Data Visualization & Business Metrics
Create compelling charts with matplotlib and seaborn, then learn the business metrics every analyst must know: revenue, conversion rate, retention, churn, CAC, and LTV.
Why learn Data Visualization & Business Metrics?
A chart that misleads is worse than no chart at all, and knowing the metrics behind it matters as much as knowing how to plot it. Data Visualization & Business Metrics teaches you to build clear, accurate charts with matplotlib and seaborn, then teaches the business metrics every analyst is expected to know: revenue and growth, conversion rate and funnels, retention and churn, and CAC and LTV.
It is 2 modules and 9 lessons: data visualization, covering choosing the right chart, matplotlib in practice, seaborn for data analysis, and dashboard design, and business metrics, covering revenue and growth metrics, conversion rate and funnels, user retention and churn, CAC and LTV, and building a metrics dashboard.
The course ends by building a metrics dashboard, combining both halves into one deliverable rather than treating charts and business numbers as separate skills.
Who this course is for
- Aspiring data analysts who can make a chart but do not know which chart to choose
- Anyone who has heard terms like churn, CAC, and LTV in a meeting and nodded along
- Python for AI or Data Cleaning & EDA graduates ready for the presentation layer
- Developers preparing for the Data Analyst Portfolio track
What you'll build and practice
- Charts chosen deliberately for what they need to communicate, not by habit
- Practical matplotlib and seaborn charts built on real data
- An understanding of revenue, growth, conversion, retention, churn, CAC, and LTV you can explain in plain language
- A metrics dashboard that combines visualization and business metrics into one view
What you'll learn
Data Visualization & Business Metrics is organized into 2 focused modules. By the end you'll be comfortable with:
- Data Visualization
- Business Metrics
Course curriculum
9 lessons across 2 modules. Lessons marked Free preview are readable without an account.
Module 1. Data Visualization
Build clear, accurate charts that communicate insights immediately
- 10mChoosing the Right ChartFree preview
Match your data question to the right chart type — and avoid the most common visualization mistakes.
- 12mmatplotlib in PracticeFree preview
Master the matplotlib object-oriented API, subplots, styling, and saving figures.
- 12mseaborn for Data Analysis
Use seaborn's high-level plots — histplot, boxplot, heatmap — to visualize distributions and relationships quickly.
- 10mDashboard Design
Design dashboards that communicate insights in 5 seconds — layout, hierarchy, and what to avoid.
Module 2. Business Metrics
Measure and communicate revenue, retention, churn, CAC, and LTV like a professional analyst
- 12mRevenue & Growth Metrics
Calculate MoM and YoY growth, run rate, and revenue per user — the numbers every analyst reports first.
- 12mConversion Rate & Funnels
Calculate stage-by-stage conversion rates and visualize where users drop off in a funnel.
- 12mUser Retention & Churn
Calculate retention rate, churn rate, and build cohort tables — the metrics that show whether users are coming back.
- 12mCAC and LTV
Calculate customer acquisition cost and lifetime value — the two metrics that determine whether a business is economically viable.
- 14mBuilding a Metrics Dashboard
Combine SQL queries, pandas, and matplotlib into a single script that outputs all key KPIs.
Frequently asked questions
- Do I need a business background to understand the metrics half of this course?
- No. The Business Metrics module explains each concept, revenue, conversion, retention, churn, CAC, and LTV, from first principles with plain-language definitions and worked examples, not assumed business-school vocabulary.
- Does the course teach matplotlib, seaborn, or both?
- Both, as separate lessons inside the Data Visualization module: dedicated practice with matplotlib first, then seaborn for the statistical chart types matplotlib makes more tedious.
- What does choosing the right chart actually mean as a skill?
- It is the first lesson in the course for a reason: matching the shape of your data and your question to the chart type that actually communicates it, rather than defaulting to a bar chart or a line chart out of habit.
- Is CAC and LTV covered with real formulas, or just definitions?
- With real formulas and worked calculations. The CAC and LTV lesson walks through computing both from underlying numbers, not just defining the acronyms, so you can compute them on a dataset of your own afterward.
- How does this course fit with Applied Statistics?
- They are companion tracks at the same level. This course focuses on communicating data through charts and standard business metrics. Applied Statistics focuses on the underlying statistical reasoning, like distributions and A/B testing, that often sits behind those same numbers.
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