/Open-source-Data-Science-couch

A structured 3-year curriculum for data science, covering foundational, intermediate, and advanced topics

GNU General Public License v3.0GPL-3.0

A structured 3-year curriculum for data science, covering foundational, intermediate, and advanced topics:

Year 1: Foundations of Data Science

Semester 1: Introduction to Data Science

  1. Introduction to Data Science - Coursera
  2. Python for Data Science - edX
  3. Mathematics for Machine Learning - Coursera

Semester 2: Data Wrangling and Visualization

  1. Data Cleaning and Transformation - DataCamp
  2. Data Visualization with Matplotlib and Seaborn - DataCamp
  3. Exploratory Data Analysis - Udacity

Semester 3: Statistical Foundations

  1. Statistics for Data Science - Coursera
  2. Probability and Statistics - Khan Academy
  3. Bayesian Statistics - Coursera

Year 2: Intermediate Data Science

Semester 4: Machine Learning Basics

  1. Machine Learning by Andrew Ng - Coursera
  2. Applied Machine Learning - DataCamp
  3. Feature Engineering for Machine Learning - Udemy

Semester 5: Big Data and Cloud Computing

  1. Big Data Essentials - Coursera
  2. Introduction to Cloud Computing - edX
  3. Apache Spark - DataCamp

Semester 6: Deep Learning and Neural Networks

  1. Deep Learning Specialization - Coursera
  2. Convolutional Neural Networks for Visual Recognition - Stanford CS231n
  3. Natural Language Processing with Deep Learning - Coursera

Year 3: Advanced Data Science

Semester 7: Advanced Machine Learning

  1. Advanced Machine Learning - Coursera
  2. Reinforcement Learning - Udacity
  3. Time Series Analysis and Forecasting - DataCamp

Semester 8: Data Science Applications

  1. Data Science Capstone Project - Real-world application of data science skills
  2. Data Ethics and Privacy - Coursera
  3. Data Science in Industry - Internships and practical experience

Semester 9: Special Topics and Electives

  1. AI for Everyone - Coursera
  2. Blockchain and Data Science - edX
  3. Data Science Research - Collaborative research projects or thesis

Additional Resources:

  • Books: "Python for Data Analysis" by Wes McKinney, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron, "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
  • Data Science Competitions: Participate in Kaggle competitions to apply and sharpen skills.
  • Data Science Blogs and Podcasts: Stay updated with the latest trends and discussions in the data science community through platforms like Towards Data Science and Data Skeptic podcast.

This curriculum provides a comprehensive pathway to becoming proficient in data science, combining theoretical foundations with hands-on practical experience and advanced specialization. Adjustments can be made based on specific interests or career goals within data science.