Career Guide for Master of Science in Digital Marketing Students

Skills, Career Readiness, and Resources

A comprehensive guide to job titles, skills, and resources for careers in digital marketing and marketing analytics
Author
Affiliation

MSDM Faculty

Cal Poly Pomona

Published

January 9, 2025

Introduction

Important

Digital Marketing and Marketing Analytics offer exciting, high-impact career paths where creativity meets data-driven insight. This page is designed to help you envision your future, understand the skills and tools that will set you apart, and chart a path toward meaningful, innovative work. Whether you aspire to shape digital strategy or uncover insights through analytics, this guide will help you build the confidence, capabilities, and direction needed to pursue a career that makes a difference.

Please note that coursework alone may not provide complete proficiency in every tool or skill you hope to master. To become truly career-ready, you should make a concerted effort to train yourself beyond the classroom.

Take advantage of the many extracurricular opportunities—workshops, symposiums, guest speakers, and more—offered by the Center for Customer Insights and Digital Marketing, the MSDM Club, and programs across the university. These experiences will deepen your learning and help you stand out in a competitive marketplace.

1 Skills Employers Look for in Digital Marketing Roles

1.1 Core Digital Marketing Skills

Employers expect a strong grounding in marketing principles + digital execution:

  • SEO/SEM: Keyword research, on-page SEO, technical SEO, Google Ads.

  • Content Marketing: Strategy, writing, optimizing for engagement & conversions.

  • Social Media Marketing: Paid/organic campaigns across platforms, audience targeting.

  • Email Marketing & CRM: Segmentation, A/B testing, lifecycle campaigns.

  • Performance Marketing: Paid search, display, social ads, ROI optimization.

  • Marketing Automation: HubSpot, Salesforce Marketing Cloud, Marketo, etc.

  • Campaign Analytics: Attribution models, funnel analysis, ROI tracking.

1.2 Analytics & Data Skills

This is where R, Python, SQL, and BI tools give candidates a big edge:

  • Data Wrangling & Analysis: SQL (databases), R/Python for data cleaning and modeling.

  • Web & Campaign Analytics: Google Analytics 4 (GA4), Tag Manager, Looker Studio.

  • Marketing Mix Modeling (MMM) & Attribution: Understanding how channels drive results.

  • A/B Testing & Experimentation: Experimental design, statistical analysis.

  • Customer Analytics: Segmentation, lifetime value prediction, churn modeling.

  • Forecasting: Time-series forecasting of sales, demand, or campaign KPIs.

  • Dashboarding & Visualization: Tableau, Power BI, R Shiny, or Quarto for storytelling.

  • Big Data Tools (nice-to-have): Working with APIs, warehouses (BigQuery, Snowflake), Arrow/duckdb for large datasets.

1.3 Tech & Emerging Tools

  • Quarto / RMarkdown: Reproducible reports & documentation.

  • Machine Learning Basics: Predictive modeling, recommendation systems (entry-level knowledge is often enough for marketing roles).

  • AI for Marketing: Understanding how GenAI is applied to content creation, personalization, and customer insights.

1.4 Business & Soft Skills

This often makes or breaks candidates:

  • Storytelling with Data: Translating analytics into business recommendations.

  • Project Management: Agile workflows, cross-team collaboration.

  • Communication: Explaining technical insights to non-technical stakeholders.

  • Problem Solving: Turning ambiguous marketing challenges into testable, data-driven solutions.

  • Commercial Awareness: Understanding ROI, CAC, CLV, and KPIs that matter to business leaders.

1.5 Skill Bundles by Role Type

To help your students target different flavors of digital marketing roles:

  • Digital Marketing Analyst / Data Scientist
    • SQL, R/Python, A/B testing, MMM, GA4, dashboarding.
  • Performance Marketing Specialist
    • Paid ads, GA4, attribution, campaign optimization, Excel/SQL.
  • Marketing Automation / CRM Specialist
    • Email automation tools, segmentation, lifecycle marketing, A/B testing.
  • Growth Marketer
    • Blend of analytics + campaign execution + experimentation mindset.
  • Digital Marketing Manager (strategic)
    • Team leadership, vendor management, high-level analytics (dashboards, ROI tracking).
Important👉 Bottom Line:

Employers want marketers who can work with data. Even if they’re not “full data scientists,” knowing SQL + R/Python basics + GA4 + A/B testing gives them a big competitive edge in digital marketing roles. Pair that with strong storytelling and business impact communication, and they’re highly employable.

1.6 Social Media Marketing Managers & Content Creators

1.6.1 Core skills they must have:

  • Platform expertise: Meta, TikTok, LinkedIn, YouTube, X (ads + organic).

  • Content strategy: Matching brand voice with platform audience.

  • Creative skills: Copywriting, design tools (Canva, Adobe suite), short-form video.

  • Community management: Engaging audiences, managing comments, brand reputation.

  • Campaign performance: Reading dashboards (reach, engagement, CTR, conversions).

1.6.2 Data skills (nice-to-have, but not the core):

  • Basic analytics literacy:

    • Understanding metrics (CTR, engagement rate, conversion rate, ROAS).

    • A/B testing creatives (e.g., which ad copy/video works better).

  • Intermediate (optional, career-boosting):

    • SQL/R/Python not essential, but helpful if they want to grow into a broader analytics or digital marketing manager role.

    • Knowing how to pull performance data from APIs (e.g., Meta Ads, Google Ads) is a differentiator, but not expected for most content roles.

Important👉 Bottom line:

SMMs and content creators don’t need deep DBI/dbplyr/Arrow-level skills. They need to be data-aware — capable of interpreting dashboards and making creative/strategy decisions from them.

1.7 Digital Marketing Managers

1.7.1 Core expectations:

  • Strategic oversight of campaigns across channels (search, social, display, email, web).

  • Budget management: Allocating spend across channels for ROI.

  • Team coordination: Overseeing content, social, analytics, and performance specialists.

  • Vendor/agency management: Communicating expectations and results.

1.7.2 Data skills (increasingly important):

  • Definitely needed:

    • Comfort with dashboards (GA4, Looker Studio, Tableau, Power BI).

    • Ability to interpret attribution reports (which channels drive conversions).

    • Understanding MMM outputs or campaign lift studies (even if they don’t build the models).

    • Running/overseeing A/B tests (e.g., subject lines, ad targeting).

  • Not always required:

    • Hands-on SQL/R/Python isn’t required in every company — but managers who can at least query a database or validate results have a strong edge.

    • In larger organizations, they’ll work with analysts who do the heavy lifting.

    • In startups/lean teams, managers often do both strategy + basic analytics, so SQL/Excel may be expected.

Tip

👉 Bottom line:

Digital Marketing Managers must be data-literate (able to interpret and act on analytics). They don’t always need to code models themselves, but they need to manage data-driven decision-making. If they aspire to higher-level performance or growth roles, learning SQL and analytics basics is a significant advantage.

1.8 Practical Advice for Students

  • Content/Social-focused careers → Prioritize creative skills, platform mastery, and basic analytics literacy.

  • Managerial/strategic careers → Prioritize data literacy and decision-making with data. Hands-on coding isn’t always mandatory, but understanding the outputs of analytics (MMM, attribution, segmentation) is critical.

Caution👉 Do They Need Analytics?

  • Social media managers & content creators don’t need deep data science skills — dashboards and campaign analytics literacy are enough.

  • Digital marketing managers do need stronger data skills (not necessarily coding, but strong analytics interpretation). Those who can bridge creativity + analytics are the ones who move up fastest.

1.9 Social Media Manager vs. Digital Marketing Manager vs. Marketing Data Scientist

Role Core Skills & Responsibilities Data Skills & Tools (Required / Nice-to-Have)
Social Media Manager
  • Content creation, copywriting, design (video/images)
  • Community engagement and customer service
  • Trend awareness & adaptability
  • Strategic campaign planning
  • Interpreting engagement metrics (reach, likes, CTR)
  • Basic analytics (built-in platform dashboards)
  • Data storytelling and reporting1
Digital Marketing Manager

  • Campaign strategy across channels

  • Budgeting and ROI focus

  • Leadership and vendor coordination

  • Multichannel storytelling

  • Interpreting attribution models, MMM outputs, A/B test results

  • Dashboard literacy (GA4, Looker Studio, Tableau/Power BI)

  • (Optional) ability to query data/SQL for validation2 3
Marketing Data Scientist

  • Advanced modeling: MMM, CRM, churn, forecasting, segmentation

  • Analytical experimentation (A/B tests, lift studies)

  • Translating insights into strategy

  • Hands-on with SQL, R/Python

  • Database interfacing, data warehouses, Parquet/Arrow, dbplyr pipelines

  • Building dashboards or Shiny/Quarto reports (optional)

1.10 Role-by-Role Insights:

1.10.1 Social Media Managers & Content Creators

  • They need to be creative, adaptive, and engaging—focused on content + community. Analytics skills are mainly about interpreting platform metrics and crafting reports for stakeholders.

  • Employers increasingly value data storytelling—turning metrics into actionable insights. GBench tough: “social listening, data storytelling and creative direction” are core skills in 2025. (The Times of India+15Sprout Social+15Floowi Talent+15)

1.10.2 Digital Marketing Managers

  • They are strategic leaders—balancing budgets, overseeing multichannel campaigns, and making decisions grounded in performance metrics.

  • Data literacy is critical: they must interpret analytics, oversee A/B tests and attribution analyses, and monitor ROI. Some roles demand dashboard literacy and familiarity with MMM outputs. (CareerFoundryDigital Marketing InstituteBusiness Insider)

  • Employers are casting a wider net: “data literacy and analytical fluency” are among the top skills giving candidates an edge in 2025. (thetimes.co.uk+3Business Insider+3The Times of India+3)

1.10.3 Marketing Data Scientists

  • This role is all about data—building predictive models, designing experiments, segmenting customers, and forecasting trends.

  • The necessary tools span SQL, R or Python, database connections, workflows with Arrow or dbplyr, and building dashboards. These roles thrive on technical depth and the ability to deliver analytics as products to stakeholders and managers.

1.11 Summary: Do They Need Data Skills?

  • Social Media Managers & Content Creators
    → Yes, but moderately. They need analytics literacy—especially interpreting engagement data and telling data-driven stories—but not deep modeling or coding.

  • Digital Marketing Managers
    → Absolutely. Data literacy and competency in analytics tools are essential. While not all need to code, understanding and evaluating data-driven insights (MMM, attribution, tests) is critical for managing strategy and teams effectively.

1.12 Contextual Backing from Industry:

  • A recent report highlights how “data literacy and analytical fluency” are indispensable for standing out in the modern job market. (Mayple+5Teal+5Floowi Talent+5Rankvise+3Digital Marketing Institute+3CareerFoundry+3)

  • In advertising and marketing, recruiters are increasingly demanding candidates who can operate at the nexus of data, tech, and content, with a mindset oriented toward insight-driven decision-making.

2 Common Digital Marketing Job Titles & Career Readiness

2.1 Digital Marketing Specialist / Manager

  • 2.1.0.1 Focus: Broad strategy + execution across multiple channels.

  • 2.1.0.2 Skills Needed:

    • Campaign planning & execution (search, social, email, display).

    • Analytics (Google Analytics 4, reporting dashboards).

    • Basic SEO/SEM knowledge.

    • Budget management, ROI tracking.

  • 2.1.0.3 Preparation in MS Program:

    • Coursework in marketing strategy, channel mix, campaign optimization.

    • Hands-on projects using GA4, Meta Ads, Google Ads.

    • Case studies on integrated campaigns.

2.2 Social Media Marketing Specialist / Manager

  • 2.2.0.1 Focus: Content & engagement across platforms (Instagram, TikTok, LinkedIn, YouTube).

  • 2.2.0.2 Skills Needed:

    • Content creation & curation.

    • Community management & social listening tools (Sprout Social, Hootsuite).

    • Paid social ad platforms (Meta, TikTok Ads Manager, LinkedIn Ads).

    • Analytics (engagement, reach, sentiment).

  • 2.2.0.3 Preparation in MS Program:

    • Social media labs (hands-on posting, campaign tracking).

    • Training in social analytics & influencer strategy.

    • Understanding platform algorithms & trends.

2.3 Search Engine Marketing (SEM) Specialist / PPC Specialist

  • 2.3.0.1 Focus: Paid search campaigns (Google Ads, Bing Ads).

  • 2.3.0.2 Skills Needed:

    • Keyword research, ad copywriting.

    • Bidding & optimization strategies.

    • Conversion tracking & ROI measurement.

    • Google Ads & Microsoft Advertising certifications.

  • 2.3.0.3 Preparation in MS Program:

    • Google Ads certification as part of coursework.

    • Simulation projects with live campaigns.

    • Training in keyword tools (SEMrush, Ahrefs).

2.4 SEO Specialist / Manager

  • 2.4.0.1 Focus: Organic visibility & traffic through search optimization.

  • 2.4.0.2 Skills Needed:

    • On-page SEO (content, metadata, technical SEO).

    • Off-page SEO (backlink building).

    • Tools: Google Search Console, Ahrefs, Screaming Frog.

    • Basic HTML/CSS awareness.

  • 2.4.0.3 Preparation in MS Program:

    • Projects on SEO audits & optimization plans.

    • Competitor keyword analysis assignments.

    • Exposure to website CMS (WordPress, Shopify).

2.5 Email Marketing Specialist / CRM Manager

  • 2.5.0.1 Focus: Customer retention & engagement via email and CRM.

  • 2.5.0.2 Skills Needed:

    • Email campaign setup (HubSpot, Mailchimp, Salesforce Marketing Cloud).

    • Segmentation & personalization strategies.

    • A/B testing subject lines & content.

    • Knowledge of deliverability & compliance (GDPR, CAN-SPAM).

  • 2.5.0.3 Preparation in MS Program:

    • Lab assignments in CRM/email platforms.

    • Campaign segmentation projects.

    • Testing frameworks for optimization.

2.6 Database Marketing / Marketing Automation Specialist

  • 2.6.0.1 Focus: Customer data-driven marketing, automation workflows.

  • 2.6.0.2 Skills Needed:

    • CRM systems (Salesforce, HubSpot, Marketo).

    • SQL basics for segmentation queries.

    • Journey mapping & trigger-based campaigns.

    • Integration of data sources (APIs, CDPs).

  • 2.6.0.3 Preparation in MS Program:

    • Training in CRM tools and data integration.

    • Case projects linking consumer data → campaign strategy.

    • Exposure to privacy-first marketing (cookies, data ethics).

2.7 Content Marketing Specialist / Manager

  • 2.7.0.1 Focus: Content strategy for brand awareness & engagement.

  • 2.7.0.2 Skills Needed:

    • Writing, editing, storytelling.

    • SEO for content.

    • Content planning across blogs, video, whitepapers.

    • Analytics: content performance tracking.

  • 2.7.0.3 Preparation in MS Program:

    • Content strategy workshops.

    • SEO + content calendar projects.

    • Integration with paid & organic campaigns.

2.8 E-commerce / Performance Marketing Specialist

  • Focus: Driving online sales via paid performance campaigns.

  • Skills Needed:

    • E-commerce platforms (Shopify, Magento, Amazon Ads).

    • Conversion rate optimization (CRO).

    • Performance ad channels (Google Shopping, Meta, TikTok).

    • ROAS, LTV, CAC metrics.

  • Preparation in MS Program:

    • Case studies on retail media & performance ads.

    • CRO assignments (landing page tests).

    • E-commerce project labs.

2.9 Comparison Across Roles

Role Breadth vs. Depth Data Intensity Creativity Tech/Tools
Digital Marketing Specialist Broad (multi-channel) Medium Medium GA4, Ads, CRM
Social Media Specialist Narrow (platform focus) Low–Medium High Sprout, Ads Manager
SEM Specialist Narrow (search ads) High Low–Medium Google Ads, SEMrush
SEO Specialist Narrow (organic search) Medium Medium Search Console, Ahrefs
Email/CRM Specialist Medium High Medium Mailchimp, HubSpot
Database Marketing Specialist Narrow (data-driven) High Low SQL, Salesforce
Content Marketing Specialist Broad (content channels) Medium High CMS, SEO tools
E-commerce Specialist Medium High Medium Shopify, Amazon Ads

2.9.1 Preparation During MS in Digital Marketing

  • Certifications: Google Ads, GA4, HubSpot, Meta Blueprint.

  • Hands-on Tools: SQL basics, Tableau/Power BI, CRM systems, SEO tools.

  • Analytics Skills: A/B testing, ROI analysis, attribution.

  • Projects: Live case studies, simulations, client projects where possible.

  • Soft Skills: Storytelling with data, cross-team communication, project management.

2.9.2 Conclusion:

  • Digital Marketing Specialist/Manager roles = best for students who like broad exposure.

  • Channel Specialists (SEO, SEM, Social, Email) = best for students who want depth in one area.

  • Data/Automation Specialists = for students who lean analytical and technical.

  • Preparation in the MS program should blend certifications, tool practice, analytics, and projects to match whichever track they want to pursue.

2.10 Digital Marketing Job Titles Matrix

Job Title Core Skills Preparation in MS Program
Digital Marketing Specialist/Manager Multi-channel campaigns, GA4, ROI tracking, Budgeting Campaign simulations, GA4 projects, Google Ads training
Social Media Marketing Specialist/Manager Content creation, Community management, Paid social, Social analytics Social media labs, Analytics training, Influencer strategy
Search Engine Marketing (SEM) Specialist Keyword research, PPC optimization, Conversion tracking Google Ads certification, SEM tools projects, Keyword analysis
SEO Specialist/Manager On-page SEO, Technical SEO, Backlinks, SEO tools SEO audit projects, Keyword research labs, CMS exposure
Email Marketing Specialist/CRM Manager Email platforms (HubSpot, Mailchimp), Segmentation, A/B testing CRM/email labs, Segmentation projects, A/B testing exercises
Database Marketing/Automation Specialist CRM systems, SQL basics, Automation workflows, Data integration CRM tools, SQL practice, Data integration projects
Content Marketing Specialist/Manager Content strategy, SEO for content, Analytics, Storytelling Content strategy workshops, SEO + content projects
E-commerce/Performance Marketing Specialist E-commerce platforms (Shopify, Amazon), CRO, Performance ads, ROAS E-commerce projects, CRO assignments, Retail media case studies

3 How to Prepare for DMS Careers

Students should select electives, projects, and certifications that reinforce those fits—e.g., SEM/PPC for Analytical+Performance; Social/Content for Creative; CRM/Automation for Technical+Analytical; or a broad Digital Marketing Specialist path for balanced profiles.

3.1 Digital Marketing Specialist / Manager

  • Certs: Google Ads (Search), GA4 Basics; optional Meta Blueprint.

  • Projects: Integrated campaign plan + UTM tracking + post-mortem.

  • Tools: Looker Studio/Tableau dashboard; budget pacing sheet.

3.2 Social Media Marketing Specialist / Manager

  • Portfolio: 4-week content calendar, example posts, community guidelines.

  • Paid Social: Ads Manager (Meta/TikTok/LinkedIn) basics + reporting pack.

  • Analytics: Platform insights; creator/influencer brief.

3.3 Content Marketing Specialist / Manager

  • SEO + Editorial: Topic clusters, keyword briefs, internal linking plan.

  • Artifacts: 3 sample posts in different formats (article, video outline, carousel).

  • Measurement: Content scorecard (traffic, engagement, assisted conv.).

3.4 SEM / PPC Specialist

  • Certs: Google Ads Search & Shopping.

  • Exercises: Keyword research; ad testing plan; quality score improvement.

  • Implementation: Conversion tracking (incl. enhanced conversions), feed hygiene.

3.5 SEO Specialist / Manager

  • Audits: Technical (CWV, crawl, sitemap), on-page, off-page/backlinks.

  • Tools: GSC, Screaming Frog, Ahrefs/SEMrush; basic schema examples.

  • Deliverable: 90-day SEO roadmap with quick wins vs. projects.

3.6 Email Marketing Specialist / CRM Manager

  • Platform Lab: Build a lifecycle journey (onboarding, re-engagement, winback).

  • Segmentation: RFM or events-based cohorts; A/B subject/content tests.

  • Compliance: CAN-SPAM/GDPR basics; deliverability checklist.

3.7 Database / Marketing Automation Specialist

  • Data: SQL labs (joins, CTEs); warehouse basics (BigQuery/Snowflake).

  • Automation: Event-triggered programs; API/ETL overview; CDP concepts.

  • Docs: Tracking plan + data dictionary + field governance.

3.8 E-commerce / Performance Marketing Specialist

  • Shopping/Retail Media: Feed management; Merchant Center; marketplaces.

  • CRO: Landing page testing plan; funnel instrumentation; heatmaps.

  • Finance: ROAS vs. contribution margin; LTV:CAC cohorts; pacing.

3.9 Electives & Certifications (mix-and-match)

  • Core analytics: GA4, Looker Studio/Tableau, A/B testing & experiment design.

  • Search: Google Ads (Search/Shopping), SEO audit lab.

  • Social: Meta/TikTok Ads Manager basics, community management.

  • CRM/Automation: HubSpot/Braze/Marketo fundamentals; SQL for marketers.

  • E-commerce: Retail media (Amazon/Walmart), feed management, CRO.

  • Privacy & Data: Consent, tagging plans, server-side tracking basics.

4 Marketing Analysts and Marketing Data Scientists: Skills and Knowledge

4.1 Career Preparations for Marketing Analytics Career

Data science jobs tailored for marketing is different from regular data science jobs or business analytics. So, again, I consulted GPT 5. My MS in Digital Marketing students asked me if they will be ready for job market as marketing data scientist if they use R and don’t use Python. What advice would you give them?

4.1.1 R is absolutely a marketable skill, but Python often gives an edge

  • R strengths: It’s fantastic for statistics, econometrics, experimental design, marketing mix modeling, and survey data analysis—all things marketing analysts and data scientists need. Many marketing and social science–oriented companies (consultancies, CPG, ad-tech, agencies) rely on R.

  • Python strengths: It dominates in machine learning engineering, AI, data pipelines, and production-level work. If the job involves deploying models at scale or working closely with engineers, Python is usually the standard.

4.1.2 Employers care about the core skills

  • Knowing how to design experiments, analyze consumer data, build predictive models, interpret insights, and communicate recommendations matters more than the specific tool.

  • If you can demonstrate strong knowledge in data wrangling, modeling, visualization, and storytelling, you’re employable—whether in R, Python, or even SQL + BI tools.

4.1.3 Bridging R to the job market

  • For analytics-heavy roles (marketing research, econometrics, MMM, A/B testing, segmentation, customer insights): R is more than enough.

  • For ML/AI-heavy roles (recommendation systems, NLP on consumer text, computer vision for ad content): You’ll likely need Python.

4.1.4 What I’d recommend to them

  • Get excellent at R first. Become fluent in tidyverse, modeling packages, and reporting (e.g., RMarkdown/Shiny).

  • Pick up Python basics along the way (especially pandas, scikit-learn, maybe TensorFlow/PyTorch if they’re ambitious). Even if they’re not fluent, just showing “working knowledge” reduces employer concerns.

  • Always highlight transferable skills. Frame your résumé and interviews around what you can do with data (segmentation, predictive models, campaign optimization), not just which software you use.

Tip👉 So the bottom line to students would be:

Yes, you can be job-ready with just R for many marketing analytics and data science roles. But if you want to maximize opportunities—especially in ML-heavy or tech-company contexts—adding at least some Python competency will make you more competitive.

4.2 Marketing Data Scientist Job-Readiness Roadmap

4.2.1 Core Analytics & Marketing Knowledge

  • Be able to frame business problems as data problems

  • A/B testing & experimental design (campaign testing, website optimization)

  • Marketing Mix Modeling (MMM) / attribution models

  • Customer segmentation & clustering

  • Lifetime value prediction & churn analysis

  • Price elasticity and promotion lift modeling

4.2.2 Programming & Tools

4.2.2.1 Must-Have

  • R (strong foundation)

    • Data wrangling: dplyr, tidyr

    • Visualization: ggplot2, plotly

    • Modeling: caret, tidymodels, lavaan (SEM), forecast/prophet

    • Reporting: RMarkdown, Shiny

  • SQL

    • Querying large marketing/customer databases

    • Joins, aggregations, window functions

4.2.2.2 Nice-to-Have (to maximize opportunities)

  • Python (working knowledge)

    • pandas, numpy, scikit-learn for predictive modeling

    • Optional: tensorflow/pytorch for deep learning if going ML-heavy

  • BI Tools: Tableau or PowerBI (business-facing dashboards)

4.2.3 Math & Stats Foundation

  • Regression (linear, logistic, regularized)

  • Hypothesis testing, ANOVA, chi-square

  • Bayesian inference (useful for MMM & A/B testing)

  • Time series forecasting

4.2.4 Business & Storytelling Skills

  • Turning statistical outputs into marketing recommendations

  • Visualization for non-technical stakeholders

  • Writing executive-friendly reports (e.g., “Campaign X lifted ROI by 12%”)

  • Communicating uncertainty and trade-offs

4.2.5 Portfolio Project Ideas (Show Employers!)

Encourage students to publish on GitHub + LinkedIn:

  1. Marketing Mix Model (MMM) on simulated data — estimate ROI of channels

  2. Customer Segmentation with clustering (K-means / hierarchical / mixture models)

  3. A/B Test Simulation — show how to design, analyze, and interpret results

  4. Customer Churn Prediction — build a classification model from CRM data

  5. Sentiment Analysis on customer reviews or social media (Python-friendly add-on)

  6. Interactive Dashboard (R Shiny or Tableau) for campaign performance

4.2.6 Job Search Positioning

  • Frame yourself as: “I use data to optimize marketing decisions and drive ROI.”

  • Tailor résumé to highlight:

    • Tools: R, SQL, some Python

    • Skills: A/B testing, MMM, segmentation, forecasting, churn prediction

    • Communication: dashboards, storytelling

4.2.7 Final Advice

  • Yes, R is enough to get into analytics-heavy roles.

  • Python basics unlock ML/AI-heavy roles (tech firms, ad-tech, recommendation systems).

  • Employers don’t hire tools—they hire problem solvers who can generate insights from data.

4.3 What is Positron?

Positron is a free, next-generation data science IDE from Posit (formerly RStudio) that supports both Python and R natively—it’s designed for polyglot workflows. It’s built on the open-source Code OSS (the foundation of VS Code) and brings a modern, extensible environment tailored for data work (isabel.quarto.pub+12Posit+12jumpingrivers.com+12.)

Key features include:

  • Variable & Data Frame Explorer: Explore, filter, sort, and summarize your data interactively (Posit;Posit+2heise online+2).

  • Multi‑Session Console: Run R and Python code in parallel, each in separate consoles, without modifying your source files (positron.posit.co+9Posit+9heise online+9.)

  • Interpreter & Environment Management: Easily switch between different R and Python environments (Posit+10Posit+10drmowinckels.io+10)

  • Polished UI & AI Assistance: Includes a modern editor with support for VSIX extensions and the Positron Assistant for contextual AI-based help (drmowinckels.io+5Posit+5isabel.quarto.pub+5)

  • Database Connection Pane: Built-in support for browsing and querying SQL data sources directly within the IDE (Posit)

  • Integrated Data App Workflow: Launch and debug Shiny, Streamlit, Dash, or FastAPI apps with a single click (Posit+1)

4.4 How Does It Compare to RStudio for Data Warehouse Integration?

RStudio remains a highly stable and familiar environment for R work, especially in statistical modeling and reproducible reporting with R Markdown or Quarto (Posit+14positron.posit.co+14Wikipedia+14). However, when it comes to interfacing with data warehouses—typically SQL-heavy work—Positron offers distinct advantages:

4.4.1 Advantages of Positron:

  1. Built-In SQL Integration: The Database Connection Pane lets users connect to and query SQL data sources right inside the IDE, making data-access streamlined.

  2. Polyglot Workflow: For teams or students who may use both R and Python for ETL, modeling, or automation, Positron lets them do so in one session—no context-switching needed.

  3. Modern, Customizable UI: Being based on VS Code, Positron supports a wide ecosystem of extensions, customizable layout, and flexible workflows (denniseirorere.com+14drmowinckels.io+14Occasional Divergences+14Links to an external site.Occasional Divergences+1.)

4.4.2 Things to Keep in Mind:

  • Beta Maturity: Positron is still relatively new and under active development. Some features familiar from RStudio—like inline output in Quarto documents, workspace autosave on restart, history pane, and RStudio Add-ins—are not yet fully implemented (positron.posit.co+2Wikipedia+2)

  • Learning Curve: Switching from RStudio may take some onboarding time, especially for users accustomed to its tightly integrated interface (jumpingrivers.com)

  • Foundation: RStudio continues to be maintained with a focus on stability, especially for R-heavy workflows. Positron is additive, not a replacement (Posit;positron.posit.co)

4.5 Judgment Call: Is Positron Better for Data Warehouse Workflows?

Yes—especially for workflows involving SQL or dual-language environments. Here’s why:

  • The built-in SQL pane and query tools make accessing warehouse data naturally part of the coding workflow.

  • Students can seamlessly move between R and Python, which many real-world jobs require.

  • The VS Code-based engine makes Positron extensible, customizable, and future-facing.

That said, if the student is focused primarily on R and relies heavily on RMarkdown, Addins, or a very streamlined R-focused workflow, RStudio may still feel more polished.

4.6 Recommendation for Marketing Data Science Tools

  • For SQL-heavy, polyglot, or app-deployment workflows: Encourage them to experiment with Positron. Its features align well with modern data engineering and analytics workflows.

  • For R-focused, academic, or reproducibility-heavy tasks: RStudio remains excellent and highly reliable—especially for teaching foundations in R.

4.6.1 What to install (minimum viable setup)

  1. R in VS Code

  • Extension: “R” (REditorSupport) — consoles, data viewer, plots pane, workspace browser, debugging, Rmd support. (Visual Studio Marketplace;Visual Studio Code;GitHub.)

  • Helpful bits:

    • languageserver for IDE features (autocomplete, linting). (jozef.io)

    • httpgd for a great plot viewer (enable “R: Plot: Use httpgd”). Note: currently installed from GitHub. (Stack Overflow)

  1. Quarto
  1. Python

4.6.2 Connecting to data warehouses (three good paths)

A. Pure SQL inside VS Code

B. R + DBI/odbc from your code

C. Python from your code

4.6.3 VS Code vs. Positron vs. RStudio (for your use case)

  • VS Code: Most mature polyglot environment today; deep SQL tooling; constant releases; huge extension ecosystem (including Quarto). Great when students mix R, Python, and warehouse SQL. Microsoft for DevelopersLinks to an external site.

  • Positron: Promising R+Python IDE from Posit with data/variable explorer and SQL pane, but still maturing; some RStudio niceties aren’t fully there yet. If you need “ready today” for warehousing, VS Code wins on stability and breadth. (Context: Positron pages highlight active development and gaps vs RStudio.) QuartoLinks to an external site.

  • RStudio: Still superb for R-first teaching (R Markdown/Quarto, tidyverse, Shiny) but not as strong for multi-language + warehouse dev as VS Code’s ecosystem.

4.6.4 Quick-start checklist for your class

4.6.5 Bottom line

If Positron feels a bit early for you right now, VS Code is the best “bridge”: rock-solid for R + Quarto + Python and excellent for data-warehouse workflows via SQLTools/warehouse extensions or via DBI/odbc inside your code. It’s a great environment to standardize on for your cohort this year.

5 Marketing Analysts vs. Marketing Data Scientists: Comparisons

5.1 Career Preparations for Marketing Analytics Career

Two major job levels in marketing analytics are Marketing Analysts and Marketing Data Scientists. 

5.1.1 Marketing Analyst

5.1.1.1 Focus:

  • Descriptive & diagnostic insights (what happened, why it happened).

5.1.1.2 Typical Tasks:

    • Pull and clean marketing data from multiple sources (Google Analytics, CRM, ad platforms).
    • Build dashboards and reports (Tableau, Power BI, Looker).
    • Conduct campaign performance analysis (CTR, ROI, ROAS, CAC, LTV).
    • Run A/B tests and interpret results.
    • Provide actionable recommendations for channel optimization.

5.1.1.3 Core Skills:

    • Data literacy: SQL, Excel, visualization tools.

    • Statistics: Descriptive stats, correlation, significance testing.

    • Marketing knowledge: Channel metrics, attribution basics, customer segmentation.

    • Communication: Translate data into insights for marketing managers.

5.1.1.4 Career Path:

  • Often moves into Marketing Manager, Growth Marketing, or Insights Lead roles.

5.1.2 Marketing Data Scientist

5.1.2.1 Focus:

  • Predictive & prescriptive modeling (what will happen, what should we do).

5.1.2.2 Typical Tasks:

    • Develop and validate predictive models (churn prediction, customer lifetime value).

    • Build marketing mix models & attribution frameworks.

    • Apply machine learning (clustering for customer segments, NLP for social media sentiment).

    • Run advanced experiments (multivariate tests, causal inference).

    • Work with large-scale data from warehouses and cloud platforms (BigQuery, Snowflake).

5.1.2.3 Core Skills:

    • Programming: R or Python for data science (pandas, scikit-learn, tidyverse, caret).

    • Statistics & ML: Regression, classification, clustering, Bayesian methods, deep learning (basic exposure).

    • Data engineering: Handling large data (SQL optimization, Spark, APIs).

    • Business acumen: Align modeling with marketing strategy and ROI impact.

5.1.2.4 Career Path:

  • Moves into Senior Data Scientist, Marketing Analytics Lead, or even Head of Data Science/AI for Marketing.

5.1.3 Key Differences (Comparison & Contrast)

Dimension Marketing Analyst 📝 Marketing Data Scientist 🔬
Analytical depth Descriptive & diagnostic Predictive & prescriptive
Tools Excel, SQL, Tableau R/Python, ML libraries, SQL, cloud tools
Statistics Basic stats & A/B testing Advanced stats, ML, causal inference
Data scope Reports & structured data Large-scale, unstructured, complex datasets
Output Dashboards, insights, campaign reports Predictive models, simulations, optimization
Audience Marketing managers, campaign teams Senior leadership, product & data teams

5.1.4 Summary / Conclusion:

  • A Marketing Analyst is a storyteller of past and present data: they monitor performance, explain outcomes, and support tactical decisions.

  • A Marketing Data Scientist is a predictor and optimizer: they forecast trends, build models, and shape strategic decisions with advanced analytics.

  • Analysts need solid marketing + applied analytics skills; Data Scientists require strong technical depth in programming, statistics, and machine learning in addition to marketing knowledge.

5.2 Career Readiness Skills: Marketing Analyst vs. Marketing Data Scientist

5.2.1 Data & Technical Skills

Skill Area Marketing Analyst 📝 Marketing Data Scientist 🔬
Excel / Google Sheets Strong (pivot tables, formulas, charts) Strong (but less central — used for quick checks)
SQL Querying, joins, aggregations Advanced SQL (optimization, CTEs, data pipelines)
Data Visualization Tableau, Power BI, Looker Tableau/Power BI + programmatic viz (ggplot2, matplotlib, seaborn)
Programming Optional (basic R or Python helpful) Essential (R or Python: pandas, scikit-learn, tidyverse, caret, TensorFlow basics)
Cloud/Data warehouses Basic familiarity (GA4, HubSpot, Salesforce) Strong (BigQuery, Snowflake, AWS, GCP, APIs, Spark)

5.2.2 Statistics & Analytics Methods

Skill Area Marketing Analyst 📝 Marketing Data Scientist 🔬
Descriptive statistics Means, distributions, variance Core foundation (but applied to complex models)
A/B testing Design and interpret simple tests Advanced experiment design (multivariate, causal inference, uplift modeling)
Regression Linear, logistic (basic interpretation) Advanced regression, regularization (LASSO, Ridge), hierarchical models
Segmentation RFM analysis, demographics Clustering (k-means, hierarchical, DBSCAN)
Attribution Basic models (first/last click, linear) Algorithmic attribution, Shapley values, MMM (Marketing Mix Modeling)
Predictive modeling Rarely expected Core: churn prediction, CLV modeling, demand forecasting
Machine Learning Not required Expected: supervised & unsupervised ML, basics of NLP for social/media data

5.2.3 Marketing Knowledge & Business Skills

Skill Area Marketing Analyst 📝 Marketing Data Scientist 🔬
Digital marketing metrics Essential (CTR, CAC, ROAS, LTV) Essential, with ability to model relationships
Campaign analysis Core responsibility Supports via predictive optimization
Customer journey mapping Familiarity Advanced: simulate and optimize journeys
Storytelling with data Must be strong (dashboards, executive reports) Must be strong (translating ML models to decisions)
Business acumen Tactical campaign support Strategic forecasting, scenario planning

5.2.4 Readiness Levels (Quick Checklist for Students)

5.2.4.1 Marketing Analyst Readiness

  • Excel (pivot tables, advanced formulas)

  • SQL (basic querying, joins)

  • Tableau/Power BI (dashboards for campaign KPIs)

  • A/B test interpretation

  • Basic regression & correlation

  • Strong grasp of marketing metrics (CAC, LTV, ROAS, CTR)

  • Communication & storytelling with data

5.2.4.2 Marketing Data Scientist Readiness

  • R or Python (pandas, scikit-learn, tidyverse)

  • Advanced SQL & cloud data handling (BigQuery, Snowflake)

  • Predictive modeling (CLV, churn, forecasting)

  • Machine learning (classification, clustering, regression, NLP basics)

  • Advanced experiment design & causal inference

  • Marketing Mix Modeling & advanced attribution

  • Translate technical models into business decisions

5.2.5 Summary:

  • Marketing Analysts: More accessible entry path for students with solid business + intermediate analytics skills. Think “data-informed marketer.”

  • Marketing Data Scientists: Require deeper technical investment in programming, ML, and statistics. Think “data science applied to marketing.”

5.3 Career Ladder: Marketing Analytics → Marketing Data Science

5.3.1 Marketing Analyst (Entry-Level / Early Career)

5.3.1.1 Focus:

  • Reporting, campaign insights, dashboarding.

5.3.1.2 Core Skills:

  • Excel, SQL (basic queries)

  • Tableau/Power BI dashboards

  • A/B test setup & interpretation

  • Marketing metrics (CTR, ROAS, CAC, LTV)

  • Strong communication (turning data → story)

5.3.2 Senior Marketing Analyst / Marketing Analytics Specialist

5.3.2.1 Focus:

  • Deeper analysis, some modeling, mentoring junior analysts.

5.3.2.2 Extra Skills to Develop:

  • Intermediate SQL (joins, CTEs, optimization)

  • Regression analysis (linear, logistic)

  • Attribution modeling basics

  • Data storytelling for executives

  • Project management & cross-functional teamwork

5.3.3 Marketing Data Scientist (Mid-Level)

5.3.3.1 Focus:

  • Predictive & prescriptive analytics, model building.

5.3.3.2 Extra Skills to Develop:

  • R or Python (tidyverse, pandas, scikit-learn)

  • Machine learning (classification, clustering, NLP basics)

  • Predictive modeling (CLV, churn, forecasting)

  • Marketing Mix Modeling (MMM) & algorithmic attribution

  • Experimental design beyond A/B (causal inference, uplift models)

  • Data pipeline work with cloud warehouses (BigQuery, Snowflake, AWS/GCP)

5.3.4 Senior Data Scientist / Analytics Lead

5.3.4.1 Focus:

  • Advanced modeling, strategy influence, leadership.

5.3.4.2 Extra Skills to Develop:

  • Advanced ML (ensemble models, Bayesian methods, deep learning exposure)

  • Scalable data solutions (Spark, ML pipelines)

  • Model deployment / MLOps basics

  • Leading analytics projects & mentoring junior scientists

  • Translating data science → marketing strategy at senior level

5.3.5 Head of Marketing Analytics / Director of Data Science (Leadership Track)

5.3.5.1 Focus:

  • Strategy, vision, and business impact at scale.

5.3.5.2 Extra Skills to Develop:

  • People leadership & team building

  • Budgeting and resource allocation

  • Data governance & ethics in AI/marketing

  • Communicating with C-suite & non-technical stakeholders

  • Driving innovation (AI personalization, advanced attribution, causal ML)

5.3.6 Summary of Ladder:

  • Analyst → Senior Analyst = solidify reporting & applied stats.

  • Senior Analyst → Data Scientist = add programming, ML, and predictive analytics.

  • Data Scientist → Senior/Lead = move toward scalable ML & team leadership.

  • Lead → Director/Head = shift from technical depth → strategic impact.

6 Career Readiness Resources

6.1 Career Center

  • Virtual Career Center
  • Focus 2 Career: for assessments as well as career and industry exploration
  • Handshake: professional profile, job search, and access to career events
  • VMock: AI-powered resume review support, including optimizing features to tailor to different job postings
  • Big Interview: AI-guided practice for both general and industry-specific interview questions

6.2 Micro-Internship

6.3 Networking

  • MSDM Student Club