Data Analyst Portfolio
Build three portfolio-ready analysis projects: a guided e-commerce sales dashboard, an assisted marketing A/B test analysis, and an independent industry dataset deep-dive.
Why learn Data Analyst Portfolio?
Data analysis skills only prove themselves in a finished analysis someone else can read. Data Analyst Portfolio is three portfolio-ready projects across three levels of independence: a guided e-commerce sales dashboard built step by step, an assisted marketing A/B test analysis built from requirements and milestones, and a fully independent industry dataset deep-dive you design yourself.
It is 3 modules, one project each: Guided Projects builds an e-commerce sales dashboard with full guidance, Assisted Projects builds a marketing campaign A/B test analysis with requirements and checkpoints but not a walkthrough, and Solo Build Projects is an independent industry dataset analysis, your portfolio capstone.
Each project draws on the earlier data tracks, cleaning, visualization, business metrics, and applied statistics, applied to a project you can put in front of a hiring manager.
Who this course is for
- Learners who finished Data Cleaning & EDA, Data Visualization & Business Metrics, and Applied Statistics and want to apply them
- Anyone who needs concrete, presentable data analysis projects for a portfolio or job search
- Aspiring analysts who learn best by producing something real rather than more isolated exercises
- Developers ready to scope and deliver an independent analysis in the solo module
What you'll build and practice
- A guided e-commerce sales dashboard, built step by step with full implementation guidance
- A marketing campaign A/B test analysis, built from requirements and milestone checkpoints
- An independent, self-directed industry dataset analysis as your portfolio capstone
- Practice presenting an analysis clearly, not just producing the numbers behind it
What you'll learn
Data Analyst Portfolio is organized into 3 focused modules. By the end you'll be comfortable with:
- Guided Projects
- Assisted Projects
- Solo Build Projects
Course curriculum
3 lessons across 3 modules. Lessons marked Free preview are readable without an account.
Module 1. Guided Projects
Build a complete data analysis project step-by-step with full guidance
Module 2. Assisted Projects
Build a data analysis project with requirements, hints, and milestone checkpoints
Module 3. Solo Build Projects
Design and deliver an independent data analysis — your portfolio capstone
- 120mSolo Project: Industry Dataset Analysis
Choose a real dataset and deliver a complete analysis report with EDA, business metrics, and insights.
Frequently asked questions
- What courses should I finish before starting Data Analyst Portfolio?
- Data Cleaning & EDA, Data Visualization & Business Metrics, and Applied Statistics. This track applies the skills from all three to real projects rather than teaching new analytical concepts of its own.
- What is the difference between the guided, assisted, and solo projects here?
- The guided e-commerce dashboard walks you through every step. The assisted A/B test analysis gives you requirements and milestones but expects you to fill in the approach. The solo industry dataset analysis gives you only a brief and expects you to scope, clean, analyze, and present it independently.
- Do I choose my own dataset for the solo project, or is one assigned?
- The solo module points you toward an industry dataset deep-dive and expects you to define the scope of the analysis yourself, which is closer to how a real analyst assignment actually starts than a fully pre-defined brief would be.
- Are these projects meant for a portfolio, or just for practice?
- For a portfolio. All three are described as portfolio-ready projects in the course itself: a dashboard, an experiment analysis, and an independent deep-dive, each meant to be something you can show and explain, not a disposable exercise.
- How is this track different from AI Projects Portfolio?
- AI Projects Portfolio builds AI applications like chatbots and RAG systems using API and prompting skills. This track builds data analysis projects, dashboards, experiment analyses, and dataset deep-dives, using pandas, visualization, and statistics skills instead. They do not overlap.
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