What have I been doing last month?
Tags: python, learning, machinelearning, datascience
Published: 2020-12-23 09:53:38

Hello for everyone!
[In my last article](https://dev.to/wreemde/66daysofdata-and-the-workload-2j1h) I wrote about my plans for the past month. And now I'm really happy with the progress I've made over the last 5 weeks! 

So, what have I been doing last month?
• I finished a couple of Kaggle micro-courses: [Python](https://www.kaggle.com/learn/python) and [Pandas](https://www.kaggle.com/learn/pandas) as I planned a month ago. In these courses I learned basic Python concepts that will help me start learning data science. Two certificates and two pieces of very useful knowledges in my pocket!

• Further, I repeated Linear Algebra by [this course](https://www.coursera.org/learn/linear-algebra-machine-learning). It's paid but here is a [YouTube playlist]( https://www.youtube.com/watch?v=T73ldK46JqE&list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3). And by the Russian source [mathprofi](http://mathprofi.ru/) I learned derivative, integrals, derivative, series in mathematics and limits.

• Another math related thing is NumPy – Python library which is most widely used for carrying out mathematical operations that involve matrices. The most important feature of NumPy that sets it apart from other libraries is its ability to perform lightning speed calculations. I read [this article](https://www.freecodecamp.org/news/the-ultimate-guide-to-the-numpy-scientific-computing-library-for-python/), but, unfortunately it wasn't enough for me... Do you have other NumPy resources? Apart from the official documentation, of course :)

• Next, I realized that just theory in a library as Pandas and NumPy is not enough! My solution is a lot of practice. 

For the NumPy I do [this](https://github.com/Kyubyong/numpy_exercises) and [this](https://github.com/rougier/numpy-100). I find these exercises a little bit hard, but worth it.

On the other hand, for Pandas I found more outstanding things:
[Exercises](https://github.com/guipsamora/pandas_exercises) and passing these exercises on [YouTube](https://www.youtube.com/playlist?list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv). Learning by Doing! 
And here is my [repository](https://github.com/Wreemde/Pandas_Exercises) :)

• Also I read a Russian-language book about statistics: Stats in Cats. And now I'm taking a statistics course. It is in Russian too, but it seems wonderful to me. [Here](https://stepik.org/course/76/promo). 
But I have a lot of interesting material about statistics in English! So, maybe I'll write a separate article about it :)
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And now I want to tell about my plan on tomorrow challenge #66daysofdata:
• End my statistics course (now I'm in the 2nd week)
• I need Data Visualization [Kaggle micro-course](https://www.kaggle.com/learn/data-visualization). Because 7th pull of the Pandas exercises is about visualization, and I know nothing about Seaborn and Matplotlib! :(
• Practice and more practice! I have to finish Pandas and NumPy exercises. For instance, now my [NumPy repo](https://github.com/Wreemde/NumPy-exercises) looks extremely sad...
• Before starting a full-fledged Data Science/Machine Learning course (By the way, I think between [Andrew Ng](https://www.coursera.org/learn/machine-learning) and [MIPT/Yandex](https://www.coursera.org/specializations/machine-learning-data-analysis)) I need to know something about Theory of Probability. 
• Maybe start this [book](https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X)?
• And I also want to write articles about learning Pandas and statistics. The best way to consolidate your knowledge is to put it on paper or in a blog.

From tomorrow I will regularly [tweet](https://twitter.com/maszanski) my daily plan.

Thanks for the attention! Any suggestions to this article is always welcome. Please don’t forget to comment on this article if you found any mistakes :D