Book Image

The Data Analysis Workshop

By : Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev
Book Image

The Data Analysis Workshop

By: Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev

Overview of this book

Businesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you’ll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst.
Table of Contents (12 chapters)
Preface
7
7. Analyzing the Heart Disease Dataset
9
9. Analysis of the Energy Consumed by Appliances

Data Analysis

One important thing to keep in mind while analyzing data is you need to understand what it is capable of telling you. Noting down some questions you think your data can answer helps you determine a path for your analysis.

Take a look at the retail DataFrame that we updated in Exercise 8.02, Preparing Our Data; it has 15 features. These features define who bought how much of what in which country at what time on what day in which month in which year. We can use combinations of these features to provide us with a lot of insights that would answer the following questions:

  1. Which customers placed the most and fewest orders?
  2. Which customers spent the most and least money?
  3. Which months were the most and least popular for this online retail store?
  4. Which dates of the month were the most and least popular for this online retail store?
  5. Which days were the most and least popular for this online retail store?
  6. Which hours of the day were most and least...