Statistical Thinking for Data Science and Analytics Bootcamp

This facilitated Bootcamp enables you to complete the standard Statistical Thinking for Data Science and Analytics course in less time! Rapidly learn how to use effectively collect, analyze and derive conclusions from data.
• Live Instructor Q&A Sessions
• Learner Success Coach support
• Peer Learning Community
• Career Guidance with an Experienced Data Scientist

4 Weeks 8 - 12 Hours per week recommended
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Please select the start dates for your courses below.

Scheduled Start:

What is a bootcamp?

Our facilitated bootcamps focus on rapid skill acquisition by progressing you through a standard course on an accelerated schedule with peers who are committed to progressing on pace. Our bootcamps include:

  • Live kick-off event
  • Instructor facilitated Q&A for expert feedback and coaching
  • Learner Success Support: welcome call, advising sessions, personalized pace reminders
  • 24/7 help desk

About This Course:

In this bootcamp 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 course can be used towards completion of a Professional Certificate in Data Science for Executives.

What You Will 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

Meet Your Instructors:

Andrew Gelman

Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.
David Madigan - Pearson Advance

David Madigan

David Madigan received a bachelor’s degree in Mathematical Sciences and a Ph.D. in Statistics, both from Trinity College Dublin. He has previously worked for AT&T Inc., Soliloquy Inc., the University of Washington, Rutgers University, and SkillSoft, Inc. He has over 100 publications in such areas as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He is an elected Fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He recently completed a term as Editor-in-Chief of Statistical Science.

Lauren Hannah

Lauren Hannah is an Assistant Professor in the Department of Statistics at Columbia University. Dr. Hannah received a Ph.D. in Operations Research and Financial Engineering from Princeton University, and an A.B. in Classics, again from Princeton University. After completing her Ph.D., Dr. Hannah completed a postdoc at Duke in the Statistical Science Department. Her interests include machine learning, Bayesian statistics, and energy applications.

Eva Ascarza

Eva Ascarza is an Assistant Professor of Marketing at Columbia Business School. She is a marketing modeler who uses tools from statistics and economics to answer marketing questions. Her main research areas are customer analytics and pricing in the context of subscription businesses. She specializes in understanding and predicting changes in customer behavior, such as customer retention and usage. Another stream of her research focuses on developing statistical methodologies to be used by marketing practitioners. She received her PhD from London Business School (UK) and a MS in Economics and Finance from Universidad de Navarra (Spain).
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Duration

4 Weeks

Learning Partner

Columbia University

Pacing

Instructor-paced

Program Type

Bootcamp