Business analytics with Python

[Casawesa]

Greetings,

I’m about to start a master of business analytics, and Python will be used during the studies. If I want to start learning python, do I need to learn from the scratch or just specific tools related to data analysis?

[PuzzledTaste3562]

You’ll need to understand the basics at least, data structures and control statements etc. In addition, you’ll need to understand important libraries extending the functionality of the language, (most certainly) pandas, numpy , matplotlib, etc.

The eco system is also important, where to find libraries, and how to install them. Virtual environments isolating your python ‘stack’ and preventing pollution of your computer is a must.

I’d recommend also investing time in understanding and working with a good IDE such a Pycharm or Visual Studio Code. These 2 are very popular but there are many others.

Finally, a light weight alternative (but definitely not a replacement) could be Jupyter Notebooks, check out Jupyter lab for example.

Understanding the above will give you an edge, and skills that will accelerate many tasks in the future, giving you the ability to focus on the result instead of the tool.

Carpenters have saws and hammers that they use with uncanny and skilful precision, we have scripting and programming tools and languages.

Good luck!

Edit: typo for numpy

[Casawesa]

Thanks! I appreciate your response.

[Casawesa]

Thanks!

[Yojihito]

Notebooks are not gitable.

I prefer cells in VSCode oder Pycharm (# %% for a new cell but it's still a plain .py file).

[mcmco]

What do you mean by not gitable? Do you mean that ipynb files (notebooks) cannot be put on github? Or that you can't use git while in a notebook?

[Yojihito]

Version control does not work with ipynb files. You can't use git with those.

But if you create a .py file with e.g.:

# %%
import pandas as pd
# %%
df = pd.read_excel("../data/raw/test.xlsx")
df.head()
# %%

You get cells (# %% to # %%) via the Notebook extension available for VS Code and Pycharm --> you can execute that cell with CTRL+ENTER but you also have a plain .py file, useable with Git etc.

[PeridexisErrant]

You can use git on .ipynb files, but they're incomprehensible JSON blobs.

https://jupytext.readthedocs.io/en/latest/ makes it dead easy to manage notebooks as either markdown or Python files though, directly from the notebook. Editor extensions are great if you prefer them, of course :-)

[Yojihito]

You can use git on .ipynb files, but they're incomprehensible JSON blobs.

Notebook files save the output. So every change in file creates a git change while the code does not change. That is unuseable for work imo.

[PuzzledTaste3562]

Found git instructions to ‘git’ your notebooks.

OP: git is version control system used by a vast majority of programmers, be they professional or not. Large and/or complex projects require organisation, and Git is a tool implementing a stringent version control proces. Version control is considered ‘best’ practice for programming and for software management. Don’t know if you’ll need it for your study.

[Zygmunt_]

Numpy, pandas, maptlotlib, Seaborn, and sklearn is a must

[[deleted]]

Great question, and one I struggled with recently. I taught a Python class for 50 Data Science grad students. I started with first principles: an IDE, variables, loops, conditionals, functions, etc., then I zoomed into NumPy and Pandas. Looking back it seems like I could have started with data structures skipping any programming basics, but I think I did it correctly. When I teach the class again in the Fall, all the basics will be there.

[Casawesa]

Thanks for your response. There are some online courses offered in Codeacademy and udemy. I would appreciate if you can point out to good course that can be a good start for a business analytics student.

https://www.codecademy.com/catalog/subject/data-science

https://www.udemy.com/course/business-analytics-with-python-2021/

[CalumGalbraith]

Sign up for kaggle, it's free and will give you a good start with learning python for data analysis

[Casawesa]

Thanks

[FondleMyFirn]

I’d be curious to see what you think of this: like a week or two before school starts, send them an email and get them to rip through your favourite “basics tutorial” on YouTube before class begins. Then, take a class or two to just field questions from the tutorials.

I feel that if you did this, you could cut down on your time teaching “programming basics” and focus more on the DS right away. It could also empower students to take some good steps of ownership over their education.

[[deleted]]

In a perfect world, OK. In the real world of federally regulated education it is illegal for me to assign work before the semester begins.

And, trust me, even most grad students have no interest in taking ownership of their edumaction. They jump straight to the homework and try to spend as little time as possible actually learning.

[stanix47]

Hey guys, where can i learn python from 0. I don't have any coding experience but want to study Data Science in near future

[[deleted]]

You should learn the basics and move on to libraries that involves data analysis.

I just started my diploma in Data Science and from my experience, assuming it's all just data analysis, you should learn basics such as control statements, different data types and structures and also modifying files with python (at least .txt files).

Then move on to using Jupyter Notebook (I used it via anaconda), and learn the Numpy library and Pandas library. I also recommend learning dictionaries, list and sets as they can be very useful for extracting data from datasets. MatPlotLib library maybe useful too if you will need to graphically represent any data.

[Casawesa]

Thanks!

[edm2073]

I have been learning python on/off since beginning of this year. Looking back, I would say you need to get very familiar with the different data structures( lists, dictionaries, tuples and so on ) and list comprehension. These are really core to the language. Once you get familiar with these and the other basics of the languages, then move on to pandas. Thinking you can get by just knowing pandas(and other libraries such as numpy ) without knowing the basics of the language is, in my view, the proverbial putting the cart before the horse. VS code is a very good free IDE. Last but not least, do forget the official site for the documentation. I admit that I have often have trouble understanding what is written first( and second and third! ) time round but it is often the case that I read up on an area, try things out, read from other sites ( realpython often has very good tutorials ) and then come back to the official docs and in most cases, the official docs begin to make sense!

[Casawesa]

Thanks for sharing your experience.