book

Practical Data Science with Python

Name: Practical Data Science with Python
Author: Nathan George
ISBN: 9781801071970

by Nathan George

September 2021

Beginner to intermediate

620 pages

15h 30m

English

Packt Publishing

Read now

Unlock full access

Preface
Who this book is forWhat this book coversTo get the most out of this bookGet in touch
Part I - An Introduction and the Basics
Introduction to Data Science
The data science origin storyThe top data science tools and skillsPythonOther programming languagesGUIs and platformsCloud toolsStatistical methods and mathCollecting, organizing, and preparing dataSoftware developmentBusiness understanding and communicationSpecializations in and around data scienceMachine learningBusiness intelligenceDeep learningData engineeringBig dataStatistical methodsNatural Language Processing (NLP)Artificial Intelligence (AI)Choosing how to specializeData science project methodologiesUsing data science in other fieldsCRISP-DMTDSPFurther reading on data science project management strategiesOther toolsTest your knowledgeSummary
Getting Started with Python
Installing Python with Anaconda and getting startedInstalling AnacondaRunning Python codeThe Python shellThe IPython shellJupyterWhy the command line?Command line basicsInstalling and using a code text editor – VS CodeEditing Python code with VS CodeRunning a Python fileInstalling Python packages and creating virtual environmentsPython basicsNumbersStringsVariablesLists, tuples, sets, and dictionariesListsTuplesSetsDictionariesLoops and comprehensionsBooleans and conditionalsPackages and modulesFunctionsClassesMultithreading and multiprocessingSoftware engineering best practicesDebugging errors and utilizing documentationDebuggingDocumentationVersion control with GitCode styleProductivity tipsTest your knowledgeSummary
Part II - Dealing with Data
SQL and Built-in File Handling Modules in Python
IntroductionLoading, reading, and writing files with base PythonOpening a file and reading its contentsUsing the built-in JSON moduleSaving credentials or data in a Python fileSaving Python objects with pickleUsing SQLite and SQLCreating a SQLite database and storing dataUsing the SQLAlchemy package in PythonTest your knowledgeSummary
Loading and Wrangling Data with Pandas and NumPy
Data wrangling and analyzing iTunes dataLoading and saving data with PandasUnderstanding the DataFrame structure and combining/concatenating multiple DataFramesExploratory Data Analysis (EDA) and basic data cleaning with PandasExamining the top and bottom of the dataExamining the data's dimensions, datatypes, and missing valuesInvestigating statistical properties of the dataPlotting with DataFramesCleaning dataFiltering DataFramesRemoving irrelevant dataDealing with missing valuesDealing with outliersDealing with duplicate valuesEnsuring datatypes are correctStandardizing data formatsData transformationsUsing replace, map, and apply to clean and transform dataUsing GroupByWriting DataFrames to diskWrangling and analyzing Bitcoin price dataUnderstanding NumPy basicsUsing NumPy mathematical functionsTest your knowledgeSummary
Exploratory Data Analysis and Visualization
EDA and visualization libraries in PythonPerforming EDA with Seaborn and pandasMaking boxplots and letter-value plotsMaking histograms and violin plotsMaking scatter plots with Matplotlib and SeabornExamining correlations and making correlogramsMaking missing value plotsUsing EDA Python packagesUsing visualization best practicesSaving plots for sharing and reportsMaking plots with PlotlyTest your knowledgeSummary
Data Wrangling Documents and Spreadsheets
Parsing and processing Word and PDF documentsReading text from Word documentsExtracting insights from Word documents: common words and phrasesAnalyzing words and phrases from the textReading text from PDFsReading and writing data with Excel filesUsing pandas for wrangling Excel filesAnalyzing the dataUsing openpyxl for wrangling Excel filesTest your knowledgeSummary
Web Scraping
Understanding the structure of the internetGET and POST requests, and HTMLPerforming simple web scrapingUsing urllibUsing the requests packageScraping several filesExtracting the data from the scraped filesParsing HTML from scraped pagesUsing XPath, lxml, and bs4 to extract data from webpagesCollecting data from several pagesUsing APIs to collect dataUsing API wrappersThe ethics and legality of web scrapingTest your knowledgeSummary

Part III - Statistics for Data Science
Probability, Distributions, and Sampling
Probability basicsIndependent and conditional probabilitiesBayes' TheoremFrequentist versus BayesianDistributionsThe normal distribution and using scipy to generate distributionsDescriptive statistics of distributionsVariants of the normal distributionFitting distributions to data to get parametersThe Student's t-distributionThe Bernoulli distributionThe binomial distributionThe uniform distributionThe exponential and Poisson distributionsThe Weibull distributionThe Zipfian distributionSampling from dataThe law of large numbersThe central limit theoremRandom samplingBootstrap sampling and confidence intervalsTest your knowledgeSummary
Statistical Testing for Data Science
Statistical testing basics and sample comparison testsThe t-test and z-testOne-sample, two-sided t-testThe z-testOne-sided testsTwo-sample t- and z-tests: A/B testingPaired t- and z-testsOther A/B testing methodsTesting between several groups with ANOVAPost-hoc ANOVA testsAssumptions for these methodsOther statistical testsTesting if data belongs to a distributionGeneralized ESD outlier testThe Pearson correlation testTest your knowledgeSummary
Part IV - Machine Learning
Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction
Types of machine learningFeature selectionThe curse of dimensionalityOverfitting and underfitting, and the bias-variance trade-offMethods for feature selectionVariance thresholding – removing features with too much and too little varianceUnivariate statistics feature selectionCorrelationMutual information score and chi-squaredThe chi-squared testANOVAUsing the univariate statistics for feature selectionFeature engineeringData cleaning and preparationConverting strings to datesOutlier cleaning strategiesCombining multiple columnsTransforming numeric dataStandardizationMaking data more Gaussian with the Yeo-Johnson transformExtracting datetime featuresBinningOne-hot encoding and label encodingSimplifying categorical columnsOne-hot encodingDimensionality reductionPrinciple Component Analysis (PCA)Test your knowledgeSummary
Machine Learning for Classification
Machine learning classification algorithmsLogistic regression for binary classificationGetting predictions from our modelHow logistic regression worksOdds ratio and the logitExamining feature importances with sklearnUsing statmodels for logistic regressionMaximum likelihood estimation, optimizers, and the logistic regression algorithmRegularizationHyperparameters and cross-validationLogistic regression (and other models) with big dataNaïve Bayes for binary classificationk-nearest neighbors (KNN)Multiclass classificationLogistic regressionOne-versus-rest and one-versus-one formulationsMulti-label classificationChoosing a model to useThe "no free lunch" theoremComputational complexity of modelsTest your knowledgeSummary
Evaluating Machine Learning Classification Models and Sampling for Classification
Evaluating classification algorithm performance with metricsTrain-validation-test splitsAccuracyCohen's KappaConfusion matrixPrecision, recall, and F1 scoreAUC score and the ROC curveChoosing the optimal cutoff threshold Sampling and balancing classification dataDownsamplingOversamplingSMOTE and other synthetic sampling methodsTest your knowledgeSummary
Machine Learning with Regression
Linear regressionLinear regression with sklearnLinear regression with statsmodelsRegularized linear regressionRegression with KNN in sklearnEvaluating regression modelsR2 or the coefficient of determinationAdjusted R2Information criteriaMean squared errorMean absolute errorLinear regression assumptionsRegression models on big dataForecastingTest your knowledgeSummary
Optimizing Models and Using AutoML
Hyperparameter optimization with search methodsUsing grid searchUsing random searchUsing Bayesian searchOther advanced search methodsUsing learning curvesOptimizing the number of features with ML modelsUsing AutoML with PyCaretThe no free lunch theoremAutoML solutionsUsing PyCaretTest your knowledgeSummary
Tree-Based Machine Learning Models
Decision treesRandom forestsRandom forests with sklearnRandom forests with H2OFeature importance from tree-based methodsUsing H2O for feature importanceUsing sklearn random forest feature importancesBoosted trees: AdaBoost, XGboost, LightGBM, and CatBoostAdaBoostXGBoostXGBoost with PyCaretXGBoost with the xgboost packageTraining boosted models on a GPULightGBMLightGBM plottingUsing LightGBM directlyCatBoostUsing CatBoost nativelyUsing early stopping with boosting algorithmsTest your knowledgeSummary
Support Vector Machine (SVM) Machine Learning Models
How SVMs workSVMs for classificationSVMs for regressionUsing SVMsUsing SVMs in sklearnTuning SVMs with pycaretTest your knowledgeSummary
Part V - Text Analysis and Reporting
Clustering with Machine Learning
Using k-means clusteringClustering metricsOptimizing k in k-meansExamining the clustersHierarchical clusteringDBSCANOther unsupervised methodsTest your knowledgeSummary
Working with Text
Text preprocessingBasic text cleaningStemming and LemmatizingPreparing text with spaCyWord vectorsTFIDF vectorsBasic text analysisWord frequency plotsWordcloudsZipf's lawWord collocationsParts of speechUnsupervised learningTopic modelingTopic modeling with pycaretTopic modeling with Top2VecSupervised learningClassificationSentiment analysisTest your knowledgeSummary
Part VI - Wrapping Up
Data Storytelling and Automated Reporting/Dashboarding
Data storytellingData storytelling exampleAutomated reporting and dashboardingAutomated reporting optionsAutomated dashboardingScheduling tasks to run automatically Test your knowledgeSummary
Ethics and Privacy
The ethics of machine learning algorithmsBiasHow to decrease ML biasesCarefully evaluating performance and consequencesData privacyData privacy regulations and lawsk-anonymity, l-diversity, and t-closenessDifferential privacyUsing data science for the common goodOther ethical considerationsTest your knowledgeSummary
Staying Up to Date and the Future of Data Science
Blogs, newsletters, books, and academic sourcesBlogsNewslettersBooksAcademic sourcesData science competition websitesOnline learning platformsCloud servicesOther places to keep an eye onStrategies for staying up to dateOther data science topics we didn't coverThe future of data scienceSummary
Other Books You May Enjoy
Index

Overview

Practical Data Science with Python guides you through the entire process of leveraging Python tools to analyze and gain insights from data. You'll start with foundational concepts and coding essentials, progressing through statistical analysis, machine learning techniques, and ethical considerations.

What this Book will help me do

Clean, prepare, and explore data using pandas and NumPy.
Understand and implement machine learning models such as random forests and support vector machines.
Perform statistical tests and analyze distributions to enhance data insights.
Utilize SQL with Python for efficient data interaction.
Generate automated reports and dashboards for data storytelling.

Author(s)

Nathan George has extensive professional experience as a data scientist and Python developer. He specializes in the application of machine learning and statistical methods to solve real-world problems. His writing combines technical depth with an approachable style, aiming to provide readers with actionable knowledge and skills.

Who is it for?

This book is perfect for data science beginners who have a basic understanding of Python and want to build practical data analysis skills. Students in analytics programs or professionals looking to transition into a data science role will find value in its approachable yet comprehensive coverage. Aspiring data analysts and career changers will gain firsthand exposure to Python-based data science best practices. If you're eager to develop practical, hands-on experience in the data science field, this is the guide for you.

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

Data Science Projects with Python - Second Edition

Publisher Resources

ISBN: 9781801071970

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills