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Columbia University

Statistical Thinking for Data Science and Analytics

Columbia University via edX

This course may be unavailable.


This course will soon be retired. Last day to enroll is July 31st, 2023 at 00:00 UTC.

This statistics and data analysis course will pave the statistical foundation for our discussion on data science.

You will learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions and construct models for predicting future trends from data.


Week 1 – Introduction to Data Science

Week 2 – Statistical Thinking

  • Examples of Statistical Thinking
  • Numerical Data, Summary Statistics
  • From Population to Sampled Data
  • Different Types of Biases
  • Introduction to Probability
  • Introduction to Statistical Inference

Week 3 – Statistical Thinking 2

  • Association and Dependence
  • Association and Causation
  • Conditional Probability and Bayes Rule
  • Simpsons Paradox, Confounding
  • Introduction to Linear Regression
  • Special Regression Models

Week 4 – Exploratory Data Analysis and Visualization

  • Goals of statistical graphics and data visualization
  • Graphs of Data
  • Graphs of Fitted Models
  • Graphs to Check Fitted Models
  • What makes a good graph?
  • Principles of graphics

Week 5 – Introduction to Bayesian Modeling

  • Bayesian inference: combining models and data in a forecasting problem
  • Bayesian hierarchical modeling for studying public opinion
  • Bayesian modeling for Big Data

Taught by

Eva Ascarza, James Curley, Andrew Gelman , Lauren Hannah, David Madigan and Tian Zheng


2.2 rating, based on 19 Class Central reviews

Start your review of Statistical Thinking for Data Science and Analytics

  • It's very unclear who this course is supposed to be for. It skims shallowly into some topics in the lectures, then dunks you into a long technical pdf that you have to read to answer the "quiz" questions. Luckily it's easy (if you're a native Englis…
  • Ericdo1810
    Honestly, I took this course out of curiosity. The name of the course is so catchy, I couldn't resist not to enroll. When I watch the first videos, I was blown away. The videos were so good! They really did a good job conveying the topic of statisti…
  • Nan Halberg
    This class is a mess - we're unable to download lecture videos and transcripts, the powerpoint slides are not available, the quiz policy has changed midstream (you can now retake once instead of zero retakes). The quizzes are confusing and no feedb…
  • Anonymous
    I join the ranks of those here who give this course a negative assessment. (and I wish I had browsed the reviews before enrolling...) This course is just a learning-methodologic nigthmare. I am not expecting an in-depth learning experience, it's a…
  • Anonymous
    I have taken more than 10 MOOC, and this one is the worst one and really beyond awful. I was really looking forward to take all three classes in the series, but I decided not to continue after completing the first one. I think that the instructors didn't make much effort to design this class, instead they just grabbed random material from their own on-campus classes. I just want my $100 back.
  • Anonymous
    I thought the lectures were useful for someone that is new to data science. I'm a bit surprised by so many negative reviews. I didn't think they were all that bad, but I am also a newbie so don't know what a "great" course looks like.
  • Anonymous
    I totally agree with Robert Ritz above. I already have a list of topics that I want to research as soon as I finish the course. This is an introductory course, paving the way to more in depth studying. Indeed you get what you put into this. And if you don't find these articles the slightest interesting maybe you are doing the wrong course.
  • Anonymous
    Not a single problem set and the main lecturer is unintelligible. I'm extremely disappointed with this course. Will have to finish unfortunately to get that Microsoft certificate. I would rate it zero if I could.
  • A introductory course to statistic thinking, nothing too fancy and in fact, too easy to actually learn the real implication.

    Regardless, throw out some topics that you can further research into.
  • Anonymous
    Far too light of a touch in general. Would really benefit from being longer, more in depth and with more practical / real world examples and projects. Worth taking if only for the Bayesian section taught by Andrew Gelman.
  • Profile image for Robert Ritz
    Robert Ritz
    Others have said many criticisms of this course. Many of them are correct, but as is so often with learning, you get what out what you put into it. Is the class designed to turn you into a pro data scientist in 5 weeks. Absolutely not. Does it jump…
  • Martin H
    looks fragmented. why do it when you're not committed to doing a good job?
    "This class is a mess - we're unable to download lecture videos and transcripts, the powerpoint slides are not available, the quiz policy has changed midstream (you can now retake once instead of zero retakes). The quizzes are confusing and no feedback is available giving reasons for right and wrong answers. " agreed.
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    Donghyun Kang
  • Anonymous
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    Alex Ivanov
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    Sonsoles López

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