National Student Data Corps Video Library

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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

Data Science Use Cases

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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