Skip to main content

Introduction to Data Science with R
Enrollment is Closed

This is a 10 week course in Statistics for individuals with some prior programming and mathematics background. This course covers statistical concepts and techniques needed for business applications. In this course statistical concepts and methods of data analysis will be taught in a practical way using R. Students will use analytical skills and statistical tools for building predictive models for decision making.

Enrollment is Closed

About This Course

Course Contents

  • Exploratory Data Analysis (EDA)
  • Sampling and designing experiments
  • Confidence intervals and significance test.
  • Mean, Variance, and other statistics.
  • Hypothesis Testing.
  • Linear Regression.
  • Analysis of Variance (ANOVA).

Course Objectives: At the completion of the course, students will be able to do the following:

  • Be able to use R to implement all the methods that are taught in the class.
  • Be able to write R scripts to analyze data sets including creating necessary summary statistics.
  • Be able to run R code to create linear models (regression) and understand the output.
  • Be able to perform hypothesis tests .
  • Understand model estimation concepts and margin of error applied to analysis of a data set.
  • Requirements

    Ability to do basic algebraic manipulations, some basic calculus and some programming experience. In particular you must be able to download and install R and RStudio on your computer

    Text Books

    There will be two texts for the class:

    • ¨Introductory R: A Beginner’s Guide to Data Visualisation, Statistical Analysis and Programming in R ¨by Richard Knell. This e-book is available from either amazon from google play. The cost of the book is $ 5.00
    • R for data science ¨by Grolemnund and Hadley. It is available on line at http://r4ds.had.co.nz/ . Hard copies can also be purchased. It is not an expensive book and you may prefer to own your own copy.
    Dr Adam Ginensky
    Instructor
    Meet the Instructor

    Dr. Ginensky has a M.S. and a Ph.D. in mathematics, both from the University of Chicago. He is currently teaching at the University of Chicago in their Master in Predictive Analytics Program. Prior to 2008, Adam worked as a market maker at the Chicago Mercantile Exchange and was involved in the mathematics of option pricing, but primarily was a floor trader. He has gave a number of talks in the University of Chicago Mathematical Finance Program. After 2008 he worked as a quantitative analyst for a proprietary trading company where he used Matlab and R (as well as SQL and various extensions) to perform data mining and statistical analysis of various financial data sets. His responsibilities included analyzing large (tick) data sets, performing statistical modeling of various time series of trading data, and writing the software packages to implement these goals. It was at this point that he became interested in applying statistics in other fields as well as finance. His current interests include both supervised and unsupervised learning as well as time series analysis. He is also currently exploring applications of algebraic geometry to statistics (algebraic statistics). In all aspects of his research and activity, he is fascinated by the practical applications of the theoretical ideas.

    Enrollment is Closed