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 Cleaning

When doing online projects or learning from a course, the data used is often already in perfect form; there are no missing values or outliers, and all the features are accurate and useful. In reality, though, this is almost never the case. There are often rows and rows of data with inconsistencies that, if used as is, will provide us with flawed business insights, which could be disastrous if actually used to make business decisions.

For example, you're monitoring your shop's most and least active hours. This is done by tracking and storing information regarding your customers, especially what time they're coming into the shop. You have been storing the time in 24-hour clock format.

The next day, however, another employee takes over this responsibility and starts storing the time in 12-hour clock format. You suddenly have a column of data that has been stored in two different ways, and now 8:00 can mean both AM and PM. You don't notice this...