Python Basics (Variables, Data Types, Functions)

  Control Flow Statements (Loops, Conditions)

  Data Structures (Lists, Tuples, Dictionaries, Sets)

  Working with Libraries: NumPy, Pandas, Matplotlib, Seaborn

  Handling Missing Data

  Data Cleaning Techniques

  Data Transformation and Feature Engineering

  Normalization and Standardization

  Understanding Data Distributions

  Data Visualization using Matplotlib and Seaborn

  Detecting Outliers and Anomalies

  Correlation Analysis and Hypothesis Testing

  Descriptive and Inferential Statistics

  Probability Distributions

  Hypothesis Testing (T-tests, ANOVA)

  Correlation and Regression Analysis

  Introduction to Machine Learning

  Supervised vs Unsupervised Learning

  Model Training and Evaluation Metrics

  Overfitting and Underfitting

  Linear Regression and Logistic Regression

  Decision Trees and Random Forests

  Support Vector Machines (SVM)

  Gradient Boosting (XGBoost, LightGBM)

  Clustering (K-Means, Hierarchical, DBSCAN)

  Principal Component Analysis (PCA)

  Anomaly Detection

  Basics of Neural Networks

  Introduction to TensorFlow and Keras

  Convolutional Neural Networks (CNN)

  Recurrent Neural Networks (RNN)