FinPod

Meeyeon (00:02)
Hi everyone and welcome back to another episode of What's New at CFI. I'm your host, Meeyeon and we are diving in with our newest course, Data Analysis in Python. And I am joined by my dongsaeng, Joseph. You've heard and seen him here before. ⁓ Joseph, always so happy to have you here. We are gonna talk about getting started with Python, ⁓ just because this is the follow-up course to that.

The course name kind of speaks for itself. You get started and then after that, we're building on it with data analysis in Python. What is the ⁓ big idea behind this one and how do you think that it complements the first?

Joseph Yeates (00:47)
Yeah, well, thanks, Meon. Good to be here again today. I think the big idea with data analysis in Python is to focus Python onto data analysis. And I guess what I mean by that is there are so many different applications for Python. You can use Python for so many different things. It's super versatile. And we really wanted to make clear that this is learning Python specifically for analyzing data. So...

If you're familiar with Python, you've taken getting started with Python, or you're coming from Excel, really, all of these things can help contribute to data analysis in Python, and you'll be able to upskill from a lot of different areas.

Meeyeon (01:30)
And who did you have in mind when you were designing this course? example, for me, A course like Careers in Capital Markets. I really thought of myself when I was first getting into the industry. For you, did you have a person in mind when you were creating data analysis?

Joseph Yeates (01:46)
Yeah, I think the, yeah, the, the persona that I had in mind were BI professionals. So business intelligence professionals and specifically the Excel user. may have mentioned this before in the getting started course, but we tried to make as much as possible all the data sets and the data scenarios, the real world scenarios in the course based on tabular data. It is a common use case in Python.

Meeyeon (01:46)
in Python.

Joseph Yeates (02:14)
but we're really trying to make it easy or easier, I should say, for Excel users coming from spreadsheets, use to tabular data, and that's how we're going to be using Python in this course.

Meeyeon (02:28)
And let's talk a little bit about the content in the course without giving away too much. ⁓ What are learners going to be doing in the course, the practical skills that they'll develop in practice that you want to highlight for our listeners?

Joseph Yeates (02:43)
Really it covers an entire data analysis slash data visualization workflow. So everything from getting data, importing data into a Python notebook, cleaning, manipulating, transforming data, and then ending with analyzing your data. So doing some basic statistical analysis, mining for insights in your data, and then visualizing the results.

I don't really want to highlight anything in particular because I kind of love a little bit of everything. ⁓ But we do go, everything's really applicable, really hands on from the start to the end of a data analysis project.

Meeyeon (03:23)
And data analysis, we kind of, goes without saying that it is an incredibly important skill, regardless of what industry that you want to work in and what profession you want to work in. Everything is so, you know, data driven today because it is so easy to collect and manipulate data. Why do you think it's such an important skill right now for finance professionals to be able to use Python for data analysis?

Joseph Yeates (03:51)
I think two reasons come to mind. I would say the first is just the size of the datasets that you're working with generally in your day-to-day job is just getting bigger and bigger and bigger. And there are great business intelligence tools and Excel is getting better at dealing with larger and larger datasets. But programming languages like Python can really just take those same data analysis skills.

the next level using a slightly different language in a different environment.

And I think the second point is not just that Python can handle much bigger data sets than Excel, but also Python is native in Excel now. So I think that the expectation is going to be on at least a familiarity to understand what's going on with some Python scripts or some Python code inside of Excel workbooks.

Meeyeon (04:45)
Now I know for me, have a, there's personal elements to I think every type of course that I create or any type of content that I put out is can you share with us a personal experience that you had working with a large ⁓ data analysis project or something in that vein where maybe it was a little bit painful, it was a little bit fun, great learning experience. ⁓ Do you have one where?

you could share something with our audience that's very personal but related to data analysis.

Joseph Yeates (05:20)
Sure. Yeah, I think definitely a lot of challenging memories and experiences to get to this point. But I think one in one of my first roles ⁓ in retail banking, was part of an analysis team. And traditionally, they'd be using SQL and Excel and pivot tables and Excel and things like that as sort of the reporting infrastructure. And I was trying to prove how

we could sort of branch off into programming languages like Python to do some analysis and it can be more robust and it's really cool because it can do all these great things. And so I sort of over-promised, I guess, partly on my expertise at the time on trying to build a time series predictive model to predict ⁓ retail deposits coming into the bank. And so wanting to try and...

predict which customers could we be targeting with emails because they have a propensity to to bring more money into their accounts. So we were able to do it in the end, but I think over promising at first and having to figure out some, some things like time series analysis in Python, how to structure my data in Python, how to visualize the results of that time series and output it in a format that

our retail team could and marketing teams could actually action. There were so many different parts of something that for me seemed so simple at the beginning. And it was all possible in Python in the end and I had to learn a lot, but broken up into lots of small pieces. We were able to get there in the end, but it was challenging for sure.

Meeyeon (07:06)
So that said, ⁓ if there is one thing that you hope that anyone that takes this course ⁓ walks away with, what is one thing that you hope that our learner walks away with?

Joseph Yeates (07:20)
I think that Python is more approachable for more people, I think, than maybe an individual learner will realize. You don't have to be a big coding person or a computer science major or a engineer or anything like that to use Python. Even just small code blocks in Python can really help you when you're using other tools like SQL or like Excel.

Knowing Python and being able to plug in just a little aspect of your data analysis process or pipeline with Python code can just help it be more, more optimized. can be more efficient and you can get

get started analyzing your data and knowing some of these functions for data analysis more quickly than you think.

Meeyeon (08:08)
Love it. Thanks so much for joining us, Joseph. And to our listeners, data analysis with Python is available on CFI now. So if you're ready to go beyond the basics and start doing some real data analysis with Python, this is your next step.

So thanks for tuning in and we will see you next time on What's New at CFI.