# PyMC Concepts for Python Interview Preparation

**URL:** <https://discourse.pymc.io/t/pymc-concepts-for-python-interview-preparation/14670>\
**Category:** General\
**Created:** [June 26, 2024, 5:51am UTC](https://discourse.pymc.io/t/pymc-concepts-for-python-interview-preparation/14670 "2024-06-26T05:51:14Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![furiosa](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/furiosa/32/8248_2.png) [@furiosa](https://discourse.pymc.io/u/furiosa)\
**Post date:** [June 26, 2024, 5:51am UTC](https://discourse.pymc.io/t/pymc-concepts-for-python-interview-preparation/14670/1 "2024-06-26T05:51:14Z")

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Hello all,  
I am currently preparing for a [Python interview](https://www.igmguru.com/blog/python-interview-questions-and-answers/) and looking to deepen my understanding of probabilistic programming, specifically using PyMC. Could you please suggest fundamental PyMC topics or concepts crucial for interview readiness? Additionally, any recommended resources or practice problems to reinforce these concepts would be highly valued. I’m exploring resources or practice problems to solidify these concepts.

Also, I have been following [this discussion on prediction concepts in PyMC3](https://discourse.pymc.io/t/prediction-concept-in-pymc3/7990), but it seems a bit dated. If anyone has more recent updates or guidance relevant to my interview preparation, I’d greatly appreciate your insights.

Thanks in advance for my help ☺  
**Furiosa**

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<div class="post-metadata">

**Author:** ![iavicenna](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/iavicenna/32/7923_2.png) [@iavicenna](https://discourse.pymc.io/u/iavicenna)\
**Post date:** [June 26, 2024, 7:35am UTC](https://discourse.pymc.io/t/pymc-concepts-for-python-interview-preparation/14670/2 "2024-06-26T07:35:38Z")

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Don’t know what your level of knowledge about Bayesian probability is but here is some stuff roughly ordered from easy to harder

For gaining some insight about different types of probability densities and mass functions:

> **[Google Colab](https://colab.research.google.com/github/ricardoV94/probability-puzzles/blob/main/puzzles.ipynb)**

Then for basic stuff on pymc here:  
[https://www.pymc.io/projects/docs/en/stable/learn/core\_notebooks/](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/)

Beginner level model examples/modelling concepts (with pymc):  
[https://www.pymc.io/projects/examples/en/latest/blog/category/beginner.html](https://www.pymc.io/projects/examples/en/latest/blog/category/beginner.html)

Cross-validation might be a topic that maybe comes up in an interview (don’t really have much interview experience, don’t take my word for it)?

> **[Cross-validation FAQ](https://mc-stan.org/loo/articles/online-only/faq.html)**
>
> loo

If you have time before the interview and want to do some reading on Bayesian modelling then

> **[Doing Bayesian Data Analysis](https://sites.google.com/site/doingbayesiandataanalysis/home)**
>
> For more information, please click links in menu at left, or in the pop-up menu on small screens (see menu icon at top left).
> There may be formatting infelicities on some pages. In August 2020, the site host (Google Sites) required migration to new...

could be a good start (also exists in the books section of core\_botebooks link above). This notebook here contains exercises from this book solved in pymc:

[https://nbviewer.org/github/cluhmann/DBDA-python/tree/master/Notebooks/](https://nbviewer.org/github/cluhmann/DBDA-python/tree/master/Notebooks/)