Data Science Path Prerequisites?

Hi there, I am really interested in the Data Science career path, however, I am unsure if I should start it right away or complete the Python and SQL courses first. Is the content the same? If I do the data science career path should I go back and complete python and SQL? What do I need to know before starting this path? I want to make sure I have a comprehensive education, and I am just unsure on which end to start. If I complete basics in the Python and SQL courses will those autocomplete in the Data Science path?

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Welcome to the forums!

There’s no prerequisite to start. There are many things you can/should supplement your studies with though, depending on what topics you’re interested it. It never hurts to review statistics if you’re doing data science.

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Hi, welcome to the forums!

As someone who is on the DS path & has taken some other courses prior, some of the content does overlap. By that, I mean, some of it’s the same content, so you’ll get a checkmark (as completed) in the different courses.

Oh! And I second what @toastedpitabread said about stats. That’s a part of hypothesis testing with SciPy , learn stats with Numpy on the DS path (units 14-16).

If you’re already a Pro member, I say do the DS path. If not, I believe the Python 2 course is free content.

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I am a pro member, so yeah I can start right away. So just to reiterate, any of the same content completed in either the Data Science path or regular python course will transfer and autocomplete? And you think I can just start in Data Science without working through Python first?

Also how much math will I need to review? I haven’t touched stats or anything math related in YEARS, and frankly didn’t get much farther than precal. I didn’t even consider that aspect of it, Data Science just sounds cool and more like what I want to do, compiling data, analyzing it, and presenting it.

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In the past few years I took a SQL Intensive and a Data Analysis Intensive (both no longer offered). Some of the work I did in those courses showed up in the DS path. Even though I completed it, I still erased the work and went back over it again b/c redoing stuff never hurts.

You work up to Python in the DS path. It’s part 6–“Python Functions and Logic”.
There are extra projects that you can do so you solidify concepts in your brain as well. You can go off-platform and do it on your own (w/regards to installing a sql server on your machine and using Jupyter Notebooks for Python).

As for math, I think one just needs to start thinking computationally. For me, honestly, math was never a subject that I excelled at. I took stats in grad school (eons ago) and it made sense to me b/c I was using it on data that mattered to me. So, go figure! :slight_smile: When I returned to hypothesis testing with Data Analytics (and Science) I took notes & made sure I knew what I was doing before moving forward. (It’s a marathon not a sprint IMO).

It all depends on you and what you want to do. It’s different for everyone.

DS also includes Machine Learning. I am 73% of the way thru the path and I don’t know anything about ML. I’m excited about it. So, we’ll see how it goes! :slight_smile:

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Like @lisalisaj said it depends on what you want to do. I’m interested in ML personally but I feel like I need better calc/stat/linear chops to give it a serious go. So I’m trying to build that up slowly while I study other things.

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Forgive me if I am barking up the wrong tree here instead of talking to a CS professor, but I am curious how and how much math is involved in Data Science and machine learning? I took CS 101 years ago at IUPUI and they mentioned that the CS degree takes you through calc 3 and highly encouraged linear algebra, but from the web development and c# I have learned through a bootcamp we didn’t really do any math. Aside from creating accounting software, where does all the math come in to play? Frankly it was never a strong subject for me. I think a large part of that is you need a fairly high understanding before you can really get into the whys of how things work with math. I always felt like I had all these formulas but never knew how or why they worked so it didn’t stick because I didn’t have a reference point as an anchor. I am excited to start the data science path today! But I am also worried about my lack of Math chops to translate to real world use! I got through algebra and Geometry and that is about as far as I got. I always kinda liked physics because at least I understood what we were using it for, seeing real world applications was cool!

Thank you both for answering my questions and bearing with my ramblings!!!

I never took calc. I’m reading up on linear & stats as I go. :slight_smile:

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You’re not writing out the calculations long hand or with a statistical calculator (like I had to do in grad school). :slight_smile:

For hypothesis testing–you need to understand what a null and alt hypothesis are, mean, median, probability, variance, standard error (which is the sqrt of the variance/ sqrt of observations), etc.

BUT, that said, take notes while you’re studying, go over what these things are and practice. Make sure you understand what you’re learning with hypothesis testing before moving on. Once you do it a number of times you will understand it.

ahhh that all makes sense! Will do, great advice. I am starting the data science path as we speak!.

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I have a book on scikit learn and tensorflow. When they start going over the details of why things work they do it really pushes my math skills: example: https://medium.com/@purnasaigudikandula/linear-regression-in-python-with-cost-function-and-gradient-descent-bde9a8d2626

btw the book is hands-on machine learning with scikit-learn, keras, and tensorflow published by o’reilley. Highly recommended if you want to dive in. But it’s very math-y at points.

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The cool thing with python is that there are modules like NumPy and SciPy that have built in functions that will calculate things for you. ex: np.average``np.percentile,
and in scipy.stats, there are a lot of functions too, like ttest_1samp() to do a one-sample t-test. There’s a LOT already done. You just need to understand the numbers that you’re inputting via your data.