### Pandas Task #1 For **pandas** level of Data Science course we are asking you to do two courses on [Kaggle](https://www.kaggle.com/) platform to make sure everyone starts from the same level when working with data! | Course | |---| | 1. [Intro to Programming](https://www.kaggle.com/learn/intro-to-programming) | 2. [Python](https://www.kaggle.com/learn/python) - In first course, pay special attention to `functions` and how they help to avoid duplicate code and structure your programs nicely. - In second course, pay attention to `lists` and `dictionaries` you will use those **a lot** when writing code. Please do this before **22 March**, so we all move at the same pace! ### Kaggle If you are done with the task and interested in learning more about Data Science on your own, Kaggle is a great place to check out! Kaggle community is centered around Machine Learning contests with real money prizes. If you are just starting out, I would encourage you to ignore contests in favour of other resources like: - `courses` - `datasets` - `notebooks` ### Notebooks `Notebooks` you can find under [code](https://www.kaggle.com/code) tab deserve a special mention - they allow you to write and execute Python code on remote machines without having to worry about configuring your local computer! We will get to writing notebooks soon, but lets start by reading code written by others! **For extra credit:** 1. search [code](https://www.kaggle.com/code) tab of Kaggle to find interesting notebooks 2. share it with other learners in `RegenLearnings/Pandas` chat Some tutorial on topic of `data visualization` or `exploratory data analysis (eda)` can be a great starting point, but feel free to pick whatever seems interesting to you! **Notes:** - If don't enjoy Kaggle flavour of Notebooks, [Google Collab](https://colab.research.google.com/) offers a good alternative with similar features. - We call those *Jupyter* Notebooks, because first programming languages supported by the project were (**Ju**lia + **Pyt**hon + **R**) - Jupyter is a serious tool used in real-world Data Science! ### Extra Resources - Video by Rob Mulla introducing [Jupyter+Kaggle](https://youtu.be/5pf0_bpNbkw?t=1090)