Data Science Course

Weekly Syllabus

• Weeks Module 1

  Introduction to Data Science: Roles, lifecycle, applications, and tools overview.

• Weeks Module 2

  Programming Foundations: Python basics, Control structures, NumPy, Pandas, and working with files & APIs.

• Weeks Module 3

  Mathematics for Data Science: Linear algebra, probability, statistics, and calculus basics.

• Weeks Module 4

  Data Collection & Storage: SQL, NoSQL, APIs, web scraping, and data warehousing.

• Weeks Module 5

  Data Cleaning & Preparation: Handling missing values, outliers, feature scaling, and data pipelines.

• Weeks Module 6

  Exploratory Data Analysis (EDA): Statistics, distributions, correlation, and pattern discovery with Matplotlib & Seaborn.

• Weeks Module 7

  Data Visualization & Storytelling: Dashboards with Tableau/Power BI and presenting insights.

• Weeks Module 8

  Machine Learning Fundamentals: Supervised vs unsupervised, model training/evaluation, and Scikit-learn workflow.

• Weeks Module 9

  Supervised Learning: Regression, decision trees, random forest, SVM, KNN, and evaluation metrics.

• Weeks Module 10

  Unsupervised Learning: Clustering, dimensionality reduction (PCA), and anomaly detection.

• Weeks Module 11

  Advanced Machine Learning: Ensemble methods, gradient boosting, model tuning, and feature selection.

• Weeks Module 12

  Deep Learning: Neural networks, TensorFlow/PyTorch, CNNs, RNNs & LSTMs.

• Weeks Module 13

  Natural Language Processing (NLP): Text preprocessing, embeddings, sentiment analysis, and Transformers.

• Weeks Module 14

  Time Series Analysis: Forecasting models including ARIMA & Prophet.

• Weeks Module 15

  Big Data & Cloud: Hadoop, Spark, AWS, Azure, GCP, and MLOps basics.

• Weeks Module 16

  Model Deployment & MLOps: APIs with Flask/FastAPI, CI/CD, and monitoring.

• Weeks Module 17

  Ethics, Privacy & Responsible AI: Bias, fairness, transparency, and ethical decision-making.

• Weeks Module 18

  Capstone Project: End-to-end real-world data science project.