book

Cracking the Data Science Interview

Name: Cracking the Data Science Interview
ISBN: 9781805120506

by Leondra R. Gonzalez, Aaren Stubberfield

February 2024

Beginner to intermediate

404 pages

10h 46m

English

Packt Publishing

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Cracking the Data Science Interview
ForewordContributorsAbout the authorsAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookConventions usedSpecial NoteGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: Breaking into the Data Science Field
Chapter 1: Exploring Today’s Modern Data Science Landscape
What is data science?Exploring the data science processData collectionData explorationData modelingModel evaluationModel deployment and monitoringDissecting the flavors of data scienceData engineerDashboarding and visual specialistML specialistDomain expertReviewing career paths in data scienceThe traditionalistDomain expertOff-the-beaten path-erTackling the experience bottleneckAcademic experienceWork experienceUnderstanding expected skills and competencies Hard (technical) skillsSoft (communication) skillsExploring the evolution of data scienceNew modelsNew environmentsNew computingNew applicationsSummaryReferences
Chapter 2: Finding a Job in Data Science
Searching for your first data science jobPreparing for the road aheadFinding job boardsBeginning to build a standout portfolioApplying for jobsConstructing the Golden ResumeThe perfect resume mythUnderstanding automated resume screeningCrafting an effective resumeFormatting and organizationUsing the correct terminologyPrepping for landing the interviewMoore’s LawResearch, research, researchBrandingReferences
Part 2: Manipulating and Managing Data
Chapter 3: Programming with Python
Using variables, data types, and data structuresAssessmentAnswersIndexing in PythonUsing string operationsInitializing a stringString indexingAssessmentAnswersAssessmentAnswersUsing Python control statements, loops, and list comprehensionsConditional statements such as if, elif, and elseLoop statements such as for and whileList comprehensionAssessmentAnswerAssessmentAnswerUsing user-defined functionsBreaking down the user-defined function syntaxDoing “stuff” with user-defined functionsGetting familiar with lambda functionsCreating good functionsAssessmentAnswersHandling files in PythonOpening files with pandasAssessmentAnswersWrangling data with pandasHandling missing dataSelecting dataSorting dataMerging dataAggregation with groupby()AssessmentAnswerAssessmentAnswerAssessmentAnswerAssessmentAnswerAssessmentAnswerAssessmentAnswerSummaryReferences
Chapter 4: Visualizing Data and Data Storytelling
Understanding data visualizationBar chartsLine chartsScatter plotsHistogramsDensity plotsQuantile-quantile plots (Q-Q plots)Box plotsPie chartsAssessmentAnswerAssessmentAnswerSurveying tools of the tradePower BITableauShinyggplot2 (R)Matplotlib (Python)Seaborn (Python)AssessmentAnswerDeveloping dashboards, reports, and KPIsAssessmentAnswerDeveloping charts and graphsBar chart – MatplotlibBar chart – SeabornScatter plot – MatplotlibScatter plot – SeabornHistogram plot – MatplotlibHistogram plot – SeabornAssessmentAnswerApplying scenario-based storytellingAssessmentAnswerAssessmentAnswerSummary
Chapter 5: Querying Databases with SQL
Introducing relational databasesMastering SQL basicsThe SELECT statementThe WHERE clauseThe ORDER BY clauseAssessmentAnswerAssessmentAnswerAggregating data with GROUP BY and HAVINGThe GROUP BY statementThe HAVING clauseAssessmentAnswerCreating fields with CASE WHENAnalyzing subqueries and CTEsSubqueries in the SELECT clauseSubqueries in the FROM clauseSubqueries in the WHERE clauseSubqueries in the HAVING clauseDistinguishing common table expressions (CTEs) from subqueriesAssessmentAnswerAssessmentAnswerMerging tables with joinsInner joinsLeft and right joinFull outer joinMulti-table joinsAssessmentAnswerCalculating window functionsOVER, ORDER BY, PARTITION, and SETLAG and LEADAssessmentAnswerAssessmentAnswerROW_NUMBERRANK and DENSE_RANKAssessmentAnswerUsing date functionsApproaching complex queriesAssessmentProcess and answerSummary
Chapter 6: Scripting with Shell and Bash Commands in Linux
Introducing operating systemsNavigating system directoriesIntroducing basic command-line promptsUnderstanding directory typesAssessmentAnswerAssessmentAnswerFiling and directory manipulationAssessmentAnswerScripting with BashAssessmentAnswerAssessmentAnswerIntroducing control statementsAssessmentAnswerCreating functionsAssessmentAnswerProcessing data and pipelinesUsing pipesAssessmentAnswerUsing cronAssessmentAnswerSummary

Chapter 7: Using Git for Version Control
Introducing repositories (repos)Creating a repoCloning an existing remote repositoryCreating a local repository from scratchLinking local and remote repositoriesAssessmentAnswerAssessmentAnswerDetailing the Git workflow for data scientistsAssessmentAnswerAssessmentAnswerUsing Git tags for data scienceUnderstanding Git tagsUsing tagging as a data scientistUnderstanding common operationsAssessmentAnswerAssessmentAnswerSummary
Part 3: Exploring Artificial Intelligence
Chapter 8: Mining Data with Probability and Statistics
Describing data with descriptive statisticsMeasuring central tendencyMeasuring variabilityAssessmentAnswerAssessmentAnswerIntroducing populations and samplesDefining populations and samplesRepresenting samplesReducing the sampling errorAssessmentAnswerAssessmentAnswerUnderstanding the Central Limit Thereom (CLT)The CLTDemonstrating the assumption of normalityAssessmentAnswerAssessmentAnswerShaping data with sampling distributionsProbability distributionsUniform distributionNormal and student’s t-distributionsThe binomial distributionThe Poisson distributionExponential distributionGeometric distributionThe Weibull distributionAssessmentAnswerAssessmentAnswerTesting hypothesesUnderstanding one-sample t-testsUnderstanding two-sample t-testsUnderstanding paired sample t-testsUnderstanding ANOVA and MANOVAChi-squared testA/B testsAssessmentAnswerAssessmentAnswerUnderstanding Type I and Type II errorsType I error (false positive)Type II error (false negative)Striking a balanceAssessmentAnswerAssessmentAnswerSummaryReferences
Chapter 9: Understanding Feature Engineering and Preparing Data for Modeling
Understanding feature engineeringAvoiding data leakageHandling missing dataScaling dataApplying data transformationsIntroducing data transformationsLogarithm transformationsPower transformationsBox-Cox transformationsExponential transformationsEngineering categorical data and other featuresOne-hot encodingLabel encodingTarget encodingCalculated fieldsAssessmentAnswerAssessmentAnswerAssessmentAnswerPerforming feature selectionTypes of feature selectionRecursive feature eliminationL1 regularizationTree-based feature selectionThe variance inflation factorWorking with imbalanced dataUnderstanding imbalanced dataTreating imbalanced dataReducing the dimensionalityPrincipal component analysisSingular value decompositiont-SNEAutoencodersSummary
Chapter 10: Mastering Machine Learning Concepts
Introducing the machine learning workflowProblem statementModel selectionModel tuningModel predictionsGetting started with supervised machine learningRegression versus classificationLinear regression – regressionAssessmentAnswerAssessmentAnswerAssessmentAnswerAssessmentAnswerLogistic regressionAssessmentAnswerAssessmentAnswerk-nearest neighbors (k-NN)AssessmentAnswerAssessmentAnswerAssessmentAnswerRandom forestAssessmentAnswerAssessmentAnswerAssessmentAnswerExtreme Gradient Boosting (XGBoost)AssessmentAnswerAssessmentAnswerGetting started with unsupervised machine learningK-meansAssessmentAnswerAssessmentAnswerDensity-based spatial clustering of applications with noise (DBSCAN)Other clustering algorithmsEvaluating clustersAssessmentAnswerAssessmentAnswerAssessmentAnswerSummarizing other notable machine learning modelsUnderstanding the bias-variance trade-offAssessmentAnswerAssessmentAnswerAssessmentAnswerTuning with hyperparametersGrid searchRandom searchBayesian optimizationAssessmentAnswerAssessmentAnswerAssessmentAnswerSummary
Chapter 11: Building Networks with Deep Learning
Introducing neural networks and deep learningAssessmentAnswerAssessmentAnswerWeighing in on weights and biasesIntroduction to weightsIntroduction to biasesAssessmentAnswerActivating neurons with activation functionsCommon activation functionsChoosing the right activation functionAssessmentAnswerAssessmentAnswerUnraveling backpropagationGradient descentWhat is backpropagation?Loss functionsGradient descent stepsThe vanishing gradient problemAssessmentAnswerAssessmentAnswerUsing optimizersOptimization algorithmsNetwork tuningAssessmentAnswerAssessmentAnswerUnderstanding embeddingsWord embeddingsTraining embeddingsAssessmentAnswerListing common network architecturesCommon networksTools and packagesAssessmentAnswerIntroducing GenAI and LLMsUnveiling language modelsTransformers and self-attentionAssessmentAnswerTransfer LearningGPT in actionSummary
Chapter 12: Implementing Machine Learning Solutions with MLOps
Introducing MLOpsA model pipeline overviewAssessmentAnswerUnderstanding data ingestionLearning the basics of data storageReviewing model developmentPackaging for model deploymentIdentifying requirementsVirtual environmentsTools and approaches for environment managementDeploying a model with containersUsing DockerAssessmentAnswerValidating and monitoring the modelValidating the model deploymentModel monitoringThinking about governanceUsing Azure ML for MLOpsSummary
Part 4: Getting the Job
Chapter 13: Mastering the Interview Rounds
Mastering early interactions with the recruiterMastering the different interview stagesThe hiring manager stageThe technical interviewCoding questions, step by stepAssessmentAnswerThe panel stageSummaryReferences
Chapter 14: Negotiating Compensation
Understanding the compensation landscapeNegotiating the offerNegotiation considerationsResponding to the offerMaximum negotiable compensation and situational valueAssessmentAnswerSummaryFinal words
Index
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Content preview from Cracking the Data Science Interview

12 Implementing Machine Learning Solutions with MLOps

Machine Learning Operations (MLOps) has emerged as a pivotal force in the data-driven age, enabling organizations to develop, deploy, and maintain machine learning models efficiently and effectively. It addresses key challenges related to speed, collaboration, governance, scalability, and cost, making it a discipline to be aware of for anyone navigating the modern landscape of artificial intelligence and machine learning.

In the following sections, we will break down the concept of MLOps, explore its core components, and provide insights into how it can elevate your machine learning initiatives. Whether you’re an aspiring data scientist looking to see your models in action, an IT professional ...

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Cracking the Data Science Interview

by Leondra R. Gonzalez, Aaren Stubberfield

12

Implementing Machine Learning Solutions with MLOps

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.