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Data Analytics for Marketing · LearnSpace
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Data Analytics for Marketing

Курс от Packt
Средний≈ 23.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course focuses on building practical marketing analytics skills using Python and statistical methods that are essential in today’s data-driven business environment. It emphasizes turning complex datasets into meaningful insights that support strategic marketing decisions. Through hands-on examples, you’ll learn how to analyze marketing data, apply appropriate models, and interpret results to improve campaign performance and customer understanding. The course helps you move from raw data to actionable outcomes with confidence. What sets this course apart is its balance between theory and real-world application. It combines statistical reasoning with practical Python workflows to solve common marketing analytics problems. This course is ideal for marketing-focused data analysts and data scientists with prior experience in Python, basic statistics, and data analysis who want to deepen their analytical impact.

Навыки, которые вы освоите

A/B TestingDashboard CreationMarketing EffectivenessStatistical ModelingMarketingMarketing AnalyticsCustomer AnalysisDescriptive AnalyticsMarket AnalysisStatistical MethodsForecastingData PresentationMarketing StrategiesCustomer InsightsTime Series Analysis and ForecastingExtract, Transform, LoadDashboardRegression AnalysisData-Driven MarketingAnomaly Detection

Программа курса

13 модулей · 125 учебных материалов

01What Is Marketing Analytics?10 материалов

Lesson 1

Course OverviewВидеоWhat Is Marketing Analytics? - Overview VideoВидеоIntroductionЧтениеExploring Different Types of AnalyticsЧтение

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Packt - Course Instructors

Преподаватель курса

Data Analytics for Marketing
В каталоге вашей программы

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Новые знания — в удобное для вас время.

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 23.4 ч

13 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский

Часть программы вашего университета
Diagnostic AnalyticsЧтение
Walking Through the Maze of Tools and TechniquesЧтение
Practice: Diagnose a Marketing ProblemDIALOGUE
Why Python?Чтение
The Importance of Data Engineering and TrackingЧтение
Foundations of Marketing AnalyticsЗадание
02Extracting and Exploring Data with Singer and pandas9 материалов

Lesson 1

Extracting and Exploring Data with Singer and pandas - Overview VideoВидеоIntroductionЧтениеWhat is Singer?ЧтениеSummarizing Data and EDAЧтениеMeasures of Central TendencyЧтениеCheck In: Choosing the Right Summary StatisticDIALOGUEMeasures of VariabilityЧтениеDealing with Common Data IssuesЧтениеData Analysis and Preparation FundamentalsЗадание
03Design Principles and Presenting Results with Streamlit9 материалов

Lesson 1

Design Principles and Presenting Results with Streamlit - Overview VideoВидеоIntroductionЧтениеThinking About How to Best Present DataЧтениеThinking a bit about Processing InformationЧтениеPractice: Redesign a Dashboard for ClarityDIALOGUEGenerating Effective Filters, Dimensions, and MetricsЧтениеGetting Your Data Into Streamlit and Generating a Basic DashboardЧтениеLoading the Data and Creating MetricsЧтениеEffective Dashboard Development and Data PresentationЗадание
04Econometrics and Causal Inference with Statsmodels and PyMC9 материалов

Lesson 1

Econometrics and Causal Inference with Statsmodels and PyMC - Overview VideoВидеоIntroductionЧтениеExploring Different Types of Regression ModelsЧтениеHow to Do a Linear RegressionЧтениеWhat Is Logistic RegressionЧтениеPractice: From Correlation to CausationDIALOGUEWhat Is Causal InferenceЧтениеA More Practical ApplicationЧтениеCausal Inference and Econometric AnalysisЗадание
05Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast13 материалов

Lesson 1

Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast - Overview VideoВидеоIntroductionЧтениеWhat to ForecastЧтениеWhat Types of Patterns Are PresentЧтениеSTL DecompositionЧтениеSTL FeaturesЧтениеPractice: Choosing a Forecast Model StrategyDIALOGUEBasics of Time Series ForecastingЧтениеAdjusting for BiasЧтениеInformation Criteria MetricsЧтениеETS Models in PythonЧтениеThe Prophet ModelЧтениеTime Series Forecasting FundamentalsЗадание
06Anomaly Detection with StatsForecast and PyMC7 материалов

Lesson 1

Anomaly Detection with StatsForecast and PyMC - Overview VideoВидеоIntroductionЧтениеAdvantages and LimitationsЧтениеPractice: Choose the Right Anomaly Detection ToolDIALOGUEForecasting as an Anomaly Detection ToolЧтениеUsing Rates of Arrival to Identify Change PointsЧтениеAnomaly Detection in Time Series AnalysisЗадание
07Customer Insights Segmentation and RFM12 материалов

Lesson 1

Customer Insights Segmentation and RFM - Overview VideoВидеоIntroductionЧтениеDelving Deeper Into What Segmentation IsЧтениеK-Means ClusteringЧтениеTargeting the Right Segment with the Right Marketing EffortsЧтениеBut What Exactly Is a Decision Tree and How Does It Fit Into a Forest?ЧтениеPractice: Choose Your Segmentation ModelDIALOGUELinear versus Quadratic Discriminant AnalysisЧтениеExploring RFMЧтениеThe DataЧтениеProfitability EvaluationЧтениеCustomer Insights and RFM AnalysisЗадание
08Customer Lifetime Value with PyMC Marketing8 материалов

Lesson 1

Customer Lifetime Value with PyMC Marketing - Overview VideoВидеоIntroductionЧтениеWhat's Wrong with the CLV FormulaЧтениеBeyond the CLV FormulaЧтениеPractice: Analyze CLV Models in ContextDIALOGUEImplementing the BTYD Model Using PyMC MarketingЧтениеPredicting the Expected Number of Purchases for a New CustomerЧтениеCustomer Lifetime Value and Statistical ModelingЗадание
09Customer Survey Analysis10 материалов

Lesson 1

Customer Survey Analysis - Overview VideoВидеоIntroductionЧтениеAsking QuestionsЧтениеReliability and ValidityЧтениеStandard Error of MeasurementЧтениеPractice: Analyze Survey Design Trade-offsDIALOGUEHow to Do SamplingЧтениеResponse RateЧтениеCustomer Loyalty and NPS MethodologyЧтениеCustomer Survey Analysis FundamentalsЗадание
10Conjoint Analysis with pandas and Statsmodels7 материалов

Lesson 1

Conjoint Analysis with pandas and Statsmodels - Overview VideoВидеоIntroductionЧтениеConducting Conjoint Analysis in PythonЧтениеReflect: Choose Your Conjoint Analysis MethodDIALOGUEFitting a Choice ModelЧтениеMarginal Utility and Willingness to PayЧтениеConjoint Analysis and Regression ModelingЗадание
11Multi-Touch Digital Attribution8 материалов

Lesson 1

Multi-Touch Digital Attribution - Overview VideoВидеоIntroductionЧтениеLinear AttributionЧтениеAlgorithmic Attribution ModelsЧтениеPractice: Choosing an Attribution ModelDIALOGUEImplementing a Shapley Value ExampleЧтениеFractributionЧтениеExploring Attribution Models and Their MechanicsЗадание
12Media Mix Modeling with PyMC Marketing11 материалов

Lesson 1

Media Mix Modeling with PyMC Marketing - Overview VideoВидеоIntroductionЧтениеSteps Toward Implementing MMMЧтениеData GranularityЧтениеHow to Measure the Adstock EffectЧтениеPractice: Model a Marketing CampaignDIALOGUESaturation and Diminishing ReturnsЧтениеSelecting a ModelЧтениеModelingЧтениеModel ResultsЧтениеMedia Mix Modeling FundamentalsЗадание
13Running Experiments with PyMC12 материалов

Lesson 1

Running Experiments with PyMC - Overview VideoВидеоIntroductionЧтениеLooking at an ExampleЧтениеFalse Positive RiskЧтениеSurprising Results Require Strong Evidence Lower P-ValuesЧтениеDelving Deeper into Some PitfallsЧтениеStatistical PowerЧтениеUniform PriorЧтениеExperimentationЧтениеObservational StudiesЧтениеQuasi-experimentsЧтениеExperimental Analysis and Statistical TechniquesЗадание