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Statistical Thinking for Data Science and Analytics

Data Science for Executives,

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.

What you'll learn

  • Data collection, analysis and inference
  • Data classification to identify key traits and customers
  • Conditional Probability-How to judge the probability of an event, based on certain conditions
  • How to use Bayesian modeling and inference for forecasting and studying public opinion
  • Basics of Linear Regression
  • Data Visualization: How to create use data to create compelling graphics
  • Examples of Statistical Thinking
  • Numerical Data, Summary Statistics
  • From Population to Sampled Data
  • Different Types of Biases
  • Introduction to Probability
  • Introduction to Statistical Inference
  • Association and Dependence
  • Association and Causation
  • Conditional Probability and Bayes Rule
  • Simpsons Paradox, Confounding
  • Introduction to Linear Regression
  • Special Regression Models
  • 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
  • Bayesian inference: combining models and data in a forecasting problem
  • Bayesian hierarchical modeling for studying public opinion
  • Bayesian modeling for Big Data

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Rating 2.2 based on 18 ratings
Length 5 weeks
Effort 7 - 10 hours per week
Starts On Demand (Start anytime)
Cost $99
From Columbia University, ColumbiaX via edX
Instructors Eva Ascarza, James Curley, Andrew Gelman, Lauren Hannah, David Madigan, Tian Zheng
Download Videos On all desktop and mobile devices
Language English
Subjects Business Programming Data Science
Tags Business & Management Computer Science Data Analysis & Statistics Engineering

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What people are saying

bayesian section taught

Worth taking if only for the Bayesian section taught by Andrew Gelman.

robert ritz above

I totally agree with Robert Ritz above.

continue after completing

I was really looking forward to take all three classes in the series, but I decided not to continue after completing the first one.

download lecture videos

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).

grabbed random material

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.

heavy foreign accent

I can't read the 'blackboard' most of the time if I also want to read the subtitles, and the primary instructor has quite a heavy foreign accent.

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Rating 2.2 based on 18 ratings
Length 5 weeks
Effort 7 - 10 hours per week
Starts On Demand (Start anytime)
Cost $99
From Columbia University, ColumbiaX via edX
Instructors Eva Ascarza, James Curley, Andrew Gelman, Lauren Hannah, David Madigan, Tian Zheng
Download Videos On all desktop and mobile devices
Language English
Subjects Business Programming Data Science
Tags Business & Management Computer Science Data Analysis & Statistics Engineering

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