Introduction to Computational Thinking and Data Science

Source: https://ocw.mit.edu/courses/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/

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Lecture 1: Introduction, Optimization Problems

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• Computer Models
• Optimization models
• Knapsack problem
• Brute force for optimization problems
• Greedy algorithm for optimization problems

Lecture 2: Optimization problems

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• Pros and cons of greedy algorithms
• Brute force through search trees
• Dynamic programming

Lecture 3: Graph-theoretic models

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• Graphs
• Shortest path in a graph
• Depth-first search in a graph
• Breadth-first search in a graph

Lecture 4: Stochastic Thinking

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• Uncertainty
• Stochastic processes
• Probability
• Random numbers
• Sample probability
• The Birthday Problem
• Simulation Models

Lecture 5: Random Walks

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• Random Walks
• Sanity checks

Lecture 6: Monte Carlo Simulation

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• Monte Carlo Simulations
• Inferential Statistics
• Confidence intervals
• Law of Large Numbers
• Gambler’s Fallacy
• Regression to the Mean
• Variance
• Empirical Rule
• Probability Distributions
• Probability Density Function
• Normal Distributions

Lecture 7: Confidence Intervals

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• Normal Distribution
• Probability Density Function
• Central Limit Theorem
• Sanity checks

Lecture 8: Sampling and standard error

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• Inferential Statistics
• Monte Carlo Simulations
• Sampling
• Confidence intervals
• Standard of the Error Mean
• Skew

Lecture 9: Understanding Experimental Data

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• Data
• Modelling a spring
• Objective Functions
• Least Squares Objective Function
• Linear regression
• Coefficient of Determination

Lecture 10: Understanding Experimental Data (Cont.)

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• Linear regression
• Testing a model
• Validation
• How to find the right model
• Cross Validation

Lecture 11: Introduction to Machine Learning

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• Linear regression
• Machine Learning
• Supervised-Unsupervised Learning
• Clustering
• Features
• Minkowski Metric (Distance)
• Model metrics

Lecture 12: Clustering

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(Pending)

Lecture 13: Classification

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(Pending)

Lecture 14: Classification and statistical sins

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(Pending)

Lecture 15: Statistical Sins and Wrap Up

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(Pending)