Data Cleaning (Missing Values, Outliers, Duplicates)

Feature Engineering & Transformation

Statistical Summaries & EDA Techniques

Data Visualization (Matplotlib, Seaborn, ggplot2)

Descriptive vs. Inferential Statistics

Probability Distributions (Normal, Binomial, Poisson)

Hypothesis Testing (t-tests, ANOVA, Chi-square)

Bayesian Statistics Basics

Relational Databases (MySQL, PostgreSQL)

Advanced SQL (Joins, Subqueries, Window Functions)

NoSQL Databases (MongoDB, Cassandra)

Query Optimization

Supervised Learning:

• Linear & Logistic Regression

• Decision Trees, Random Forest, XGBoost

• SVM, k-NN, Naive Bayes

Unsupervised Learning:

• Clustering (K-Means, Hierarchical, DBSCAN)

• Dimensionality Reduction (PCA, t-SNE)

Model Evaluation (Accuracy, Precision, Recall, ROC-AUC)

Introduction to Neural Networks

TensorFlow & PyTorch Basics

CNNs (Image Processing)

RNNs & LSTMs (Time Series & NLP)

Transfer Learning (ResNet, BERT)

Text Preprocessing (Tokenization, Stemming, Lemmatization)

Word Embeddings (Word2Vec, GloVe)

Sentiment Analysis & Topic Modeling

Transformers & GPT Models

Hadoop & Spark (PySpark)

AWS SageMaker, Google AI Platform

Data Pipelines (Airflow, Kafka)

Flask/FastAPI for Model APIs

Docker & Kubernetes for Deployment

CI/CD in Machine Learning

Monitoring ML Models in Production

End-to-End Data Science Project (Real Dataset)

Business Problem → Data Collection → Modeling → Deployment

Presenting Insights to Stakeholders