MITx-6.00.2x

Introduction to Computational Thinking and Data Science

About this course

6.00.2x will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving . This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write a program that will simulate a robot vacuum cleaning a room or will model the population dynamics of viruses replicating and drug treatments in a patient's body.

Topics covered include:

*Advanced programming in Python 3

*Knapsack problem, Graphs and graph optimization

*Dynamic programming

*Plotting with the pylab package

*Random walks

*Probability, Distributions

*Monte Carlo simulations

*Curve fitting

*Statistical fallacies

What you'll learn

*Plotting with the pylab package

*Stochastic programming and statistical thinking

*Monte Carlo simulations

Course Syllabus

Lecture 1 – Optimization and Knapsack Problem: • Computational models • Intro to optimization • 0/1 Knapsack Problem • Greedy solutions

Lecture 2 – Decision Trees and Dynamic Programming: • Decision tree solution to knapsack • Dynamic programming and knapsack • Divide and conquer

Lecture 3 – Graphs: • Graph problems • Shortest path • Depth first search • Breadth first search

Lecture 4 – Plotting: • Visualizing Results • Overlapping Displays • Adding More Documentation • Changing Data Display • An Example

Lecture 5 – Stochastic Thinking: • Rolling a Die • Random walks

Lecture 6 – Random Walks: • Drunk walk • Biased random walks • Treacherous fields

Lecture 7 – Inferential Statistics: • Probabilities • Confidence intervals

Lecture 8 – Monte Carlo Simulations:

Lecture 9 – Monte Carlo Simulations: • Sampling • Standard error

Lecture 10 – Experimental Data: • Errors in Experimental Observations • Curve Fitting

Lecture 11 – Experimental Data: • Goodness of Fit • Using a Model for Predictions

Lecture 12 – Machine Learning: • Feature Vectors • Distance Metrics • Clustering

Lecture 13 – Statistical Fallacies • Misusing Statistics • Garbage In Garbage Out • Data Enhancement