Data Analysis
The ability to collect, clean, interpret, and communicate insights from data — one of the most transferable skills across modern business roles.
Data analysis spans everything from pulling and cleaning data in Excel or SQL, running descriptive statistics, building dashboards in Tableau or Power BI, and presenting findings to decision-makers. It's now expected at most analyst, operations, marketing, product, and finance roles — and increasingly at sales and customer success. The specific tools vary by role; the underlying skill of turning raw data into actionable conclusions is universal.
Typical time to job-readiness: ~2 months.
Learning Data Analysis
Beginner
Learn to pull data with SQL or Excel, clean it, and present findings in a clear chart. Start with the question-first mindset: decide what you need to know before touching the data.
Intermediate
Build dashboards in Tableau or Power BI, run cohort analyses, interpret A/B test results, and communicate uncertainty (confidence intervals, sample size). Understand correlation vs causation.
Advanced
Statistical modeling, experiment design, and building self-serve analytics infrastructure. Senior analysts are assessed on business impact of their analyses, not technical tool fluency.
Key concepts
- Start with the question — define what you're trying to learn before touching the data
- Data cleaning: handle nulls, duplicates, and outliers before drawing any conclusions
- Descriptive statistics: mean, median, mode, standard deviation — understand your data's shape first
- Correlation vs causation — two things moving together doesn't mean one causes the other
- Segmentation — breaking data into groups often reveals insights hidden in the aggregate
- Visualization: the right chart for the right data — bar for comparison, line for trend, scatter for correlation
Common interview topics
- Walk me through how you would analyze a sudden drop in a key metric
- What is the difference between correlation and causation — give an example
- How do you validate the data before making conclusions from it
- How would you explain a complex data finding to a non-technical stakeholder
- What tools do you use for data analysis and why