Part 1: Foundations of Digital Marketing
-
Introduction to Digital Marketing
-
Channels: SEO, SEM, Social Media, Email, Content Marketing, Affiliate Marketing
-
KPIs: Impressions, Clicks, CTR, Conversions, Cost per Acquisition (CPA)
-
Overview of digital marketing data and tracking metrics
-
-
Data Science Basics for Marketers
-
Key concepts: data types, ETL (Extract, Transform, Load), data cleaning
-
Intro to marketing data sources: Google Analytics, ad platforms, CRM systems
-
Fundamentals of exploratory data analysis (EDA)
-
-
Marketing Analytics Essentials
-
Marketing funnel and attribution modeling
-
Campaign performance analysis
-
Customer segmentation and cohort analysis
-
Part 2: Power BI for Marketing & Data Science
-
Power BI Basics
-
Installing and setting up Power BI Desktop and Service
-
Importing data from Excel, CSV, databases
-
Introduction to Power Query for data cleaning and transformationÂ
-
-
Data Modeling & DAX
-
Building relationships, star schema design
-
Basic DAX: Calculated Columns, Measures
-
Advanced DAX: time intelligence, CALCULATE, ALL, variablesÂ
-
-
Power BI Visualization & Dashboards
-
Creating charts: bar, line, pie, map, waterfall, funnel, infographicsÂ
-
Filters, slicers, bookmarks, drill-down, tooltips, custom visualsÂ
-
-
Power BI Advanced Features
-
Power Query (level up): merge, append, pivot, parametersÂ
-
Cloud and service: publishing, dashboards, dataflows, security (RLS)Â
-
AI features: Q&A, Smart visuals, anomaly detection, key phrase extractionÂ
-
Part 3: Tableau for Marketing Insights
-
Tableau Basics
-
Installing and navigating the interface; connecting to data sources (Excel, DBs)Â
-
Data types, dimensions vs. measures, discrete vs. continuous Building Visualizations in Tableau
-
-
-
Creating bar, line, scatter, dual-axis, map visualizationsÂ
-
Filters, parameters, sets, groups, calculationsÂ
-
-
Dashboards & Storytelling
-
Designing dashboards, interactivity, layout, and storytelling with dataÂ
-
Creating worksheets into actionable insights for marketing decisions
-
-
Marketing Analytics with Tableau
-
Use cases: campaign performance, buyer personas, email & paid social marketing, ROI dashboardsÂ
-
Attribution modeling visualization, LTV/CAC analysisÂ
-
Part 4: Integrating Data Science into Marketing
-
Bridging Data Science with Visualization
-
Leveraging Python or R in Power BI (data prep, modeling, predictive analytics)
-
Using advanced analytics workflows and Tableau integrationÂ
-
-
End-to-End Project
-
Students work on real-world datasets (campaign data, web analytics, CRM)
-
Build visual dashboards in both Power BI and Tableau
-
Present findings: customer segmentation, attribution insights, ROI optimization
-
-
Certifications & Career Preparation
-
Overview of certification paths (Power BI, Tableau)
-
Best practices for resumes, presenting dashboards and analytics
-
Summary Table
Module | Focus Areas |
---|---|
Digital Marketing Foundations | Data tracking, attribution, KPIs |
Power BI | Data prep, modeling, dashboards, AI |
Tableau | Visual storytelling, marketing insights |
Data Science Integration | Python/R, predictive analytics |
Capstone Project | Real-world dashboards, insights, presentation |
Supporting References
-
Power BI: Course syllabi cover data ingestion, modeling (DAX), Power Query, publishing, AI featuresÂ
-
Tableau: Comprehensive learning paths cover connecting data, chart types, filters, parameters, building dashboards, marketing applicationsÂ
-
Combined Skills: Some programs include projects across Excel, Power BI, Tableau, Python integrationÂ