Data Cleansing and Analysis

This class uses R Programming to teach students how to load and read different types of data. Cleaning data and getting it prepared for analysis takes up a majority of the time of a data scientist. We’ll do hands-on exercises of typical data cleaning tasks and discuss how those tasks might vary based on the project. Once the data is prepared, we’ll do some basic exploration of data using R. When we analyze data as a data scientist, we are looking for actions that will help our business. In this course, we combine the theory of data analysis with R programming to give you a complete look at prepping data to be analyzed. We’ll also discuss reproducible research as a necessity in data science and practice creating reproducible research. For this course, you will need: R Programming with R, RStudio, and GitHub installed on your computer.

Course Outline:

  • Reading different kinds of data:  local files, excel files, xml files, json files, mysql, the web, and APIs
  • Cleaning data to create Tidy data
  • Creating variables to summarize and aggregate data
  • Subsetting and sorting data
  • Merging expressions
  • Working with dates
  • Creating reproducible research (R markdown; knitr)
  • Basic exploration
    • Graphs
    • ggplot2
    • rplots

Unique Value of Course:


Who Should Attend:

  • Chief Analytics Officer
  • Chief Data Officer
  • Senior Managers in Operations and IT
  • Business Process Managers
  • Business Process Analysts
  • Business Analysts
  • Business Architects
  • Project managers
  • Data Analyst
  • Data Scientist
  • Data Specialist
  • Data Stewards
  • Information Architects

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