Business Metrics & KPIs
Understand what to measure and why. Tests metric definition, root-cause analysis, and building metrics frameworks that drive decisions.
Dashboard Design & BI Tools
Design interactive, self-service analytics interfaces. Tests information hierarchy, filter design, and real-time monitoring.
Data Cleaning & Insights
Clean a messy dataset and turn it into insights a manager can act on. Tests data handling, analytical thinking, and business communication.
Data Governance & Quality
Ensure data is trustworthy, compliant, and well-documented. Tests data dictionaries, quality checks, and privacy compliance.
Data Storytelling & Presentation
Turn data into narratives that move stakeholders to act. Tests executive communication, slide design, and handling conflicting data.
Data Visualisation
Choose the right chart for a specific audience and cadence, and defend the choice. Tests metric selection, visual design, and executive communication.
ETL & Data Pipelines
Design reliable data flows from source to warehouse. Tests pipeline design, schema evolution, and real-time streaming architecture.
Machine Learning Fundamentals
Apply practical, applied machine learning thinking. Tests algorithm selection, model evaluation, and bias detection.
Python for Data Analysis
Use pandas, numpy, and the Python data ecosystem for analysis and automation. Tests data wrangling, merging, and report generation.
Excel / Spreadsheet Modelling
Build robust models in the most-used analytics tool in business. Tests pivot tables, what-if analysis, and simulation techniques.
Why Build a Data Analytics Portfolio?
Data analysts succeed by transforming raw, messy data into actionable business decisions. A portfolio featuring SQL query libraries, cleaned datasets, and Power BI dashboards demonstrates that you can answer real business questions.
What Hiring Managers Look For
Writing multi-table JOINs, subqueries, and window functions to extract metrics accurately.
Handling null values, duplicate records, outliers, and datatypes before running analysis.
Presenting dashboard findings with clear executive summaries and decision recommendations.
Key Skills Tested
- SQL Querying (JOINs, GROUP BY, CTEs, Window Functions)
- Microsoft Excel (VLOOKUP, Pivot Tables, Macros)
- Power BI & Tableau Dashboard Design
- Python Data Manipulation (pandas, NumPy)
- Statistical Analysis & Trend Identification
Frequently Asked Questions (Data Analytics)
What is the most important skill to showcase in a data analyst portfolio?
SQL querying capability combined with clear data visualization. Hiring managers want to see how you pull and present data to answer specific commercial questions.
Should I include Python or Excel in my data portfolio?
Both. Excel and SQL handle the vast majority of entry-level reporting, while Python demonstrates advanced data cleaning and automation skills.
Where can I find public datasets for these tasks?
Each task brief provides structured datasets or links to open-data sources like Kaggle and government data portals.
How long should a data analytics portfolio case study be?
Focus on quality over length: a clear business problem statement, 3 key visualizations, SQL query snippets, and a 3-bullet executive recommendation.
Want to prepare for job applications in Data Analytics?
Follow our step-by-step career roadmap for skills, salary benchmarks, and interview preparation.