🧭 Finally, with this roadmap, I understood where to start learning data science!

👨🏻‍💻 This map is designed like a metro map that starts from basic concepts and goes through machine learning, big data, and data visualization.

✏️ In this roadmap, it shows step by step where to start, what to learn, and how to become a professional data scientist.

1⃣ Fundamentals: First of all, you need to strengthen your foundation; that means math, probability, distributions, and linear algebra. These are the backbone of any data analysis.

2⃣ Programming and Statistics: Learn to work with tools like Python, R, and Excel, and enter the world of statistics: analysis of variance (ANOVA), confidence intervals, and regression are the main basics.

3⃣ Machine Learning: Move on to classification, clustering, prediction, and learn how to train and test machine learning models.

5⃣ Big Data and NLP: If you deal with large datasets, work with tools like Hadoop and Hive. In natural language processing, learn techniques like sentiment analysis, tagging, and text analysis.

5⃣ Visualization and Data Engineering: Learn how to turn data into understandable and engaging stories using tools like Tableau, D3.js, and ggplot2. Also, get familiar with concepts like pipelines, cleaning, and data preparation.

6⃣ Optional Paths: Depending on your career path, some parts may not be necessary. For example, if you want to specialize in machine learning, you don’t need to go very deep into big data or text mining.

📹 Recommended YouTube Channels:

🎞 Channel Krish Naik

🎞 Channel Ken Jee

🎞 Channel StatQuest

🎞 Channel codebasics

🎞 Channel Emma Ding

🔻 Certificates and Learning Resources:

📄 SQL Fundamentals

📄 IBM Data Science Professional

📄 Khan Academy

🏐 Start with simpler topics, keep going consistently, and most importantly, build real projects while learning. Because this way of learning helps increase your confidence, and high confidence turns into real job opportunities.

✅ https://t.me/CodeProgrammer ✅