Week 1: Introduction to Data Science

• Reading: Nelson, Daniel. July 21, 2020. “What is Data Science.” Unite AI.
• YouTube: Brunton, Steve. June 6, 2019. “Introduction to Data Science.” University of Washington Recorded Lectures. There are several videos in this series. After listening to this introduction to Data Science lecture, you can pick and choose which other videos you are interested in watching. Videos range from 12 minutes to 30 minutes.

Week 2: Data Science Process

• Reading: Lau, Cher Han, Dr. January 3, 2019. “5 Steps of a Data Science Project Lifecycle.” Towards data science 
• Blog Post: Fritzler, Alan. May 12, 2018. “Ethical Checklist for Data Science” Data Science for Social Good

Week 3: Big Data

• Reading: Foote, Keith D. December 14, 2017. “A Brief History of Big Data” Dataversity

Week 4: Managing Data

• Reading: Wani, Mudasir Ahmad, and Suraiya Jabin. “Big data: issues, challenges, and techniques in business intelligence.” In Big data Analytics, pp. 613-628. Springer, Singapore, 2018.
• YouTube: “Big Data Tools and Technologies” Simplilearn November 2, 2017.

Week 5: Statistics for Data Science

• Reading: ‘Malik, Farhad. February 29, 2020.  Understanding Probability and Statistics” Towards Data Science
• YouTube: Hill, Adrean. October 3, 2018. “Regression: Crash Course Statistics #32” CrashCourse.

Week 6: Probability for Data Science

• Reading: Zhang, Zixuan. August 14, 2019. “Naïve Bayes Explained.” Towards Data Science
• YouTube:
  • Hill, Adriene. July 25, 2018. “You know I am all about the Bayes: Crash Course Statistics #24”
  • Hill, Adriene. August 1, 2018. “Bayes in science and everyday life: Crash Course Statistics #25”

Week 7: Natural Language Processing (NLP)

• Readings:
  • Some, Kamalika. July 28, 2020. “Understanding the blackbox of Natural Language Processing (NLP).” Analytics Insight
  • Banerjee, Dibyendu. April 14, 2020. “Natural Language Processing (NLP) Simplified: A Step-by-step Guide” DataScience Foundation
• TED-Ed: Papachimonas, Ioannis. October 26, 2015. “How computers translate human language.”
• TED Talks: Li, Fei-Fei. March 2015. “How we are teaching computers to understand pictures.”

Week 8 and 9: Data Mining

• Readings:
  • Introduction to Data Mining in Principles of Data mining by Max Bramer
  • Luenendonk, Martin. September 23, 2019. “Comprehensive Guide on Data Mining (and Data Mining Techniques) Cleverism.
• YouTube: Sullivan, David G. November 9, 2017. “Data Mining Fundamentals.”

Week 10 and 11: Machine Learning

• Readings:
  • Nabi, Javaid. August 15, 2018. “Machine Learning – Fundamentals.” Medium
  • Edwards, Gavin. November 18, 2018. “Machine Learning An Introduction.” Medium 
  •  Shaw, Jonathan. February 2019. “Artificial Intelligence and Ethics.” Harvard Magazine
  •  Budd, Chris. December 12, 2018. “Machine learning: Is it ethical?” +plus Magazine
• YouTube: Tan-Holmes, Jade. November 23, 2017. “Machine Learning Explained in 5 Minutes.”

Week 12: Machine Learning Models

• Readings:
  • Dibble, Megan. “An explanation of machine learning models even you could understand.” Neural Human-Centric AI News and Analysis
  • Goyal, Kechit. April 28, 2020. “Top 5 Machine Learning Models Explained for Beginners.” upGrad Blog
• YouTube: Curry, Caleb. December 30, 2017. “Machine Learning Tutorial 3 – Intro to Models.”

Week 13: Data Visualization, Techniques and Tools

• Readings:
  • “What is Data Visualization and Why is It Important.” October 29, 2010. import.io
  • “How Data Visualizations Impact Business Growth.” May 14, 2015. import.io
• YouTube:
  • Henley, Dale. October 25, 2018. “What is Data Visualization.”
  • Hill, Adrienne. February 21, 2018. “Charts are like pasta – data visualization Part 1: Crash Course Statistics #5.”
  • Hill, Adrienne. February 28, 2018. “Plots, Outliers, and Justin Timberlake: Data Visualization Part 2: crash Course Statistics #6.”

Week 14: Ethics

• Readings:
  • Crews, Alfred Jr. August 10, 2020. “Advanced AI Technologies Present Ethical Challenges – Thought Leaders.” United AI
  • Saucedo, Alejandro. August 13, 2020. “The top three risks posed by AI, and how to safeguard against them.” ITProPortal
  • Leprince-Ringuet, Daphne. July 27, 2020. “Data scientists are used to making up the rules. Now they’re getting some of their own to follow.” ZDNet
  • Cunningham, William A., Jay J. Van Bavel, Leah H. Sommeryvilled.  August 5, 2020. “Letters to Young Scientists: How to be an ethical scientist.” AAAS Science

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