🎓 COMPLETE DATA SCIENCE CURRICULUM

📊 Data Science Course Hub

From zero programming knowledge to building real machine learning models. Every concept explained like you're 5 years old!

📈 Course Progress

90% Complete

Python, Data Structures, Math, Statistics, Linear & Logistic Regression, Decision Trees, Deep Learning, NLP, and A/B Testing are ready!

📥 Download Course Datasets

Every lesson that uses data has a dataset you can download. Save the file in the same folder as your script or notebook so pd.read_csv("filename.csv") works.

View & download all datasets Pro

✅ READY
🐍

Module 1: Python Basics

Start here if you've never programmed before! Learn variables, data types, strings, loops, and conditions.

Python Fundamentals (Start Here!)
In this lesson
     Data Structures (Lists, Tuples, Sets, Dicts)
    In this lesson
       Functions (Coming Soon)
      ✅ READY
      📐

      Module 2: Math for Data Science

      Don't worry, we make math easy! Learn mean, median, variance, percentiles, and normalization.

      Math Foundations (Super Simple!)
      In this lesson
        ✅ READY
        📊

        Module 3: Statistics

        Statistics is just asking questions about data! Learn normal distribution, correlation, Chi-Square, and T-Tests.

        Statistics Foundations
        In this lesson
           Missing Values & OutliersPro
          In this lesson
             Hypothesis Testing Deep DivePro
            In this lesson
              ✅ READY
              🤖

              Module 4: Machine Learning Basics

              Your first ML algorithms! Linear & Logistic Regression, kNN, and Support Vector Machines.

              Linear Regression (Predict Numbers)Pro
              In this lesson
                 Bias, Variance & Gradient DescentPro
                In this lesson
                   Logistic Regression (Classification)Pro
                  In this lesson
                     k-Nearest Neighbors (kNN)Pro
                    In this lesson
                    • Classify by voting among K nearest neighbors
                    • Distance metrics, scaling, choosing K
                    • Full Python walkthrough
                     Support Vector Machines (SVM)Pro
                    In this lesson
                    • Maximum margin classifier with kernel trick
                    • Hard vs soft margin, C and gamma tuning
                    • Complete Python walkthrough with GridSearchCV
                     SVM Code Walkthrough (Line by Line)Pro
                    In this lesson
                    • Bank customer churn prediction with SVM
                    • EDA, feature engineering, scaling
                    • GridSearchCV with interactive heatmap
                    ✅ READY
                    🌳

                    Module 5: Advanced ML

                    Decision Trees, Random Forests, Boosting algorithms, and Feature Engineering techniques.

                    Decision Trees & Random ForestsPro
                    In this lesson
                       Random Forest Code Walkthrough (Car Evaluation)Pro
                      In this lesson
                         Boosting (XGBoost, AdaBoost)Pro
                        In this lesson
                           Feature Engineering (Intro below)
                          What you’ll learn
                          • Creating new columns from existing ones (e.g. ratios, bins, interactions)
                          • When and why it improves models
                          📌 Feature Engineering in one sentence: It means building new, useful inputs (features) from your raw data—e.g. "revenue per visit" from revenue and visits, or binning age into groups—so the model has better signals to learn from. Full lesson coming soon.
                          ✅ READY
                          🎯

                          Module 6: Clustering & Unsupervised

                          Find patterns without labels! K-Means, Hierarchical Clustering, and PCA dimensionality reduction.

                          Clustering (K-Means, DBSCAN, Hierarchical)Pro
                          In this lesson
                             K-Means Code Walkthrough (Line by Line)Pro
                            In this lesson
                            • Every line of the K-Means notebook explained
                            • Hotel Reservations dataset
                            • Scaling, encoding, elbow method
                             PCA (Principal Component Analysis) (Intro below)
                            What you’ll learn
                            • Reducing many columns to fewer “summary” columns
                            • When to use it (visualization, noise reduction)
                            📌 PCA in one sentence: PCA finds a small set of new “summary” directions (principal components) that capture most of the variation in your data—so you can reduce 100 columns to a few for visualization or faster modeling, with minimal information loss. Full lesson coming soon.
                            ✅ READY
                            🧠

                            Module 7: Deep Learning

                            Neural Networks, CNNs for images, and introduction to TensorFlow/Keras.

                            Neural Network Basics (Complete Guide)Pro
                            In this lesson
                               CNNs & Time Series (MNIST, LSTM)Pro
                              In this lesson
                                ✅ READY
                                💬

                                Module 8: Natural Language Processing

                                Work with text data! Tokenization, TF-IDF, Sentiment Analysis, and more.

                                NLP Basics (Complete Guide)Pro
                                In this lesson
                                   NLP Data Cleansing & ApplicationsPro
                                  In this lesson
                                    ✅ READY
                                    🧪

                                    Module 9: Applied Data Science

                                    Real-world applications: A/B Testing, Market Basket Analysis, Time Series.

                                    A/B Testing (Complete Guide)Pro
                                    In this lesson
                                       Market Basket AnalysisPro
                                      In this lesson
                                         Time Series (in CNNs & Time Series)Pro
                                        In this lesson
                                          ✅ READY
                                          🚀

                                          Module 10: Deployment & Advanced

                                          Explainable AI, Gen AI intro, and deployment concepts (AWS, ML project structure).

                                          Explainable AI (XAI)Pro
                                          In this lesson
                                             Introduction to Gen AIPro
                                            In this lesson
                                               Deploy on AWS - Coming

                                              📚 Content based on comprehensive Data Science curriculum

                                              Created by Fakhruddin Khambaty