Creating An Efficient Data Analysis Workflow, Part 2
- Guided Project
- 0.2 hours
- Beginner
- R
Practice R programming to clean sales data, process reviews, and evaluate a new program's business impact.
Overview
In this project, you'll step into the role of a data analyst at a book company to evaluate the impact of a new program launched on July 1, 2019 aimed at encouraging customers to buy more books. Your challenge is to analyze the company's 2019 sales data to determine if the program successfully increased book purchases and improved review quality.
You'll leverage your data analysis skills in R, using powerful packages like dplyr, stringr, and lubridate to clean and process the sales data. This will involve handling missing values, converting text reviews into positive/negative sentiment, and comparing key sales and review metrics before and after the program start date. Through hands-on practice, you'll gain experience efficiently analyzing a real-world business dataset to deliver actionable insights.
Objective: Analyze book sales data using R to determine if a newly launched program led to an increase in purchases and improvement in review sentiment.
What You'll Learn
- ✓ Expand your portfolio with a data analysis project
Before You Start
- ✓ Manipulating strings in R using stringr functions
- ✓ Working with dates and times in R using lubridate
- ✓ Applying the map function to vectorize custom functions
- ✓ Understanding and employing regular expressions for pattern matching
Project Steps
9 steps
- 1 Introduction
- 2 Data Exploration
- 3 Handling Missing Data
- 4 Processing Review Data
- 5 Comparing Book Sales Between Pre- and Post-Program Sales
- 6 Comparing Book Sales Within Customer Type
- 7 Comparing Review Sentiment Between Pre- and Post-Program Sales
- 8 Further Steps
- 9 Next Steps
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