Data Science Complete Masterclass
The full pipeline, honestly taught: Python, statistics and SQL, machine learning that holds up, then deep learning, ending with a capstone you deploy.
Syllabus updated August 2026
Recordings are free with a Google sign-in. These recordings show how we teach, not the exact syllabus of this course.
How long this course takes
This course runs 10-12 months (44-52 weeks) at 2 live classes/week + 4-6 hours practice. The range allows for background and pace: a complete beginner uses the full span, and a student with prior knowledge finishes sooner. The syllabus below is planned to the shorter end, with margin kept for revision.
For personalised duration planning, call +91 91233 66161 and we'll map a schedule to your goals.
At a glance
- Who it is for
- Complete beginner (18+); college students, graduates and working professionals. Age: 18+; college students, graduates and working professionals.
- Prerequisites
- None. Everything is taught from the first line
- Format
- Live, interactive classes with a real instructor, never pre-recorded videos. Live classes run about one hour.
- Language
- English or Hindi, depending on the batch.
- Duration
- 10-12 months (44-52 weeks)
- Weekly commitment
- 2 live classes/week + 4-6 hours practice
- Class size
- Group batches of 5 to 10 students, mini batches of 3 to 4, or 1-on-1.
- Certificate
- Certificate from Modern Age Coders, awarded on passing the final exam
- Watch first
- Free recordings of real classes for ages 13 and up, free with a Google sign-in. These recordings show how we teach, not the exact syllabus of this course.
- Live demo
- Optional. Enroll directly on this page, or book a free live demo if you would like to meet a mentor first.
How we teach
Deep understanding. Real projects. Expert guidance.
Learn coding, AI and maths through careful explanations, practical demonstrations, and hands-on problem-solving. Our instructors guide you from the foundations to advanced ideas, helping you understand what happens, why it happens, and how to build it yourself.
You will explore concepts step by step, write and improve code, investigate mistakes, ask questions, and apply your knowledge to meaningful projects. Lessons are designed around active participation, thoughtful feedback, and growing independence.
Expert-led live teaching, with practical participation built into every lesson.
- Students explain their thinking.
- Instructors demonstrate, then guide practice.
- Learners code, solve, or build during lessons.
- Questions and misconceptions get attention.
- Assignments receive useful feedback.
- Progress is checked before advancing.
Ready to Master Data Science Course: Python, ML & Deep Learning?
Choose your plan and start your journey into the future of technology today.
Rated 4.9 across 547 Google reviews. Enroll directly, or take a free live demo first if you like. No card needed for the demo. Monthly billing, cancel anytime.
International Students (Outside India)
Billed monthly in US dollars, the same price in every country. Contact us with any questions.
Program Overview
Data science hiring has grown sceptical: everyone lists the same libraries, so interviews now dig for the difference between importing scikit-learn and understanding what the model just did. This live online masterclass builds that difference in order: Python with NumPy and Pandas properly, statistics that survive contact with real data, SQL and EDA as daily craft, then machine learning done honestly, regression to ensembles with evaluation you can defend, real end-to-end projects and Kaggle practice, then the deep learning layer: neural networks, CNNs, RNNs and NLP, applied computer vision and a research-paper implementation to prove you can read the field. A finale month ships a deployed capstone and sits the final exam.
The pace is honest: ten teaching months at two live classes a week plus four to six hours of practice, with two more in hand. Problem sets, monthly mixed reviews, phase exams, and a final exam that decides the certificate with a free retest.
What Makes This Program Different
- Statistics before models: evaluation and honesty are the actual job
- End-to-end from mid-course: raw data to working model, not notebook fragments
- Kaggle as training ground, framed honestly: practice, not trophies
- A research-paper implementation: proof you can read the field, not just tutorials
- An honest 10-month arc with 2 months in hand
- Real assessment: weekly problem sets, monthly mixed reviews, phase exams and a final exam with a free retest
Your Learning Journey
Career Progression
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 exam: a practical build defended live, plus a written paper mixing this phase with everything before it
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
- 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 exam: a practical build defended live, plus a written paper mixing this phase with everything before it
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
- 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 exam: a practical build defended live, plus a written paper mixing this phase with everything before it
Topics Covered
- Choosing the capstone: an end-to-end data science project: raw data to a deployed model with an evaluation you can defend
- A one-page spec: scope, milestones, stack
- Repository and skeleton ready on day one
- Instructor sign-off before the build begins
Projects You Build
- Approved capstone spec plus the running skeleton
Practice & Assignments
Pitch the spec to the batch and absorb one hard question
Topics Covered
- Two focused weeks in vertical slices
- Instructor checkpoints and honest scope cuts
- Using the whole course: the statistics spine, ML judgment, deep learning where it earns its place, and honest evaluation
- Daily commits and an always-working build
Projects You Build
- The capstone, feature-complete with version control
Practice & Assignments
A working build at the end of every session
Topics Covered
- The finishing pass: edge cases, errors, documentation
- Deployed and documented: the model serving predictions outside the notebook
- A README worth reading and a demo worth watching
- A full-course spiral review before the exam
Projects You Build
- The capstone shipped, documented and rehearsed
Practice & Assignments
Two mock demos, each tighter than the last
Topics Covered
- Presenting the capstone: live demo plus the hardest bug story
- The honest map of next roads: data science roles, ML engineering depth, or the analytics leadership track
- Where our other courses continue the journey
Projects You Build
- Demo day presentation delivered to the batch
Assessment
FINAL EXAM: a practical build plus a written paper with questions mixed from every phase. Passing earns the certificate; falling short earns a focused revision plan and a free retest
Projects You'll Build
Build a professional portfolio with 15+ real projects, crowned by a deployed end-to-end capstone real-world projects.
Weekly Learning Structure
Certification & Recognition
Technologies & Skills You'll Master
Comprehensive coverage of the entire modern web development stack.
Support & Resources
Career Outcomes & Opportunities
Transform your career with industry-ready skills and job placement support.
Prerequisites
Who Is This Course For?
Career Paths After Completion
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.
Course Guarantees
Real students. Real moments. Real joy.
These are our actual student meetups. No stock photos, no filters. Swipe through and meet the community you'll be joining.
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.”
Hear it from our students
Real parents and students in their own words, on our public YouTube channel.
Straight answers about Data Science Course: Python, ML & Deep Learning
- Who should take this course?
- Complete beginner (18+); college students, graduates and working professionals. Age: 18+; college students, graduates and working professionals. It suits students targeting data science and analytics placements; professionals moving into data roles with a structured, honest path; analysts ready to add ml and deep learning to their toolkit.
- What will they learn and build?
- Data science hiring has grown sceptical: everyone lists the same libraries, so interviews now dig for the difference between importing scikit-learn and understanding what the model just did.
- How deeply are topics covered?
- The course runs 10-12 months (44-52 weeks) at 2 live classes/week + 4-6 hours practice, across 4 phases listed week by week in the syllabus below. A structured, well-paced curriculum taught step by step, with classwork in every session and problem sets after every class.
- Who teaches it?
- Modern Age Coders mentors, who teach the live classes themselves. You can watch them at work in the free class recordings before you decide.
- How do practice, feedback and assessment work?
- Monthly mixed reviews, cumulative phase exams, and a final exam that decides the certificate: real knowledge, real work. Doubt support between classes over WhatsApp, so you are never left stuck. A certificate you earn by passing the final exam, with a free retest after revision if needed.
- What does it cost?
- Three monthly plans: a group batch, a mini batch and 1-on-1. Prices are shown in your currency in the plans section. Monthly billing, cancel any time.
- When can classes take place?
- Live classes are scheduled around your week. Group batches meet at a fixed weekly slot; 1-on-1 students choose their own. International students are scheduled in their own timezone. Ask on WhatsApp for the current slots.
- Can I watch the teaching before deciding?
- Yes. Full recordings of real 13 and up classes are free to watch, free with a Google sign-in. These recordings show how we teach, not the exact syllabus of this course. Some classes are in English and some in Hindi; everyone follows at their own pace. Watch a class end to end, like you are in it. Sessions are interactive and each moment builds on the last.
- Can I enroll without a live demo?
- Yes. Choose a plan on this page and enroll directly; a booking or a demo is not required. A free live demo is optional, for anyone who wants to meet a mentor first. Outside India, our team confirms the plan and completes payment with you over WhatsApp, so allow a little time for that step.
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