Nearly every aspect of business is affected by data analytics. There are many powerful tools that can quickly process large amounts of data. For businesses to capitalize on data analytics, they need leaders who understand the data analytic process. Even more valuable are leaders who know how to analyze big data. This course addresses the human skills gap by providing a foundational set of data analytic skills that can be applied to many business settings.
Introduction to Business Analytics with R
Offered By
Introduction to Business Analytics with R
University of Illinois at Urbana-Champaign
About this Course
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Skills you will gain
Business AnalyticsR Programming
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Flexible deadlines
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Approx. 11 hours to complete
Suggested: 7 hours/week
English
Subtitles: English
Offered by
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Syllabus - What you will learn from this course
4 hours to complete
Course Overview and Module 1 Introduction to Business Analytics and R
In this module you will be introduced to the role of data analytics in business domains.
4 hours to complete
18 videos (Total 112 min), 4 readings, 2 quizzes
18 videos
Meet Prof Ron Guymon2m
Meet Prof Ashish Khandelwal50s
Introduction to Business Analytics and R6m
Lesson 1-1.1: Overview of Business Analytics3m
Lesson 1-1.2: Examples of Business Analytics5m
Lesson 1-1.3: FACT Framework7m
Lesson 1-2.1: Introduction to R6m
Lesson 1-2.2: Getting Started with R5m
Lesson 1-2.3: Calculations with R12m
Lesson 1-2.4: Making Your Code Readable4m
Lesson 1-3.1: Functions and Using the Built-In Help8m
Lesson 1-3.2: Reading and Writing Data12m
Lesson 1-3.3: Getting to Know Your Data 14m
Lesson 1-3.4: Getting to Know Your Data 27m
Lesson 1-3.5: Getting to Know Your Data 35m
Lesson 1-3.6: Introduction to Other Data Types12m
Module 1 Conclusion1m
4 readings
Syllabus10m
About the Discussion Forums10m
Module 1 Overview10m
Module 1 Readings1h
2 practice exercises
Orientation Quiz
Module 1 Quiz30m
3 hours to complete
Module 2 Assembling Data
In this module you'll identify how framing a question and the intended analysis influence the way in which data is assembled.
3 hours to complete
14 videos (Total 111 min), 2 readings, 1 quiz
14 videos
Lesson 2-1.1: Framing a Question6m
Lesson 2-1.2: Assembling Data7m
Lesson 2-1.3: Calculate the Results9m
Lesson 2-2.1: Data Types6m
Lesson 2-2.2: Packages9m
Lesson 2-2.3: Creating Date Types8m
Lesson 2-2.4: Calculations with Dates9m
Lesson 2-2.5: Factors9m
Lesson 2-2.6: Logical Type and Relational Operators11m
Lesson 2-3.1: Conditional Statements7m
Lesson 2-3.2: Stacking Data7m
Lesson 2-3.3: Loops8m
Module 2 Conclusion1m
2 readings
Module 2 Overview10m
Module 2 Readings10m
1 practice exercise
Module 2 Quiz30m
4 hours to complete
Module 3 Data Quality and Data Transformation
This module starts with a discussion on the importance of Data Quality in business analytics. Later, we learn Data Transformation using Tidyverse, a group of useful R packages.
4 hours to complete
13 videos (Total 65 min), 2 readings, 1 quiz
13 videos
Lesson 3-1: Data Quality4m
Lesson 3-2.1: Data Structure based on the Business Problem Part 15m
Lesson 3-2.2: Data Structure based on the Business Problem Part 25m
Lesson 3-2.3: Subset Data using Filter and Select functions5m
Lesson 3-2.4: Useful Operators for Data Manipulation5m
Lesson 3-2.5: Creating New Variables using Mutate function3m
Lesson 3-2.6: Data Aggregation using Summarize and Group_By functions5m
Lesson 3-2.7: Handling Missing Values6m
Lesson 3-2.8: Data Join8m
Lesson 3-2.9: Long vs Wide Format for Data5m
Lesson 3-2.10: Manipulating Strings7m
Module 3 Conclusion55s
2 readings
Module 3 Overview10m
Module 3 Readings2h
1 practice exercise
Module 3 Quiz30m
4 hours to complete
Module 4 Exploratory Data Visualization using R
In this module, we will learn a powerful visualization package in ggplot2 to create useful visualizations for exploratory data analysis (EDA).
4 hours to complete
14 videos (Total 53 min), 2 readings, 2 quizzes
14 videos
Lesson 4-1: What to Explore in the Data?2m
Lesson 4-2: Univariate and Bivariate Exploration2m
Lesson 4-3.1: Introduction to the ggplot2 Package3m
Lesson 4-3.2: Introduction to ggplot Syntax4m
Leson 4-4.1: Univariate Exploration of a Numeric Variable5m
Lesson 4-4.2: Univariate Exploration of a Categorical Variable4m
Lesson 4-5.1: Visualizing Relationship between two Numeric Variables4m
Lesson 4-5.2: Visualizing Relationship between a Numeric and a Categorical Variable5m
Lesson 4-5.3: Visualizing Relationship between two Categorical Variables5m
Lesson 4-6: Multidimensional Charts6m
Lesson 4-7: Introduction to ggplot Builder7m
Module 4 Conclusion40s
Course Conclusion1m
2 readings
Module 4 Overview10m
Module 4 Readings40m
1 practice exercise
Module 4 Graded Quiz30m
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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