Thread by Avi Kumar Talaviya

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Think you can't learn data science on your own? Think again!

Here are the top free resources to master the art of data science📊💯 (Don't miss this thread)

A Thread👇
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: 🔗 www.w3schools.com/python/default.asp
2. Math & statistics

⁕ Topics to learn: linear algebra, stats, calculus, probability

⁕ Time allocation: 30 Days

⁕ Learning outcome: get your hands on key math concepts to learn data science and ML

⁕ FREE Learning resources:
www.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
www.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: www.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:
www.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:
www.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:
www.coursera.org/learn/introduction-git-github?irclickid=VLIXqcQX4xyIUq2WaWTSN2NBUkDwX8T0LSstxM0&irgw...
A new workshop on "Intro to Data analysis workflows & Method Chaining" has been launched by @DataKwery

Interesting part? It's FREE

Click the link below to sign up👇
us02web.zoom.us/webinar/register/7516794479303/WN_LVQLV4qVRluy1AZIHSNddA
End of this thread!👍

If you've found it informative then do like, RT/QT first tweet, and comment what you think on this💬

And Don't forget to follow me at @avikumart_ and @DataKwery for more updates🔥👍

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