- Mentor 3 beginners
- Create free educational resource
- Organize local meetup
- Contribute to major open source project
💪 Practice
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
Recognized by 500+ hiring partners
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 Expectations
After 6 Months
₹6-10 LPA (Junior Data Scientist)
After 12 Months
₹10-20 LPA (Data Scientist)
After 18 Months
₹15-30 LPA (Senior Data Scientist)
After 24 Months
₹20-50+ LPA (Lead/Principal Data Scientist)
Freelance
₹3000-10000/hour based on expertise
International
$80k-200k USD based on location and experience
Course Guarantees
Money Back
30-day money back guarantee
Job Assistance
Job placement support with 500+ partner companies
Lifetime Updates
Free access to all future content updates
Mentorship
Dedicated mentor throughout the journey
Certificate
Industry-recognized certification
Portfolio
Production-ready portfolio by completion