Welcome to the Advanced Data Science course! This page provides an overview of the course content, objectives, and structure. If you are interested in learning more about data science and its applications, we recommend checking out our Introduction to Data Science course.

Course Objectives

  • Gain a deep understanding of advanced data science concepts and techniques.
  • Learn to apply machine learning algorithms to real-world problems.
  • Develop skills in data visualization and analysis.
  • Explore the ethical considerations and societal impact of data science.

Course Structure

  • Week 1: Introduction to Advanced Data Science

    • Overview of advanced data science concepts
    • Introduction to machine learning algorithms

  • Week 2: Supervised Learning

    • Linear regression
    • Logistic regression
    • Decision trees and random forests

  • Week 3: Unsupervised Learning

    • Clustering algorithms
    • Dimensionality reduction techniques

  • Week 4: Deep Learning

    • Introduction to neural networks
    • Convolutional neural networks (CNNs)
    • Recurrent neural networks (RNNs)

  • Week 5: Data Visualization and Analysis

    • Python libraries for data visualization
    • Techniques for exploratory data analysis

  • Week 6: Advanced Topics

    • Time series analysis
    • Natural language processing
    • Reinforcement learning

  • Week 7: Capstone Project

    • Apply the knowledge gained in the course to a real-world problem
    • Present the project to the class

Learning Resources

Data Science