ST101: Intro to Statistics (Udacity)

This is a short summary of Udacity's new offering: ST 101 - Introduction to Statistics.


Udacity's latest offering is something that interested me as soon as I saw the announcement. Since I'll be taking Mathematical Statistics I in the fall (for credit), the ability to freely review what I know about Statistics and learn some new concepts intrigued me. With Udacity's course offerings being 6-8 weeks, and ST101 starting this week (June 25), it fit very well into my schedule so as not to overload me when the fall semester begins.

I'm had time to review about half of the first week's offerings, which mostly focus on "What is Statistics?" and how to chart and analyze data. Sebastian Thrun begins with a fictional list of houses sold based on square footage and the sale price. From this he proceeds into a concise explanation of the basic, from linearity to scatterplots to various graphing styles. All of it is laced with Thrun's typical humor and obvious love of teaching. Having taken the Artificial Intelligence course last year with Thrun, I can clearly see some of the places where his online teaching style has evolved over the past few months. He's getting better at the intricacies of a field he is pioneering, and it shows.

I've posted a copy of the quasi-syllabus for the class below. If you're interested in following along, the class has just started, so there's still time to begin with playing catch up.

From Udacity: 

• Unit 1: Visualizing relationships in data

  Seeing relationships in data and predicting based on them; dealing with noise 

• Unit 2: Processes that generates data

  Random processes; counting, computing with sample spaces; conditional probability; Bayes Rule 

• Unit 3: Processes with a large number of events

  Normal distributions; the central limit theorem; adding random variables 

• Unit 4: Real data and distributions

  Sampling distributions; confidence intervals; hypothesis tests; outliers 

• Unit 5: Systematically understanding relationships

  Least squares;residuals; inference 

• Unit 6: Understanding more complex relationships

  Transformation; smoothing; regression for two or more variables, categorical variables 

• Unit 7: Where to go next

  Statistics vs machine learning; what to study next; where statistics is used
  Final exam