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BECOMING A DATA ANALYST IN 2025
Becoming a data analyst doesn’t have to be expensive in 2025.
With the right free resources and a structured approach,
you can become a skilled data analyst.
Here’s a roadmap with free resources to guide your journey:
1️⃣ Learn the Basics of Data Analytics
Start with foundational concepts like:
↳ What is data analytics?
↳ Types of analytics (descriptive, predictive, prescriptive).
↳ Basics of data types and statistics.
📘 Free Resources:
1. Intro to Statistics : https://www.khanacademy.org/math/statistics-probability
2. Introduction to Data Analytics by IBM (audit for free) :
https://www.coursera.org/learn/introduction-to-data-analytics
2️⃣ Master Excel for Data Analysis
Excel is an essential tool for data cleaning, analysis, and visualization.
📘 Free Resources:
1. Excel Is Fun (YouTube): https://www.youtube.com/user/ExcelIsFun
2. Chandoo.org: https://chandoo.org/
🎯 Practice: Learn how to create pivot tables and use functions like VLOOKUP, SUMIF, and IF.
3️⃣ Learn SQL for Data Queries
SQL is the language of data—used to retrieve and manipulate datasets.
📘 Free Resources:
1. W3Schools SQL Tutorial : https://www.w3schools.com/sql/
2. Mode Analytics SQL Tutorial : https://mode.com/sql-tutorial/
🎯 Practice: Write SELECT, WHERE, and JOIN queries on free datasets.
4️⃣ Get Hands-On with Data Visualization
Learn to communicate insights visually with tools like Tableau or Power BI.
📘 Free Resources:
1. Tableau Public: https://www.tableau.com/learn/training
2. Power BI Community Blog: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/bg-p/community_blog
🎯 Practice: Create dashboards to tell stories using real datasets.
5️⃣ Dive into Python or R for Analytics
Coding isn’t mandatory, but Python or R can open up advanced analytics.
📘 Free Resources:
1. Google’s Python Course https://developers.google.com/edu/python
2. R for Data Science (free book) r4ds.had.co.nz
🎯 Practice: Use libraries like Pandas (Python) or dplyr (R) to clean and analyze data.
6️⃣ Work on Real Projects
Apply your skills to real-world datasets to build your portfolio.
📘 Free Resources:
Kaggle: Datasets and beginner-friendly competitions.
Google Dataset Search: Access datasets on any topic.
🎯 Project Ideas:
Analyze sales data and create a dashboard.
Predict customer churn using a public dataset.
7️⃣ Build Your Portfolio and Network
Showcase your projects and connect with others in the field.
📘 Tips:
→ Use GitHub to share your work.
→ Create LinkedIn posts about your learning journey.
→ Join forums like r/DataScience on Reddit or LinkedIn groups.
Final Thoughts
Becoming a data analyst isn’t about rushing—it’s about consistent learning and practice.
💡 Start small, use free resources, and keep building.
💡 Remember: Every small step adds up to big progress.
BY Data science/ML/AI
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