Federico Trotta - from Medium

July the 22th

How to choose the right project for you

Hey friend,

one of the things newbies in Python programming and Data Science struggle the most is how to choose the right project.

Today, there are a lot of resources out there and a lot of courses that help you, providing data to create your personal project.

But you know what?

What's the difference between junior professionals who have the same grade of education, fresh from a University degree, and others who have developed the same projects?

None.

Or, at least, for hiring managers and HRs there is no particular difference.

Yes, if you graduated from a course in development or Data Science you may have some points more because, somehow, you have experience.

But what if 3 people who concluded the same course and made the same projects apply for the same position in the same company? How could HR choose between them?

Well, there won't be any difference between them, of course.

But, please, don't get me wrong: is not that you don't have to take courses.

Is that you shouldn't only make the project of the courses. These are important to learn programming and Data Science.

But to differentiate yourself and get the job you deserve you need to do personal projects.

"OK Federico, very clear. But I don't know what projects I have to do".

Let me be very clear: the reality is that you have to understand yourself. The truth is that you don't know which industry you'd like to work in.

Data Science is a very broad field and you could work in fields like Manufacturing, Finance, Medical, Sports, Biology, Climate Change, and many more.

So the question is: which data you'd like to analyze every day?

What's your point? Who'd like to help?

Are you interested in climate change? Then, find companies who work in that field and that you'd love to work with and create 2-3 projects on Data Science for climate change, then apply.

The hardest part of getting your first job is that you don't know yourself.

It took me more than a year to really understand that I wanted to be a Technical Writer. Talking about AI and Machine Learning, helping people like you understand complex ideas behind them, and developing a career centered on Python and Data Science, gives me more purpose than being a Data Scientist struggling with the lack of data.

So, here are some takeaways to help you choose the right project for you to develop your personal career:

1. If you're already working in a particular industry, you like it but want to change role and move to Data Science, get access to some data. Every company has data somewhere, so you need to get them.
   "Yes, Federico, they have it, but they're a mess!".
   Welcome to the real world, friend! Unless you're working in big and structured companies, it's highgly unluckily that you'll have your data prepared (generally, by a Data Engineer) and ready for Machine Learning. Learn how to organize them and develop the right skills: you may also discover you'll like more Data Engineering than Data Science (I've seen many folks I know following this path so, trust me: as I said before, it's more important to understand yourself than just complete courses).
2. If you're not working or if you're working but want to work in data in another industry, you have to get the data somewhere and analyze it. You have a couple of different possibilities:
   a) Go on Kaggle and search for some data you may be interested in. But take care: don't do the projects everyone does. We've already said that. So, search for datasets that interest you for the particular industry they refer to.
   Also, explore other sites that store and provide data like the FAO and the World Bank websites, but there are many more.
   The idea is to differentiate yourself from others to get your first job, so don't do what the others do.
   b) Use web scraping to get your data. In case you don't know, web scraping is a technique we can use to get data from the web.
   You'll need to target a website you're interested in, get the data from it, store it locally (i.e., in a CVS file), and analyze it.
   Of course, it's a longer way than point a), but didn't we talk about differentiation? Learning web scraping is an important asset that can even become your work (it's also very well paid!!).
   "Ok, but where do I start?"
   There are 3-4 different Python libraries. My advice is to begin directly with Selenium because, in my experience, is the most complete and somehow user-friendly than the others (and, as a rule of thumb, with Selenium you can do whatever the other libraries allow you to do, and much more).
   So, pick a course on Selenium, for example on Udemy, and start scraping the web.
   Then, let me know how it goes.

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MY LATEST ARTICLES

Here are the articles I published this week:

• Are you struggling with Python lists? Here's a comprehensive guide to help you get all the knowledge you need. Read it on Medium: Python Lists: The Definitive Guide for Working With Ordered Collections of Data​
• Did you know that, in Python, to preserve the order of dictionaries we've better use a particular configuration? I've written an article for Stack Abuse. Read it for free here: Data Integrity: How OrderedDict Preserves Key Order in Python​​
• Have you ever thought of creating interactive visualization? They really improve your work! Read my Medium: Building Interactive Data Visualizations in Python: An Introduction to Plotly​

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That's all for this week.
I hope you like my advice. If you want to give me feedback on how this newsletter is helping you, or on how I could improve it, just respond to this email: I'd be glad to get some feedback from you to help you better develop your career! :)

Have a lovely weekend,

Federico

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