Statistical Thinking for Data Science and Analytics

About this course

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.

This is the first course in the three-part Data Science and Analytics XSeries.


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

View Course Syllabus

Meet the instructors

  • bio for Eva Ascarza

    Eva Ascarza

    Assistant Professor of Marketing at Columbia Business SchoolColumbia University

  • bio for James Curley

    James Curley

    Assistant Professor of PsychologyColumbia University

  • bio for Andrew Gelman

    Andrew Gelman

    Professor of Statistics and Political ScienceColumbia University

  • bio for Lauren Hannah

    Lauren Hannah

    Assistant Professor in the Department of StatisticsColumbia University

  • bio for David Madigan

    David Madigan

    Executive Vice President and Dean of Faculty of Arts and SciencesColumbia University

  • bio for Tian Zheng

    Tian Zheng

    Series CreatorColumbia University

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