A complete data science roadmap

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A complete data science roadmap for beginners🤖🚀 ▶ Roadmap, tech-stack, learning resources, & projects to build a portfolio along with Timeline📅⏲ A thread🧵↓

📅 Step-by-step guide form start to finish data science until applying for a job📝 A Roadmap to follow:🛣↓ 1. Python 2. Math & statistics 3. SQL/No-SQL 4. Pandas, Matplotlib, Numpy 5. Excel & Tableau 6. Machine learning 7. Deep learning 8. Deployment-AWS/GCP 9. Git/GitHub

1. Python ⁕ Level of skills: Basics to intermediate ⁕ Time allocation: 21-25 Days ⁕ Project Idea: Count down calculator ⁕ Learning outcome: Build python programming skills ⁕ FREE Learning resources: 🔗 w3schools.com/python/default.asp

2. Math & statistics ⁕ Topics to learn: linear algebra, stats, optimization, probability ⁕ Time allocation: 30 Days ⁕ Learning outcome: get your hands on key math concepts to learn data science and ML ⁕ FREE Learning resources: khanacademy.org/math

3. SQL/No-SQL ⁕ Tools to learn: SQL, MySQL, MongoDB ⁕ Time allocation: 20 Days ⁕ Project Ideas: Extract and analyze Employee data using SQL&MySQL ⁕ Learning outcome: Get your hands on RDBMS ⁕ FREE Learning resources: 🔗 sqlbolt.com/

4. Pandas, Matplotlib, Numpy ⁕ Level of skills: UPTO intermediate ⁕ Time allocation: 30-45 Days ⁕ Project Ideas: Google play store data analysis using pandas, NumPy, and matplotlib ⁕ Learning outcome: Learn Key DS libraries

⁕ FREE Learning resources: 🔗 w3schools.com/python/matplotlib_intro.asp

5. Excel/Tableau ⁕ Tools to learn: Excel, power query, tableau, ⁕ Time allocation: 20 Days ⁕ Project Ideas: HR analytics dashboard ⁕ Learning outcome: Get your hands on Excel and tableau ⁕ FREE Learning resources: 🔗 youtube.com/c/ExcelTutorials/videos

6. Machine learning ⁕ Alogorithms: Regression, classification, clustering ⁕ Time allocation: 30-35 Days ⁕ Project Ideas: Players auction price prediction ⁕ Learning outcome: Build regression and classification models ⁕ Learning resources: 🔗 developers.google.com/machine-learning/crash-course/ml-intro

7. Deep learning ⁕ Alogorithms: CNN, RNN, LSTM ⁕ Time allocation: 40-50 Days ⁕ Project Ideas: digit recognizer, images classifier ⁕ Learning outcome: Build image classification models ⁕ FREE Learning resources: 🔗 freecodecamp.org/learn/machine-learning-with-python/

8. Deployment ⁕ Tools: Heroku, streamlit and AWS ⁕ Time allocation: 30 Days ⁕ Project Idea: deploy credit card default model on Heroku ⁕ Learning outcome: get hands-on deployment of ML projects ⁕ Learning resources: 🔗 coursera.org/learn/mlops-fundamentals

9. Git/GitHub ⁕ Skills: Get familiar with Github/ learn to use GIT commands ⁕ Time allocation: 10 Days ⁕ Learning outcome: learn to work with GitHub and work in group projects ⁕ Learning resources: 🔗 coursera.org/learn/introduction-git-github?irclickid=VLIXqcQX4xyIUq2WaWTSN2NBUkDwX8T0LSstxM0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=1193708&utm_content=b2c

Now to summerise everything you need to become a data scientist at ONE PLACE🤯 One of the best notebooks of everything you need to learn🔽 Have DATAI's notebooks as your go-to reference for anything data science ...and thank me later kaggle.com/kanncaa1/code

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