Skills • Tools • Courses Alignment

Choose your electives that are aligned with your career

How MSDM courses develop the data, analytical, and strategic skills for digital-marketing careers.
Published

December 5, 2025

Introduction

As students progress through the MSDM program, they develop a growing set of tools and skills that support their professional goals. This page outlines key software, technical abilities, and learning milestones at each stage of the journey—from foundational digital skills to advanced analytics, content creation, and strategic marketing capabilities. Use this guide to understand what to focus on, when to develop each skill, and how these competencies strengthen your career readiness..

1 Tools and Skills along the Student Learning Journey

1.1 Course → Skill → Tools

Term MSDM Course Core / Elective Skill Area(s) Typical Tools & Methods*
Year 1 – Fall IBM 5910 – Strategic Data Wrangling & Visualization Core Data Wrangling, Data Cleaning, Visualization, Data Storytelling R (Tidyverse), Quarto, RStudio/Positron, Tableau, Power BI, Excel, SQLite, Project
IBM 6010 – Digital Marketing Core Digital Strategy, Campaign Design, Analytics Foundations Social Media Platforms, Web CMS, Graphics Editor, Video Editing Tools, HubSpot, GA4, Google Ads, Google Keyword Planner, Meta Ads, BigQuery/Looker Studio, Chrome, html/css, Zotero, Micro-Internship
Year 1 – Spring IBM 6510 – Foundations of Customer Analytics Core Data Analysis, Modeling, Statistical Inference R, RStudio/Positron, gtsummary, GitHub, Project
IBM 6100 – Search Engine Marketing Elective Search Advertising, Keyword Optimization, Performance Analytics Content Creation Tools, Web CMS, Chrome, Video Editing Tools, Google Ads Search Certification, Google Ads Display Certification, Search Console, SEMrush, GA4, GTM, Looker Studio, Micro-Internship
IBM 6150 – Database Marketing Elective Customer Segmentation, CRM, Automation SQL, Salesforce/HubSpot, Email Platforms, Tableau / Power BI, Content Creation Tools, GA4, GTM, BigQuery, Looker Studio, Project
IBM 6520 – Market Forecasting Elective Predictive Modeling, Forecasting, Time Series R (forecast, prophet), RStudio/Positron, Excel, GitHub, Project
Year 1 – Summer IBM 6300 – Retailing in Digital Economy Elective E-Commerce Strategy, Digital Merchandising, Customer Journey Analytics Shopify, Google Merchant Center, Amazon Ads, GA4, CRM Dashboards, Social Media Platforms, Web CMS, Content Creation Tools, Zotero, Looker Studio, BigQuery, Project
IBM 6540 – Applied Machine Learning in Marketing Elective ML Apps, Model Building, Evaluation R (caret, tidymodels), lavaan, Python (scikit-learn, TensorFlow), RStudio/Positron, GitHub, Project
IBM 6600 – Marketing Text Analytics with LLMs & AI Elective NLP, Text Mining, Generative AI R (Tidytext), RStudio/Positron, Python (transformers, spaCy), LLM/ChatGPT APIs, GitHub, Project
Year 2 – Fall IBM 6200 – Online Consumer Psychology & Behaviors Core Consumer Behavior, Experimental Design, Behavioral Insights, MSDM Culminating Experience Project UX Research Tools, A/B Test / Experiment, Project Management tool (e.g., Notion), Zotero, MSDM CEP
IBM 6500 – Customer Insights Methods & Survey Research Core Survey Design, Data Collection, Quant Research Qualtrics, SPSS, R, RStudio/Positron, Zotero, Project
IBM 6450 – AI in Marketing Elective Generative AI, Predictive AI, Automation LLM/GenAI Tools & Platforms, Social Media Platforms, Web CMS, Content Creation Tools, Project
Year 2 – Spring IBM 6250 – Social Media Marketing Elective Content Strategy, Influencer Marketing, Social Analytics Social Media Platforms, Content Creation Tools, Meta Business Suite, LinkedIn Campaign Manager, Google Ads Video Certification, Hootsuite, Project
IBM 6530 – Marketing Analytics Elective Data Integration, Dashboarding, KPI Reporting GA4, GTM, BigQuery, Looker Studio, R, RStudio/Positron, Shiny App, Quarto website, Attribution modeling, Project
IBM 6400 – Current Issues in Digital Marketing Core Emerging Trends, Ethics/Policy, Tech Applications Case/Toolkits vary by topic, Ethics / Privacy.
IBM 6800 – Data-Driven Digital Marketing Strategy I Core Data analysis, Insights generation, Strategic Integration, Campaign Planning, Team Project GA4, Looker Studio, R/Python, RStudio/Positron, gtsummary, Project management tool (e.g., Notion), MSDM CEP
Year 2 – Summer IBM 6950 – Data-Driven Digital Marketing Strategy II Core Plan execution, KPI’s, Capstone Integration, Presentation, Decision Support Social Media Platforms, Web CMS, Video Editing Tools, BI Dashboards, Visualization Tools, GA4, Project management tool, MSDM CEP
IBM 6700 – Marketing Data Management Elective Data Architecture, ETL, Governance R/Python, RStudio/Positron, SQL, BigQuery, Data Warehouse Tools (e.g., Databricks, snowflake), Project

2 Project Management Tools

2.1 List by Categories

When it comes to Digital Marketing and Marketing Analytics professionals, project management tools tend to prioritize campaign planning, collaboration, client tracking, performance dashboards, and content calendars.

Here’s a focused list by category 👇

2.1.1 All-in-One Marketing Project Management Platforms

Tool Key Features Why Marketers Love It
Asana Campaign templates, timeline view, cross-functional dashboards, and automated workflows. Clean interface and strong collaboration features for content, ads, and analytics teams.
Monday.com Visual campaign pipelines, automation, integrations with HubSpot, Meta Ads, Google Analytics. Ideal for managing multiple campaigns and creative production workflows.
ClickUp Combines tasks, docs, chat, goals, and reporting; strong dashboards for KPIs. Great for agencies and data-driven marketing teams needing both flexibility and structure.
Wrike (Marketing Suite) Campaign briefs, proofing tools, custom dashboards, and advanced reporting. Enterprise-grade system designed for marketing operations.

2.1.2 Content & Campaign Workflow Tools

Tool Key Features Best Use Case
Trello Visual Kanban boards for campaign progress, content calendars, and social media pipelines. Small teams and content creators managing multiple channels.
Notion Customizable marketing wiki, asset database, and content calendar templates. Marketing teams combining campaign planning, analytics notes, and brainstorming.
Airtable Spreadsheet-database hybrid with automations, filters, and integrations with ad platforms. Tracking influencers, campaign budgets, creative assets, and metrics.
CoSchedule Calendar-focused platform integrating with WordPress, Google Analytics, and social channels. Content-heavy marketing teams scheduling posts and tracking performance.

2.1.3 Analytics & Reporting-Oriented Tools (with Project Management Features)

Tool Key Features Best Use Case
Databox Centralized dashboards pulling data from GA4, HubSpot, Facebook Ads, etc. Analytics teams tracking KPIs and sharing reports.
HubSpot Marketing Hub Combines CRM, campaign tracking, automation, and analytics dashboards. Inbound marketing management and lead analytics.
Smartsheet (for Marketing) Campaign calendars, resource allocation, and integration with Power BI or Google Data Studio. Cross-department coordination of marketing analytics and execution.

2.1.4 Specialized Collaboration & Proofing Tools

Tool Key Features Purpose
Miro Visual collaboration whiteboard for brainstorming campaigns and data storytelling. Ideation, creative concept mapping, analytics visualization.
Slack + Asana / ClickUp integration Real-time communication linked to tasks. Keeps marketing and analytics updates centralized.
Google Workspace + Notion / Airtable integration Easy coordination of documents, campaign sheets, and reports. Streamlines workflows for marketing teams that already use Google tools.

2.2 Top Choices by Use Case

Scenario Recommended Tool
Agency managing multiple client campaigns ClickUp or Monday.com
In-house marketing & analytics team Asana or Wrike Marketing Suite
Solo marketer or small team Notion or Trello
Data-heavy marketing analytics team Airtable + Databox combo

2.3 Categories by Career

2.3.1 Marketing Analytics–Heavy Teams

These teams prioritize data integration, KPI dashboards, reporting automation, and collaboration between analysts and strategists.

Tool Strengths Data Integrations Reporting & Dashboards Collaboration & Workflow Pricing (approx.) Best For
ClickUp Combines task management, docs, goals, and dashboards; automations for analytics updates. Google Analytics, HubSpot, Meta Ads, Data Studio via Zapier. Custom KPI dashboards and widgets. Real-time chat, task automation, integrations with Slack. Free–$12/user/mo Hybrid marketing-analytics teams.
Asana (Business plan) Visual timelines, campaign tracking, portfolio dashboards. Google Analytics, Tableau, Power BI via connectors. Custom dashboards, CSV export. Smooth cross-functional task flow. Free–$25/user/mo Analytics teams coordinating with creative or media.
Wrike (Marketing Suite) Campaign briefing forms, proofing, analytics reporting. Google Ads, GA4, Adobe, Salesforce. Custom reports and visual dashboards. Advanced approval workflows. $24–$36/user/mo Enterprise analytics teams.
Smartsheet (Marketing) Excel-like interface with powerful automation and integrations. GA4, Power BI, Tableau, Salesforce. Robust reporting and Gantt charts. Strong versioning and permission control. $9–$32/user/mo Teams used to Excel/project data environments.
Airtable Database-like system; great for connecting marketing data and tagging campaigns. GA4, HubSpot, Meta Ads, Looker Studio. Dashboards through interfaces or apps. Simple collaboration and automations. Free–$20/user/mo Analysts managing datasets + project tracking.
Databox Pulls metrics from 70+ sources into dashboards. GA4, Facebook Ads, HubSpot, SQL, etc. Beautiful dashboards, goals, alerts. Comments, scheduled reports, Slack alerts. Free–$23+/user/mo Data visualization and performance monitoring.

Summary:
If you’re managing data-heavy dashboards and campaign KPIs, Databox + ClickUp or Airtable + Asana is a strong pairing — depending on whether you prefer dashboard-first or workflow-first setups.

2.3.2 Digital Content–Heavy Teams

These teams focus on creative production, content calendars, campaign coordination, and client approvals.

Tool Strengths Content Calendar Features Collaboration Tools File Management Pricing (approx.) Best For
Monday.com (Marketing Suite) Highly visual timelines, automation, creative review templates. Prebuilt campaign & content calendar boards. Integrated chat, notifications, automations. File storage + version control. Free–$12/user/mo Agencies managing multiple content campaigns.
Notion Customizable pages, databases, and kanban calendars. Flexible editorial calendar templates. Comments, mentions, real-time editing. Centralized asset library with embeds. Free–$10/user/mo Small content teams, brand storytelling.
Trello Simple Kanban with drag-and-drop and calendar power-ups. Calendar & timeline add-ons. Lightweight collaboration via comments. Attach files via Google Drive, Dropbox. Free–$10/user/mo Solo marketers or small content teams.
CoSchedule Native marketing calendar integrating with WordPress and GA4. Unified blog + social calendar. Comments, approval workflows. Syncs with cloud storage. $29+/user/mo Social media and content scheduling teams.
Airtable Database + gallery views for content tracking, visuals, and deadlines. Content & asset calendar templates. Real-time collaboration and approvals. Attachment support for media. Free–$20/user/mo Visual-heavy teams needing flexibility.
Wrike (Creative) Proofing, approvals, version tracking for creative assets. Gantt and list views. In-app feedback on visuals. Full digital asset management (DAM). $24–$36/user/mo Larger creative teams with multi-channel assets.

💡 Summary:
If your focus is content production and approvals, Monday.com or Wrike Creative are top choices for structure. For lightweight content strategy and writing workflows, Notion + CoSchedule pair beautifully.

5 SQL (Structured Query Language)

5.1 Introduction: The Role of SQL in Digital Marketing and Analytics

In today’s data-driven marketing landscape, the ability to access, understand, and analyze data directly is a defining skill for success. Among the many tools available, Structured Query Language (SQL) stands out as the universal language of data. SQL allows professionals to interact directly with databases — retrieving customer, campaign, and performance data that drive informed marketing decisions.

While SQL originated as a technical tool for database administrators and data engineers, it has become indispensable across many digital marketing roles.

  • Digital marketing analysts use SQL to extract and clean data from Google Analytics, CRM, and advertising platforms.

  • Marketing data scientists rely on SQL to join and aggregate large datasets before modeling or visualization.

  • Database marketing specialists use SQL to manage and segment customer lists for targeted campaigns.

  • Even digital marketing managers increasingly benefit from understanding SQL to ask better questions and interpret analytics outputs more effectively.

The rise of cloud-based data warehouses like Google BigQuery, Snowflake, and Amazon Redshift has made SQL skills even more valuable. These platforms use SQL as their core query language, allowing marketers to explore millions of records quickly — from customer purchase behavior to campaign ROI — without needing advanced programming.

This study guide provides a structured approach to learning Standard SQL (ANSI-compliant), focusing on how it applies to marketing and digital analytics contexts. You will learn how to:

  • Query and manipulate marketing data efficiently.

  • Perform audience segmentation and campaign performance analysis.

  • Combine SQL with tools like R or Python for deeper insights.

  • Prepare for data-driven roles such as Digital Marketing Analyst, Marketing Data Scientist, and Database Marketing Specialist.

By mastering SQL, digital marketers can go beyond dashboards and pre-built reports — they can uncover insights hidden in raw data, ask better questions, and make evidence-based decisions that drive measurable business outcomes.

5.2 Google BigQuerry

Google BigQuery is an excellent skill for digital marketing analytics and data science, especially if you are working with large-scale data (e.g., GA4 exports, ad platform data, CRM logs). Below are curated learning resources organized by type and skill level:

5.2.1 Google Cloud Skills Boost (Free Tier Available)

  • BigQuery for Data Analysts Learning Path
    Structured by Google itself; includes interactive labs (Qwiklabs).
    Covers:
    • BigQuery basics and UI
    • Writing and optimizing SQL
    • Loading and exporting data
    • Using BigQuery ML
    • Connecting BigQuery with Looker Studio

5.2.2 Documentation & Quickstarts

5.2.3 Video Courses

5.2.3.1 Beginner
  • YouTube – Google Cloud Tech Channel
    • “Introduction to BigQuery” (30 min concise overview)
    • “Analyzing Data with BigQuery” (hands-on examples)
  • freeCodeCamp (YouTube) – Google BigQuery Full Course for Beginners (4 hours, project-based)
5.2.3.2 Intermediate / Applied

5.2.4 Hands-on Practice

  • Google Cloud BigQuery Sandbox – free, no credit card required.
    Ideal for practice without billing worries.

  • Public Datasets to Explore:

    • bigquery-public-data.google_analytics_sample

    • bigquery-public-data.thelook_ecommerce (great for marketing data)

    • bigquery-public-data.hacker_news (for text data analysis)

5.3 SQL Learning Resources

Here’s a curated list of the best learning resources — from official documentation to hands-on tutorials, books, and YouTube channels — focused on teaching the standard core SQL concepts (not vendor-specific syntax like T-SQL or PL/SQL).

5.3.2 Mode Analytics SQL Tutorial

  • Why it’s great: Free, interactive, and designed for data analysts.

  • Focus: Core SQL concepts (SELECT, WHERE, GROUP BY, JOIN, subqueries).

  • Emphasis: Teaches why queries work, not just syntax.

  • Bonus: Uses real business-style datasets.

5.3.2.1 SQLBolt

  • Why it’s great: Clean, fast lessons and practice problems.

  • Focus: Standard SQL only — vendor-neutral and minimal jargon.

  • Good for: Building foundational fluency and quick refreshers.

5.3.2.2 W3Schools SQL Tutorial

  • Why it’s great: Simple, consistent examples; you can test queries live.

  • Focus: ANSI-standard SQL syntax and structure.

  • Good for: Beginners or for checking syntax quickly.

5.3.2.3 Kaggle Learn SQL Course

  • Why it’s great: Teaches SQL in an analytics context with practical exercises.

  • Focus: SELECT, filtering, aggregation, joins, and analytic use cases.

  • Good for: Analysts and data scientists.

5.3.3 Books for Deep Understanding

5.3.3.1 “SQL for Data Analytics” by Upom Malik, Matt Goldwasser, and Benjamin Johnston (O’Reilly, 2020)

  • Why it’s great: Explains how SQL supports data analysis and reporting.

  • Covers: Joins, subqueries, window functions, and real-world use cases.

  • Usefulness: Excellent bridge between SQL and analytics work.

5.3.3.2 “Learning SQL” (3rd Edition) by Alan Beaulieu (O’Reilly)

  • Why it’s great: Gold standard for learning ANSI SQL syntax deeply.

  • Covers: All major commands and best practices in a vendor-neutral way.

  • Good for: Self-learners or instructors wanting a structured curriculum.

5.3.3.3 “Practical SQL” by Anthony DeBarros

  • Why it’s great: Uses PostgreSQL, which follows ANSI SQL closely.

  • Focus: Realistic datasets (crime, demographics, etc.), with analysis questions.

  • Good for: Building real-world analytical SQL skills.

5.3.4 Practice Environments (Hands-On Learning)

5.3.4.1 Google BigQuery Sandbox

  • URL: https://cloud.google.com/bigquery/docs/sandbox

  • Free, serverless, and uses Standard SQL (ANSI-compliant).

  • Great for working with large, real-world public datasets like:

    • bigquery-public-data.thelook_ecommerce

    • bigquery-public-data.google_analytics_sample

5.3.4.2 SQLite

  • Why it’s great: Lightweight, fully ANSI SQL–compliant.

  • You can practice locally with a GUI like DB Browser for SQLite.

5.3.4.3 PostgreSQL

  • Why it’s great: Open-source and closest to ANSI SQL among major databases.

  • Use with pgAdmin or DBeaver to visualize and query data.

  • Excellent for learning schema design and advanced functions.

5.3.5 Video Courses (Visual & Hands-On)

5.3.5.1 freeCodeCamp – SQL Full Course for Beginners

  • URL: YouTube: freeCodeCamp SQL

  • Length: ~4 hours

  • Why it’s great: Practical, clear explanations with live query examples.

  • Focus: Core SQL features across dialects; ANSI-compliant syntax.

5.3.5.2 DataCamp – “Introduction to SQL”

5.3.6 Reference and Deeper Reading

5.4 SQL: Customized Learning Roadmap for MSDM Students

Here’s a customized resource and learning roadmap for MSDM students to master Standard SQL with a marketing analytics focus — emphasizing campaign, customer, and digital performance data analysis.

5.4.1 Goal

Learn Standard SQL deeply in the context of marketing data — so you can:

  • Query large datasets (e.g., GA4, eCommerce, CRM)

  • Analyze campaign and customer performance

  • Build insights to support digital marketing and data science workflows

5.4.2 Core SQL Learning (Standard + Analytics Context)

5.4.2.1 Mode Analytics SQL Tutorial – Marketing Data Focused

  • Teaches SQL using real analytical problems (sales, user retention, cohorts).

  • Focus: SELECT, JOIN, GROUP BY, HAVING, subqueries.

  • Application: customer segmentation, purchase funnel analysis.

  • Recommended pace: 1 lesson per day for 2 weeks.

5.4.2.2 Kaggle Learn – Intro to SQL

  • Dataset: eCommerce sales and product data.

  • You’ll practice:

    • Finding best-selling products.

    • Comparing customer spending by region.

    • Calculating repeat purchase rates.

  • Interactive notebooks — no setup needed.
5.4.2.3 SQLBolt

  • Focus on Standard SQL syntax (portable across all databases).

  • Use it as a drill tool for the first 10 lessons — perfect for classroom warm-ups.

5.4.3 Hands-On Marketing Datasets to Practice On

5.4.3.1 Google BigQuery Public Datasets

Accessible via the free BigQuery Sandbox
Use these to apply marketing analytics logic with Standard SQL:

These datasets allow cross-platform analytics (ads, web, sales) without needing credentials.
Dataset Description Example Marketing Analysis Query
bigquery-public-data.thelook_ecommerce Synthetic eCommerce data (orders, customers, events) Which age group contributes most to revenue?
bigquery-public-data.google_analytics_sample GA4-style website traffic data Which channels drive the most transactions by region?
bigquery-public-data.covid19_open_data (Optional) For social or behavioral trend analysis Did search interest change across regions during key events?

5.4.4 Tools for Practicing

Tool Use Case Why It’s Useful
Google BigQuery Sandbox Query large marketing datasets using Standard SQL Free, no setup, uses ANSI SQL
SQLite + DB Browser Practice SQL locally Lightweight & pure Standard SQL
PostgreSQL (via pgAdmin or DBeaver) Learn full relational logic Excellent for advanced joins & views
R + {DBI} / {bigrquery} Combine SQL + data science Ideal for marketing data pipelines in R

5.4.5 Books & Courses Tailored to Marketing Analytics

5.4.5.1 “SQL for Data Analytics” (O’Reilly)

  • Explains SQL using marketing-style datasets (eCommerce, campaign ROI).

  • Chapters on cohort analysis, funnel analysis, customer segmentation.

  • Skill level: Intermediate; great after learning basics.

5.4.5.2 “Practical SQL” by Anthony DeBarros

  • Uses PostgreSQL (very ANSI compliant).

  • Teaches you how to analyze customer, demographic, and event data.

  • Includes examples like “analyzing campaign results by region.”

5.4.5.3 freeCodeCamp: SQL for Data Analytics (YouTube)

  • Teaches practical analytical SQL using business-like data.

  • 4-hour video — highly visual, clear explanations.

  • Great for revisiting joins, grouping, and window functions.

5.4.6 Example Marketing Analytics Problems to Solve in SQL

Practice these with BigQuery or PostgreSQL after learning basics:

Analysis Task SQL Concept Example Query Prompt
Campaign ROI analysis JOIN, SUM, GROUP BY Combine ad_costs and revenue tables to find ROI per campaign.
Customer segmentation CASE, GROUP BY, AVG Classify customers into high, medium, low spenders.
Funnel analysis WINDOW, PARTITION BY, ORDER BY Track user journey from visit → add_to_cart → purchase.
Retention analysis DATE_DIFF, COUNT DISTINCT Calculate returning users by month.
Channel performance JOIN, FILTER, HAVING Compare CPC vs. conversion rate by ad channel.

5.5 Using SQL within R

5.5.1 Introduction: Using SQL Within R

In modern marketing analytics and data science, professionals often need to work with data stored in large, external databases or cloud data warehouses such as Google BigQuery, Snowflake, or PostgreSQL. While these systems use SQL for querying, analysts and data scientists frequently prefer to analyze, visualize, and model data in R.

The ability to use SQL within R bridges these two worlds — combining the efficiency and scalability of SQL databases with the flexibility and analytical power of R. This integrated approach allows analysts to query large datasets directly from R without fully loading them into memory, transforming R into a front-end interface for high-performance, database-backed analytics.

5.5.2 Who Should Consider This Approach

This method is particularly valuable for:

  • Marketing analysts and data scientists who work with large datasets (ad impressions, web logs, customer transactions) that exceed local memory limits.

  • Database marketing specialists who need to query and segment customer data efficiently while applying statistical or predictive models in R.

  • Researchers or graduate students who want to practice SQL and database handling within an R-centric workflow.

  • Organizations that use R for analytics but store their data in relational systems like BigQuery, PostgreSQL, or DuckDB.

In short, anyone who uses R as their main analysis environment but needs to pull, join, or filter data from databases can benefit from learning SQL within R.

5.5.3 Key Benefits

Benefit Explanation
Efficiency and Scalability Query only the data you need from large databases, saving time and system memory.
Seamless Integration Combine SQL queries with R packages for visualization, modeling, or reporting (e.g., ggplot2, tidymodels, Quarto).
Familiar Syntax Use dplyr verbs (e.g., filter(), mutate(), summarize()) that automatically translate into SQL — no need to rewrite code.
Performance Optimization Packages like Arrow and DuckDB handle large datasets efficiently, even beyond RAM limits.
Reproducibility Keep SQL queries embedded within R scripts or notebooks for transparent, documented workflows.

5.5.4 Potential Limitations

Limitation Mitigation or Consideration
Database Setup Required Requires connection setup via DBI or odbc; most tutorials include examples.
Learning Curve Understanding both R and SQL together can take time; start with simple queries and dbplyr.
Performance Depends on Backend Query speed and efficiency depend on the underlying database (e.g., local SQLite vs. cloud BigQuery).
Limited Write Operations The R–SQL interface is optimized for reading and querying; heavy data writes or schema changes are better done directly in SQL clients.

5.5.5 When to Use SQL Within R

Summary
Scenario Recommended Approach
Need to analyze large marketing datasets stored remotely (e.g., BigQuery, Snowflake) Use DBI + dbplyr to query within R
Working with Parquet or Arrow files locally Use {arrow} to read and query data efficiently
Building automated marketing analytics workflows Integrate SQL queries into R scripts or Shiny dashboards
Combining SQL querying with modeling or visualization Use SQL within R for data extraction, then analyze in R

Using SQL within R empowers analysts to query big data and analyze results seamlessly in one environment. It reduces data transfer, improves reproducibility, and helps marketing professionals unlock insights from enterprise-level databases without switching tools.

The following resources will guide you through this workflow — from foundational database connections using DBI and dbplyr to advanced integrations with Arrow for high-performance analytics.

5.5.6 Learning Resources

  • “Using DBI with Arrow” (R‑DBI blog)
    This tutorial walks through how DBI’s new Arrow-oriented generics work—such as dbReadTableArrow(), dbGetQueryArrow(), and more—and shows how to improve performance and type fidelity using Arrow streams instead of traditional data frames.
    YouTube+13R Database Interface+13CRAN+13Links to an external site.

  • CRAN’s “Using DBI with Arrow” vignette
    Official documentation on DBI’s Arrow integration, complete with code examples using dbReadTableArrow(), dbGetQueryArrow(), dbBindArrow(), and chunk-based streaming results.
    CRANLinks to an external site.

  • Apache Arrow R Cookbook – “Manipulating Data – Tables”
    Explains how to use dplyr verbs directly on Arrow tables via arrow_table() and how lazy evaluation and efficient in-memory formats help when working with larger-than-memory data.
    YouTube+15Apache Arrow+15Apache Arrow+15Links to an external site.

  • R for Data Science 2nd Edition – Chapter 22: Arrow
    Shows how to use Apache Parquet files and the Arrow package in R, demonstrating how to manipulate them with familiar dplyr syntax and explaining performance features and partitioning.
    R for Data Science+1Links to an external site.

  • R for Data Science 2nd Edition – Chapter 21: Databases
    A foundational walkthrough of DBI and dbplyr, teaching how to connect to databases, query using dplyr-style syntax, and how SQL translation under the hood works.
    Apache Arrow+15R for Data Science+15YouTube+15Links to an external site.

  • R‑Squared Academy – “Chapter 2: dbplyr”
    A step-by-step guide with code examples demonstrating how to connect to a database using DBI, copy data into it, and query it via tbl() from the dbplyr approach.
    R Squared AcademyLinks to an external site.

5.5.7 YouTube Video Tutorials

“Accessing SQL Databases in R: Three Approaches” – A clear and practical walkthrough of accessing SQL databases from R using DBI, dbplyr, and a direct SQL method.

Other helpful videos:

  • TidyX Episode 70: Databases with {dbplyr}
    A friendly intro to using dbplyr in real-world contexts (“Making friends with your database admin…”). Shows how to use dplyr commands to interface with actual databases.
  • Resolving Memory Issues with arrow, duckdb, and dbplyr
    A focused video that shows how to optimize memory usage in R when using arrow, duckdb, and dbplyr—great for handling large datasets beyond memory limits.
  • How to Properly Connect to Postgres Using DBI in R
    A practical tutorial on setting up DBI connections to PostgreSQL (which parallels other DBI backends) and making it work reliably with dbplyr.

5.5.8 Quick Reference Table

Resource Type Purpose
Web Guides In-depth code examples and conceptual clarity for DBI, dbplyr, and Arrow integration.
YouTube Videos Visual tutorials perfect for learners who prefer to watch setup and execution in real time.
5.5.8.1 Wrap-Up

  • Start with Chapter 21 (Databases) of R4DS for DBI + dbplyr fundamentals.

  • Explore Chapter 22 (Arrow) for efficient, memory-savvy workflows with Parquet and Arrow.

  • Use the DBI + Arrow tutorials (CRAN and R-DBI blog) to dive deeper into streaming and Arrow-native performance optimizations.

  • Supplement with videos like “Accessing SQL Databases in R” and memory optimizations for guided, example-rich learning.

6 Certifications

There are many certifications. We selected some representative certifications based on reputation, relevance to the industry, rigor, ad skills, organized by progressive skill level — from foundational to advanced — and aligned with what’s most relevant for MS in Digital Marketing and Analytics (MSDM) students.

6.1 Foundational Digital Marketing Certifications

Goal: Build essential skills and credentials recognized across marketing roles.

✅ Recommended for first-semester or early-program students starting to build their professional portfolio.
Certification Provider Reputation Relevance Cost / Access Notes
Google Digital Marketing & E-commerce Certificate Google / Coursera ★★★★★ Broad coverage of SEO, SEM, e-commerce, and measurement Free to audit / ~$39/mo Excellent “entry point” into digital marketing
HubSpot Digital Marketing Certification HubSpot Academy ★★★★☆ Inbound marketing, content, email, automation Free Ideal for CRM-driven and content-focused students
Google Ads (Search, Display, Video) Google Skillshop ★★★★★ Paid advertising and PPC campaign skills Free Recognized by agencies and performance marketers
Google Analytics Certification (GA4) Google Skillshop ★★★★★ Website & app analytics Free Core credential for any digital marketing student

6.2 Professional / Applied Analytics Certifications

Goal: Strengthen data literacy and marketing measurement capabilities.

✅ Recommended for students who completed foundational certificates and want to demonstrate applied analytics and insight-generation skills.
Certification Provider Reputation Rigor Cost / Access Notes
Meta Marketing Analytics Professional Certificate Meta / Coursera ★★★★☆ Moderate–high ~$39/mo (free audit available) Focused on campaign measurement, A/B testing, and interpreting marketing data
Tableau Desktop Specialist Tableau / Salesforce ★★★★☆ Moderate ~$100 exam fee Builds strong visualization and dashboarding skills
Microsoft Power BI Data Analyst Associate Microsoft ★★★★☆ Moderate–high ~$165 exam Enterprise-focused analytics certification
LinkedIn Marketing Strategy Certification LinkedIn Learning ★★★★☆ Moderate Often free via campus library Connects analytics to B2B and brand strategy

6.3 Advanced / Strategic Certifications

Goal: Develop high-level strategic and analytical expertise for leadership or analyst-track roles.

✅ Recommended for graduate-level students aiming for managerial, data science, or strategy positions.
Certification Provider Reputation Rigor Cost / Access Notes
Google Data Analytics Professional Certificate Google / Coursera ★★★★★ High ~$39/mo Teaches SQL, R, data storytelling, and statistical analysis
Google Marketing Platform (GA, Display & Video 360) Google Skillshop ★★★★☆ High Free Advanced ad tech and programmatic analytics
Wharton Online: Digital Marketing Strategy Wharton / edX ★★★★★ High ~$585 Academic depth + strategic focus
Digital Marketing Institute (DMI) Certified Digital Marketing Professional (CDMP) DMI + AMA ★★★★★ High ~$1,500 Industry gold standard for digital marketing mastery

6.4 Specialized Channel & Platform Certifications

Goal: Diversify expertise with focused, platform-based competencies.

✅ Recommended for students interested in specific domains (social, retail, influencer, or platform management).
Certification Provider Reputation Focus Cost / Access Notes
Hootsuite Social Media Marketing Certification Hootsuite Academy ★★★☆☆ Social media management ~$199 (discounts for education) Practical for agencies and community management
Meta Social Media Marketing Professional Certificate Meta / Coursera ★★★★☆ Paid and organic social media ~$39/mo Complements analytics by focusing on content and engagement
Amazon Advertising Certification Amazon Learning Console ★★★★☆ E-commerce & retail media Free Growing relevance in digital retail marketing

7 CCIDM Workshops

Check out the on-demand workshops prepared for students by the center.

8 DataCamp Virtual Classroom

I offer access to DataCamp’s virtual classroom, which provides training for many of the analytics tools used in the MSDM program. If you would like to take advantage of the free certification courses, please contact me. Several MSDM students are already participating in this program.

Although I can enroll you in the virtual classroom, I do not teach or grade these courses. Each course is taught by DataCamp instructors and is self-paced. You will:

  • Watch instructional videos

  • Complete practice questions

  • Code in a web-based interactive environment

The platform provides instant feedback, making the learning experience efficient and engaging.