Complete Your First Project in R
2h 15mIntermediate2024-03-07
Authors
Megan Silvey
Course details
Expand your knowledge and skillset with R in this course with data science consultant Megan Silvey. Megan presents a hands-on project using R to analyze sales data and work through a real-world scenario that focuses on performing data analysis for customers of an enterprise tech company. Get experience using data analysis and machine learning techniques, including classification and clustering, to analyze data that includes information about orders, sales employees, customers, products purchased, and customer payment statuses. Join Megan in this course to gain know-how and skills to take your data career to the next level.
Learning objectives
Enhance your learning in R
Add a potential project to your portfolio
Learning objectives
Enhance your learning in R
Add a potential project to your portfolio
Skills covered
RStudioRStatisticsProjectData AnalysisProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - Introduction to R
- 02 - What you should know
- 03 - RStudio
1. Exploring Customer Data
- 04 - Introducing Red30 Tech
- 05 - Understanding Red30 Tech data
- 06 - How to perform basic descriptive analysis
- 07 - How to find top customers
- 08 - How to better understand customers
- 09 - How to analyze sales employees
- 10 - How to determine the best product category
- 11 - Challenge - Customer longevity analysis
- 12 - Solution - Customer longevity analysis
2. Classification Analysis
- 13 - Understanding classification analysis
- 14 - How to prepare data for classification
- 15 - How to run a decision tree algorithm
- 16 - How to run a random forest algorithm
- 17 - How to run a support vector machine algorithm
- 18 - Understanding summary metrics
- 19 - How to decide which algorithm is best
- 20 - How to improve the chosen algorithm
- 21 - Challenge - Explore the chosen algorithm
- 22 - Solution - Explore the chosen algorithm
3. Cluster Analysis
- 23 - Understanding cluster analysis
- 24 - How to prepare data for clustering
- 25 - How to run a k-means algorithm
- 26 - How to run a hierarchical clustering algorithm
- 27 - How to run a GMM clustering algorithm
- 28 - Evaluating cluster results
- 29 - Challenge - Clustering late payment customers
- 30 - Solution - Clustering late payment customers
Conclusion
- 31 - Next steps