
National Student Data Corps Video Library
Check out these videos on data science topics based on IBM’s OpenDS4All curriculum and presented by Columbia University Master’s students.
Video Library Contents
Introduction to Data Science, with Yucen Wang
Data Acquisition and Wrangling, with Renyin Zhang
Data Science Ethics, with Abhishek Sinha and Varalika Mahajan
Introduction to Data Cleaning, with Stephanie Guo
Introduction to R, with Dashansh Prajapati
Introduction to Structured Query Language (SQL), with Jingnan Qi
Supervised Machine Learning, with Tomislav Galjanic
AI Explainability, with Rahulraj Singh
AI Fairness, with Rahulraj Singh
Introduction to Data Science
With Yucen Wang
Introduction to Data Science – Part 1 (Yucen Wang)
Introduction to Data Science – Part 2 (Yucen Wang)
Data Acquisition and Wrangling
With Renyin Zhang
Data Acquisition and Wrangling – Part 1 (Renyin Zhang)
Data Acquisition and Wrangling – Part 2 (Renyin Zhang)
Data Acquisition and Wrangling – Part 3 (Renyin Zhang)
Data Science Ethics
With Abhishek Sinha
Data Science Ethics – Part 1
(Abhishek Sinha)
Data Science Ethics – Part 2
(Varalika Mahajan)
Introduction to Data Cleaning
With Stephanie Guo
Introduction to Data Cleaning (Stephanie Guo)
An Introduction to R
With Dashansh Prajapati
An Introduction to R (Dashansh Prajapati)
An Introduction to Structured Query Language (SQL)
With Jingnan Qi
Introduction to Structured Query Language (SQL) – Part 1 (Jingnan Qi)
Introduction to Structured Query Language (SQL) – Part 4 (Jingnan Qi)
Introduction to Structured Query Language (SQL) – Part 2 (Jingnan Qi)
Introduction to Structured Query Language (SQL) – Part 5 (Jingnan Qi)
Introduction to Structured Query Language (SQL) – Part 3 (Jingnan Qi)
Supervised Machine Learning
with Tomislav Galjanic
Introduction (Tomislav Galjanic)
Random Forest (Tomislav Galjanic)
Introduction to Decision Trees (Tomislav Galjanic)
Python Code (Tomislav Galjanic)
Decision Trees (Tomislav Galjanic)
AI Explainability
With Rahulraj Singh
AI Explainability – Part 1 (Rahulraj Singh)
AI Explainability – Part 2 (Rahulraj Singh)
AI Fairness
With Rahulraj Singh
AI Fairness – Part 1 (Rahulraj Singh)
AI Fairness – Part 2 (Rahulraj Singh)
Data Science Use Cases
From the AI for Social Good Fall 2020 Symposium
Measuring and Visualizing Social Distancing Using Deep Learning and 3D Computer Vision (Bilal Abdulrahman, Zhigang Zhu)
Robust Lock-Down Optimization for COVID-19 Policy Guidance
(Ankit Bhardwaj, Han-Ching Ou)
Asymptotic Cross-Entropy Weighting and Guided-Loss in Supervised Hierarchical Setting using Deep Attention Network
Health Care Misinformation: An artificial intelligence challenge for low-resource languages (Sarah Luger, Christopher M. Homan)
Clean Water: How the AI community can contribute to accessing water sources in developing countries
(Karthik Dusi)
A Two-Step Framework for Parkinson’s Disease Classification: using Multiple One-Way ANOVA on Speech Features and Decision Trees (Gaurang Prasad, Thilanka Munasinghe)
Socioeconomic and Geographic Variations that Impact the Spread of Malaria
(Thilanka Munasinghe)
Artificial Intelligence and Resource Allocation in Health Care: The Process-Outcome Divide in Perspectives on Moral Decision-Making (Sonia Jawaid Shaikh)
Check out the NSDC Educator Central and NSDC Learner Central for more data science resources.
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