SEARCH COURSES

Introduction to Computational Thinking and Data Science (MIT)

(14 Lectures Available)


S# Lecture Course Institute Discipline
1 Introduction and Optimization Problems Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
2 Graph-theoretic Models Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
3 Stochastic Thinking Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
4 Random Walks Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
5 Monte Carlo Simulation Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
6 Confidence Intervals Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
7 Sampling and Standard Error Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
8 Understanding Experimental Data Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
9 Understanding Experimental Data (cont.) Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
10 Introduction to Machine Learning Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
11 Clustering Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
12 Classification Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
13 Classification and Statistical Sins Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences
14 Statistical Sins and Wrap Up Introduction to Computational Thinking and Data Science (MIT) MIT Applied Sciences

of 1 14 Lectures Available.