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.