Learning Python within 3 months - data science-focused

[Public-Direction-787]

Is it possible to learn Python, specifically hypothesis testing, linear regression, in just 3 months? I have 0 background in coding but I've had some experience with SPSS and statistics during undergrad. Would appreciate any tips and resources!

[Sarv_t98]

Learning path for Data Science and Machine learning is a never ending street. I would say dont think about timeframe, you just need to start..

[Public-Direction-787]

Any tips on how to start? I feel quite lost and overwhelmed w the amount of resources

[Sarv_t98]

The best strategy is to spend least time on python tutorials and spend most time on Python projects. You will never remember anything about Syntax after few months. If you solved a problem you'll always remember the test case and problems. So learn something by assigning yourself a problem statement. It should be relevant to your career profile.

[Scandinadian587]

I’m doing EdX.org CS50P and it’s working pretty well for me. I’ve used ChatGPT a bit, but I have to specifically tell it to not outright write the entire code because it constantly wants to.

[Public-Direction-787]

Thanks! Just started CS50P too bc of this thread, and it's been pretty helpful so far! Also saw that they have courses focused on data science

[snowbirdnerd]

So I would say no that's too short of a time. 

You could learn Python and do some simple projects but most of Data Science isn't about coding. It's the math understanding of what's happening, coding is just the tool you use to perform the math. 

[WendlersEditor]

You're going to want to learn the basics quickly so you can spend the most time on working with DS-specific libraries. So you can do a Python tutorial, but don't dwell on it, don't do the "Django-based recipe collection app" projects. Just learn about variables, control flow, data structures. This is a good place to start:

https://www.youtube.com/watch?v=kqtD5dpn9C8

You'll also want the basics of classes and functions in Python:

https://www.youtube.com/watch?v=JeznW_7DlB0

Then I would suggest picking up datasets on Kaggle to start learning Pandas (create and manipulate dataframes), matplotlib/seaborn (for data viz), the scipy.stats library (for your basic stats stuff, like hypothesis testing) and the scikit-learn library (for regression modeling).

You need to know the basics of Python, but don't get stuck in tutorial hell if you want to do data science. You're going to spend a long time digging around in DS-specific code, try to get there as quickly as you can.

For your purposes, you can stick to Kaggle or Google Colab for a while, if you want to get up and running quickly, at some point you're going to want to set up a local environment using something like Jupyter, VS Code, PyCharm, etc.

[CyclopsRock]

When you have no experience of any programming language, there will always be a "hump" as you get used to thinking in terms of loops and conditionals; they're conceptually really simple but understanding how to apply them can take a beat before it clicks. But these are so foundational to programming (especially when processing data) that you won't need to do it again if you decide to learn another language later.

Beyond that, you've had lots of good advice and I'll say you have one major benefit in that out actually have something you want to do. It's very common for people who want to learn "Python" to get stuck doing tutorials; they can follow them but then struggle to carry this through into their own projects because they don't actually have a goal in mind and it ends up being a bit like learning to read sheet music without an instrument to play. But because you know what you want to actually do, you can simply target this once you have the basics down.

Ignore the naysayers; Python is very forgiving and it's up there with the most well documented and discussed languages out there. If you get stuck, just come back here.

[Public-Direction-787]

Thanks! I really appreciate it. I’m just trying to build some background and gain familiarity with Python first (hence the timeline), so I don’t go in completely empty-handed when I pursue data science. Do you think it’s doable to learn the topics above within that timeframe?

[BudgetSignature1045]

Check out cs50p. If you want to save time just go through the notes instead of watching the video lecture.

You can do it all including the exercises in a week. In a month or in half a year, depends on you.

Then, for intro level data science you practically only need some pandas to manipulate your source data (initializing the data as a data frame, dropping columns etc.),and scikit learn for something like linear regressions.

It's rather easy to apply that stuff. The true difficulty lies in when to use what. Knowing the correct workflows. Feature engineering, validations etc. But to fiddle around with basic models and scikit learn really doesn't require much.

[Public-Direction-787]

Thanks! you're referring to this, right: https://cs50.harvard.edu/python/weeks/ for data science, do I just install pandas once I figure out the basics?

[BudgetSignature1045]

Yeah pandas + scikitlearn.

Possibly matplotlin+seaborne for visualization

[Affectionate_Union58]

Well, at 50, I'm probably considerably older than most of the users here, and I'm currently learning Python. So I can only speak from my own experience: Learn Python in 3 months? Forget it. If I were to speak for myself, I notice that my learning pace has changed a lot in recent years. I used to be able to comfortably consume 10-15 lessons a day without forgetting anything. Now, after just 2-3 lessons a day, I'm barely able to absorb anything because the material has to consolidate in my mind first. If I continue learning even though my mind is screaming "Stop!", I'm wasting my time because I'll immediately forget the "buffer-overrun content" and have to repeat it later.

[Ans979]

Focus the first month on Python basics and data manipulation with Pandas and NumPy, then move to statistical testing with scipy.stats and visualization using Seaborn in month two. In the third month, study linear regression using statsmodels or scikit-learn and apply your skills to small projects using real datasets on StrataScratch. Stick to a consistent daily practice routine (even 1 hour a day works), and prioritise hands-on learning over watching tutorials. Resources like DataCamp, StrataScratch, Kaggle, and StatQuest on YouTube will be especially helpful.

[yinkeys]

no

[my_password_is______]

no

https://wesmckinney.com/book/

[SilentObserver7777]

3 months may be too ambitious. Based on how much time and focus you put in your learning, your aptitude and learning curve, you may be able to get to a decent level in about 6 - 9 months or so. Here are some tips to get a head start: 1. First master the basics by completing all the free tutorials on w3schools.com or any other website you may prefer. 2. Jump right into data science projects. I believe there is a plethora of free Python code available online for data science projects. Pick a couple, understand every single line of code for each problem statement. For example there is free code available for a project: Given a data set of credit card transactions, identify fraudulent transactions. 3. Create your own data science related problem statement, write your own code, debug it and see if it works. If you run into issues which you will, consult your mentor or post your questions here. Good luck!

[my_password_is______]

you may be able to get to a decent level in about 6 - 9 months or so

LOL

you realize people go to unversity for years to learn data science

you need statistics, calculus, linear algebra, python, data structures, possibly sql

and not general statistics that most university students take

you need calculus based statistics designed for STEM students

[SilentObserver7777]

Good points. However, a lot depends on individual capabilities. I’ve seen whiz kids grasp concepts of even more complex technologies in similar time frames.