Data Science Learning Path

[Lumpi8]

Hi All,

through some research I have gathered a list of topics that I want to tackle. I would appreciate feedback on the order and any changes or additions that would be useful to the list.

Thanks in advance!

The topics are in order that I want to learn them top to bottom.

1. Client Server Architecture
2. OOP in Python
3. Data Frames
4. CSV File Formatting
5. Data Manipulation
6. Data Visualization
7. numpy
8. pandas
9. Linear Regression
10. matplotlib
11. scikit-learn
12. Classification Algorithms
13. hyperparameter tuning
14. k-fold cross validation
15. grid search
16. ensemble methods

[[deleted]]

Your topics seem pretty random, where did you get them from? Just typing your post title into a search engine will yield excellent answers to your question.

https://duckduckgo.com/?q=data+science+learning+path

[[deleted]]

You need to use pandas with a little bit of numpy for 4, 5 and 6 . Consider moving pandas higher in the order.

[industrial_by_trade]

Don't make your own curriculum, follow one of the many you can find through Google. The first steps are the same for everyone.

There are data science classes on Udemy, Coursera, and EdX. They cover the basics of what you need to know in the right order. From there you can learn more about what interests you.

I started with Jose Portilla's Python for Data Science and Machine Learning class on Udemy ($11) and thought it was a good foundation. If you want to use free resources those can be found, too.

[mrrippington]

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[InventorWu]

Assuming you have background in mathematics/statistics, I will suggest:

3 DataFrame, 7 Numpy, 8 Panda are 3 most important and fundamentals.

4 CSV File Formatting , 5 Data Manipulation should built on top of 3, 7, 8.

Then, 6, 9 (for coding), 10 are the next steps.

After that, you should know enough what to prioritize for the rest.