Nearly every aspect of business is influenced by data analytics. This course from the University of Illinois at Urbana-Champaign equips students with essential data processing skills using R. The comprehensive curriculum delves into the human skills gap and its relevance to various business settings, emphasizing the interplay between business principles and data analytics.
The course covers topics such as the data analytic language R, data preparation for analytic tools, business analytic workflow, delegation, control, feasibility, and methods for communicating data analytic results. Students will also gain experience using RStudio, an integrated development environment, to simplify coding with R. The practical nature of the course allows learners to explore examples of business problems that can be solved with data automation and analytics.
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This course comprises four modules, each focusing on different aspects of data analytics using R. Students will learn about using R and RStudio for data processing, explore the interaction between business principles and data analytics, and gain practical skills for data preparation and analysis.
Module 1 introduces students to using a data analytic language, R, to solve business problems. It covers essential concepts such as the FACT framework, making code readable, calculations with R, and reading and writing data. Additionally, students will familiarize themselves with RStudio and its features, preparing them for more advanced data processing tasks.
Module 2 delves into exploring and sharing data with others. Students will learn about tidy dataframes, data dictionaries, summary statistics, R notebooks, and creating dashboards. The module emphasizes the importance of effectively communicating data analytic results and introduces students to tools for achieving this.
Module 3 focuses on utilizing functions to assist with data preparation. Topics covered include data types, packages, date types, factors, logical type and relational operators, and character strings. Students will gain practical skills in assembling and manipulating data for analysis.
Module 4 emphasizes data preprocessing, discussing framing questions for actionable insight, dataframe shape, control versus feasibility, and wide versus long dataframe shapes. Students will learn to use dplyr functions for data manipulation, handling missing values, data aggregation, and joining data. The module concludes with a review of the course and the opportunity to obtain a course certificate.
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