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The Data Analysis Workshop

The Data Analysis Workshop

By : Gururajan Govindan , Shubhangi Hora , Konstantin Palagachev , Brent Broadnax, John Wesley Doyle , Ashish Jain, Robert Thas John, Ravi Ranjan Prasad Karn, Pritesh Tiwari
4.4 (21)
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The Data Analysis Workshop

The Data Analysis Workshop

4.4 (21)
By: Gururajan Govindan , Shubhangi Hora , Konstantin Palagachev , Brent Broadnax, John Wesley Doyle , Ashish Jain, Robert Thas John, Ravi Ranjan Prasad Karn, Pritesh Tiwari

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)
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Preface
7
7. Analyzing the Heart Disease Dataset
9
9. Analysis of the Energy Consumed by Appliances

Data Preprocessing

In this section, we perform some preprocessing steps, which will allow us to transform the data into a more human-readable format. Note that data preprocessing and wrangling is one of the most important parts of data analysis. In fact, a lot of hidden patterns and relationships might arise when data is transformed in the correct way.

Furthermore, some machine learning algorithms might not even converge, or they may provide an erroneous result when fed with badly preprocessed data (a typical example of this is not scaling data in deep learning). In other cases, deriving insights from normalized data might be difficult for a human; therefore, it is good practice to transform the data before presenting the results.

In this use case, the data is already normalized and ready for analysis; nevertheless, some of its columns are hard to interpret from a human perspective. Before proceeding further, we will perform some basic transformations on the columns, which will...

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The Data Analysis Workshop
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