book

The Data Analysis Workshop

Name: The Data Analysis Workshop
ISBN: 9781839211386

by Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev, Brent Broadnax, John Wesley Doyle, Ashish Jain, Robert Thas John, Ravi Ranjan Prasad Karn, Pritesh Tiwari

July 2020

Beginner to intermediate

626 pages

10h 2m

English

Packt Publishing

Read now

Unlock full access

The Data Analysis Workshop
Preface
About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentInstallation and SetupInstalling LibrariesAccessing the Code Files
1. Bike Sharing Analysis
IntroductionUnderstanding the DataData PreprocessingExercise 1.01: Preprocessing Temporal and Weather Features Registered versus Casual Use AnalysisExercise 1.02: Analyzing Seasonal Impact on RidesHypothesis TestsExercise 1.03: Estimating Average Registered RidesExercise 1.04: Hypothesis Testing on Registered RidesAnalysis of Weather-Related FeaturesExercise 1.05: Evaluating the Difference between the Pearson and Spearman CorrelationsCorrelation Matrix PlotTime Series AnalysisExercise 1.06: Time Series Decomposition in Trend, Seasonality, and Residual ComponentsARIMA ModelsExercise 1.07: ACF and PACF Plots for Registered RidesActivity 1.01: Investigating the Impact of Weather Conditions on RidesSummary
2. Absenteeism at Work
IntroductionInitial Data AnalysisExercise 2.01: Identifying Reasons for AbsenceInitial Analysis of the Reason for AbsenceAnalysis of Social Drinkers and SmokersExercise 2.02: Identifying Reasons of Absence with Higher Probability Among Drinkers and SmokersExercise 2.03: Identifying the Probability of Being a Drinker/Smoker, Conditioned to Absence ReasonBody Mass IndexAge and Education FactorsExercise 2.04: Investigating the Impact of Age on Reason for AbsenceExercise 2.05: Investigating the Impact of Education on Reason for AbsenceTransportation Costs and Distance to Work FactorsTemporal FactorsExercise 2.06: Investigating Absence Hours, Based on the Day of the Week and the Month of the YearActivity 2.01: Analyzing the Service Time and Son ColumnsSummary
3. Analyzing Bank Marketing Campaign Data
IntroductionInitial Data AnalysisExercise 3.01: Analyzing Distributions of Numerical Features in the Banking DatasetExercise 3.02: Analyzing Distributions of Categorical Features in the Banking DatasetImpact of Numerical Features on the OutcomeExercise 3.03: Hypothesis Test of the Difference of Distributions in Numerical FeaturesModeling the Relationship via Logistic RegressionLinear RegressionLogistic RegressionExercise 3.04: Logistic Regression on the Full Marketing Campaign DataActivity 3.01: Creating a Leaner Logistic Regression ModelSummary
4. Tackling Company Bankruptcy
IntroductionExplanation of Some of the Important FeaturesImporting the DataExercise 4.01: Importing Data into DataFramesPandas ProfilingRunning Pandas ProfilingPandas Profiling Report for DataFrame 1 Pandas Profiling Report for DataFrame 2Missing Value AnalysisExercise 4.02: Performing Missing Value Analysis for the DataFramesImputation of Missing Values Mean ImputationExercise 4.03: Performing Mean Imputation on the DataFramesIterative ImputationExercise 4.04: Performing Iterative Imputation on the DataFrameSplitting the FeaturesFeature Selection with LassoLasso Regularization for Mean-Imputed DataFramesLasso Regularization for Iterative-Imputed DataFramesActivity 4.01: Feature Selection with LassoSummary
5. Analyzing the Online Shopper's Purchasing Intention
IntroductionData Dictionary Importing the DataExploratory Data AnalysisUnivariate AnalysisBaseline Conversion Rate from the Revenue Column Visitor-Wise Distribution Traffic-Wise Distribution Exercise 5.01: Analyzing the Distribution of Customers Session on the WebsiteRegion-Wise Distribution Exercise 5.02: Analyzing the Browser and OS Distribution of CustomersAdministrative Pageview DistributionInformation Pageview Distribution Special Day Session Distribution Bivariate Analysis Revenue Versus Visitor Type Revenue Versus Traffic TypeExercise 5.03: Analyzing the Relationship between Revenue and Other VariablesLinear Relationships Bounce Rate versus Exit RatePage Value versus Bounce RatePage Value versus Exit RateImpact of Administration Page Views and Administrative Pageview Duration on Revenue Impact of Information Page Views and Information Pageview Duration on Revenue Clustering Method to Find the Optimum Number of ClustersExercise 5.04: Performing K-means Clustering for Informational Duration versus Bounce RatePerforming K-means Clustering for Informational Duration versus Exit RateActivity 5.01: Performing K-means Clustering for Administrative Duration versus Bounce Rate and Administrative Duration versus Exit RateSummary
6. Analysis of Credit Card Defaulters
IntroductionImporting the DataData Preprocessing Exploratory Data AnalysisUnivariate AnalysisBivariate AnalysisExercise 6.01: Evaluating the Relationship between the DEFAULT Column and the EDUCATION and MARRIAGE ColumnsPAY_1 versus DEFAULTBalance versus DEFAULTExercise 6.02: Evaluating the Relationship between the AGE and DEFAULT ColumnsCorrelationActivity 6.01: Evaluating the Correlation between Columns Using a HeatmapBuilding a Profile of a High-Risk CustomerSummary
7. Analyzing the Heart Disease Dataset
IntroductionExercise 7.01: Loading and Understanding the DataOutliersExercise 7.02: Checking for Outliers Activity 7.01: Checking for OutliersExercise 7.03: Plotting the Distributions and Relationships Between Specific FeaturesActivity 7.02: Plotting Distributions and Relationships between Columns with Respect to the Target ColumnExercise 7.04: Plotting the Relationship between the Presence of Heart Disease and Maximum Recorded Heart RateActivity 7.03: Plotting the Relationship between the Presence of Heart Disease and the Cholesterol Column Exercise 7.05: Observing Correlations with a HeatmapSummary
8. Analyzing Online Retail II Dataset
IntroductionData CleaningExercise 8.01: Loading and Cleaning Our DataData Preparation and Feature Engineering Exercise 8.02: Preparing Our DataData AnalysisExercise 8.03: Finding the Answers in Our DataActivity 8.01: Performing Data Analysis on the Online Retail II DatasetSummary

9. Analysis of the Energy Consumed by Appliances
IntroductionExercise 9.01: Taking a Closer Look at the Dataset Exercise 9.02: Analyzing the Light Energy Consumption ColumnActivity 9.01: Analyzing the Appliances Energy Consumption Column Exercise 9.03: Performing Feature EngineeringExercise 9.04: Visualizing the DatasetActivity 9.02: Observing the Trend between a_energy and dayExercise 9.05: Plotting Distributions of the Temperature ColumnsActivity 9.03: Plotting Distributions of the Humidity ColumnsExercise 9.06: Plotting out_b, out_hum, visibility, and windSummary
10. Analyzing Air Quality
IntroductionAbout the DatasetExercise 10.01: Concatenating Multiple DataFrames and Checking for Missing ValuesOutliersExercise 10.02: Identifying OutliersActivity 10.01: Checking for OutliersMissing ValuesExercise 10.03: Dealing with Missing ValuesExercise 10.04: Observing the Concentration of PM25 and PM10 per YearActivity 10.02: Observing the Pollutant Concentration per Year Activity 10.03: Observing Pollutant Concentration per Month HeatmapsExercise 10.05: Checking for Correlations between Features Summary
Appendix
1. Bike Sharing AnalysisActivity 1.01: Investigating the Impact of Weather Conditions on Rides2. Absenteeism at WorkActivity 2.01: Analyzing the Service Time and Son Columns3. Analyzing Bank Marketing Campaign DataActivity 3.01: Creating a Leaner Logistic Regression Model4. Tackling Company BankruptcyActivity 4.01: Feature Selection with Lasso5. Analyzing the Online Shopper's Purchasing IntentionActivity 5.01: Performing K-means Clustering for Administrative Duration versus Bounce Rate and Administrative Duration versus Exit Rate6. Analysis of Credit Card DefaultersActivity 6.01: Evaluating the Correlation between Columns Using a Heatmap7. Analyzing the Heart Disease DatasetActivity 7.01: Checking for OutliersActivity 7.02: Plotting Distributions and Relationships between Columns with Respect to the Target ColumnActivity 7.03: Plotting the Relationship between the Presence of Heart Disease and the Cholesterol Column 8. Analyzing Online Retail II DatasetActivity 8.01: Performing Data Analysis on the Online Retail II Dataset9. Analysis of the Energy Consumed by AppliancesActivity 9.01: Analyzing the Appliances Energy ConsumptionActivity 9.02: Observing the Trend between a_energy and dayActivity 9.03: Plotting Distributions of the Humidity Columns10. Analyzing Air QualityActivity 10.01: Checking for OutliersActivity 10.02: Observing the Pollutant Concentration per YearActivity 10.03: Observing Pollutant Concentration per Month

Content preview from The Data Analysis Workshop

Preface

About the 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 ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781839211386

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

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

Preface

About the Book

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.