Statistical Thinking for Data Science and Analytics

Обучение бесплатное
Сертификация платная
11 месяцев
О курсе

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.

Statistical Thinking for Data Science and Analytics
Learn how statistics plays a central role in the data science approach.
Что Вы изучите?
  • 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
Andrew Gelman
Andrew Gelman
Professor of Statistics and Political Science Columbia University
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
David Madigan
Executive Vice President and Dean of Faculty of Arts and Sciences Columbia University
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
Assistant Professor in the Department of Statistics Columbia University
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
Assistant Professor of Marketing at Columbia Business School Columbia University
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).
James Curley
James Curley
Assistant Professor of Psychology Columbia University
Dr. Curley has very broad interests in behavioral development. He has conducted and published research at molecular, systems, organismal and evolutionary levels of analysis in both animals and humans. The focus of Dr. Curley’s lab at Columbia is on the development of social behavior. Dr. Curley is interested in how both inherited genetic variability and social experiences during development can shift individual differences in various aspects of social behavior and what the neuroendocrinological basis of these differences may be. He also researches the reliability and validity of social behavioral tests conducted in the laboratory and whether it is possible to utilize alternative statistical and methodological approaches to more appropriately assess social behavior. Dr Curley believes that it is critical to understand how the 'social brains' of humans and other animals have been differentially shaped by evolution and to acknowledge how this should better inform translational research.
Tian Zheng
Tian Zheng
Series Creator Columbia University
Tian Zheng is associate professor of Statistics at Columbia University. She obtained her PhD from Columbia in 2002. Her research is to develop novel methods and improve existing methods for exploring and analyzing interesting patterns in complex data from different application domains. Her current projects are in the fields of statistical genetics, bioinformatics and computational biology, feature selection and classification for high dimensional data, and network analysis. Especially, Dr. Zheng have been developing statistical and computational tools for high dimensional data, searching for genetic interactions associated with complex human disorders, quantifying social structure and studying hard-to-reach populations using survey questions, with more than 40 peer-reviewed publications in journals including JASA, AOAS and PNAS. Her work was recognized with the 2008 Outstanding Statistical Application Award from the American Statistical Association, The Mitchell Prize from ISBA and a Google research award. She is on the editorial board of Statistical Analysis and Data Mining and Frontier in Genetics. She was Associate Editor for JASA from 2007 to 2013.
Эта платформа предоставляет все курсы бесплатно. Авторами выступают топовые университеты и корпорации, которые стараются удерживать стандарты качества. За несоблюдение дедлайнов, невыполнение домашнего задания студенты теряют баллы. Как и в других платформах, лекционные видео чередуются с практическими заданиями. Обучение проводится на английском, китайском, испанском, французском и хинди.