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Table of contents

1. Introduction to building a Data portfolio

Why a data portfolio is essential:

A portfolio demonstrates your problem-solving capabilities, technical skills, and thought process.

It shows employers how you handle real data issues—beyond what a resume can convey.

What are the components of a great portfolio:

• Projects: Highlight end-to-end projects—from data sourcing and cleaning to model deployment—that solve real-world problems.
• Storytelling: Present a clear narrative. Frame the business problem, outline the approach, and discuss insights and outcomes.
• Visual Design & Documentation: Clean, easy-to-navigate websites, dashboards, and visualizations make your work accessible. Detailed documentation (READMEs, code comments, and presentations) shows professionalism and thoroughness.

2. Choosing the right projects for each Data role

For Data Analysts:

• Key Skills to Showcase:
  • Proficiency in SQL: Expertise in writing efficient queries to extract, manipulate, and blend data from large datasets.
  • Data Cleaning & EDA: Strong hands-on experience with Python (using pandas, numpy) or SQL to clean messy datasets, identify trends, and uncover insights during exploratory data analysis.
  • Data Visualization: Ability to create impactful visualizations using tools like Tableau, Power BI, or libraries like matplotlib and seaborn to effectively communicate findings to both technical and non-technical stakeholders.
  • Problem-Solving & Business Acumen: A knack for framing business questions, diving deep into the data, and deriving actionable insights that drive decision-making.