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Essential Statistics for Non-STEM Data Analysts

Name: Essential Statistics for Non-STEM Data Analysts
Author: Rongpeng Li
ISBN: 9781838984847

by Rongpeng Li

November 2020

Beginner

392 pages

8h 16m

English

Packt Publishing

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Essential Statistics for Non-STEM Data Analysts
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Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
Section 1: Getting Started with Statistics for Data Science
Chapter 1: Fundamentals of Data Collection, Cleaning, and Preprocessing
Technical requirementsCollecting data from various data sourcesReading data directly from files Obtaining data from an APIObtaining data from scratchData imputationPreparing the dataset for imputationImputation with mean or median valuesImputation with the mode/most frequent valueOutlier removalData standardization – when and howExamples involving the scikit-learn preprocessing moduleImputationStandardizationSummary
Chapter 2: Essential Statistics for Data Assessment
Classifying numerical and categorical variablesDistinguishing between numerical and categorical variablesUnderstanding mean, median, and modeMeanMedianModeLearning about variance, standard deviation, quartiles, percentiles, and skewnessVarianceStandard deviationQuartilesSkewnessKnowing how to handle categorical variables and mixed data typesFrequencies and proportionsTransforming a continuous variable to a categorical oneUsing bivariate and multivariate descriptive statisticsCovarianceCross-tabulationSummary
Chapter 3: Visualization with Statistical Graphs
Basic examples with the Python Matplotlib packageElements of a statistical graphExploring important types of plotting in MatplotlibAdvanced visualization customizationCustomizing the geometryCustomizing the aestheticsQuery-oriented statistical plottingExample 1 – preparing data to fit the plotting function APIExample 2 – combining analysis with plain plottingPresentation-ready plotting tipsUse stylingFont matters a lotSummary
Section 2: Essentials of Statistical Analysis
Chapter 4: Sampling and Inferential Statistics
Understanding fundamental concepts in sampling techniquesPerforming proper sampling under different scenariosThe dangers associated with non-probability samplingProbability sampling – the safer approachUnderstanding statistics associated with samplingSampling distribution of the sample meanStandard error of the sample meanThe central limit theoremSummary
Chapter 5: Common Probability Distributions
Understanding important concepts in probabilityEvents and sample spaceThe probability mass function and the probability density functionSubjective probability and empirical probabilityUnderstanding common discrete probability distributionsBernoulli distributionBinomial distributionPoisson distributionUnderstanding the common continuous probability distribution Uniform distributionExponential distributionNormal distributionLearning about joint and conditional distributionIndependency and conditional distributionUnderstanding the power law and black swanThe ubiquitous power lawBe aware of the black swanSummary
Chapter 6: Parametric Estimation
Understanding the concepts of parameter estimation and the features of estimatorsEvaluation of estimatorsUsing the method of moments to estimate parametersExample 1 – the number of 911 phone calls in a dayExample 2 – the bounds of uniform distributionApplying the maximum likelihood approach with PythonLikelihood functionMLE for uniform distribution boundariesMLE for modeling noiseMLE and the Bayesian theoremSummary

Chapter 7: Statistical Hypothesis Testing
An overview of hypothesis testingUnderstanding P-values, test statistics, and significance levelsMaking sense of confidence intervals and P-values from visual examplesCalculating the P-value from discrete eventsCalculating the P-value from the continuous PDFSignificance levels in t-distributionThe power of a hypothesis testUsing SciPy for common hypothesis testing The paradigm T-testThe normality hypothesis testThe goodness-of-fit testA simple ANOVA modelStationarity tests for time seriesExamples of stationary and non-stationary time seriesAppreciating A/B testing with a real-world exampleConducting an A/B testRandomization and blockingCommon test statisticsCommon mistakes in A/B testsSummary
Section 3: Statistics for Machine Learning
Chapter 8: Statistics for Regression
Understanding a simple linear regression model and its rich contentLeast squared error linear regression and variance decompositionThe coefficient of determinationHypothesis testingConnecting the relationship between regression and estimatorsSimple linear regression as an estimatorHaving hands-on experience with multivariate linear regression and collinearity analysisCollinearityLearning regularization from logistic regression examplesSummary
Chapter 9: Statistics for Classification
Understanding how a logistic regression classifier worksThe formulation of a classification problemImplementing logistic regression from scratchEvaluating the performance of the logistic regression classifierBuilding a naïve Bayes classifier from scratchUnderfitting, overfitting, and cross-validationSummary
Chapter 10: Statistics for Tree-Based Methods
Overviewing tree-based methods for classification tasksGrowing and pruning a classification treeUnderstanding how splitting worksEvaluating decision tree performanceExploring regression treeUsing tree models in scikit-learnSummary
Chapter 11: Statistics for Ensemble Methods
Revisiting bias, variance, and memorizationUnderstanding the bootstrapping and bagging techniquesUnderstanding and using the boosting moduleExploring random forests with scikit-learnSummary
Section 4: Appendix
Chapter 12: A Collection of Best Practices
Understanding the importance of data qualityUnderstanding why data can be problematicAvoiding the use of misleading graphsExample 1 – COVID-19 trendExample 2 – Bar plot croppingFighting against false argumentsSummary
Chapter 13: Exercises and Projects
ExercisesChapter 1 – Fundamentals of Data Collection, Cleaning, and PreprocessingChapter 2 – Essential Statistics for Data AssessmentChapter 3 – Visualization with Statistical GraphsChapter 4 – Sampling and Inferential StatisticsChapter 5 – Common Probability DistributionsChapter 6 – Parameter EstimationChapter 7 – Statistical Hypothesis TestingChapter 8 – Statistics for RegressionChapter 9 – Statistics for ClassificationChapter 10 – Statistics for Tree-Based MethodsChapter 11 – Statistics for Ensemble MethodsProject suggestionsNon-tabular dataReal-time weather dataGoodness of fit for discrete distributionsBuilding a weather prediction web appBuilding a typing suggestion appFurther readingTextbooksVisualizationExercising your mindSummary
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Overview

Essential Statistics for Non-STEM Data Analysts is your comprehensive guide to mastering the statistical concepts needed for data science. By working through real-world datasets and Python-based examples, you'll learn how to interpret data and build insightful analyses. This book demystifies statistics, making it accessible to anyone aiming to become proficient in data analysis.

What this Book will help me do

Learn how to preprocess, clean, and prepare data for analysis using Python.
Master the foundations of statistical methods such as hypothesis testing and probability theory.
Develop skills to interpret and explain statistical results in the context of data science.
Understand how statistical concepts apply to machine learning tasks like classification and regression.
Build confidence in statistical principles to tackle interviews and enhance your career prospects.

Author(s)

None Li is an experienced data scientist and educator with a strong focus on making abstract statistical concepts intuitive and applicable. With a background in designing data science curriculums, None has a passion for teaching statistics to individuals from diverse and often non-mathematical backgrounds. Through clear explanations and practical examples, None aims to empower everyone to excel in data analysis and machine learning.

Who is it for?

This book caters specifically to data analysts, data science enthusiasts, and developers eager to enhance their statistical knowledge. It's crafted for readers transitioning into data science who may lack a strong mathematical or statistics background. If you have a basic grasp of Python programming and a keen interest in understanding how to work effectively with data, this book is a perfect fit. Beginners and students aiming to familiarize themselves with statistical foundations for data-oriented careers will greatly benefit from this resource.

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