Mısra Turp

To boost your performance and code like a pro

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Photo by Sid Balachandran on Unsplash

Prefer to watch this? Check out the video version on YouTube.

Isn’t Pandas the best? It is such a great library with so much potential and so much flexibility. I remember the times when I just started using it. I immediately fell in love with it.

Don’t get me wrong, it does have a steep-ish learning curve. Not everything is immediately obvious from the start. There are a couple of tricky concepts in Pandas. And these will be the things to take you to 80% with just 20% effort.

Here are the main working principles of Pandas I have picked up over the years that will boost your coding performance. …


Without having to learn web development

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Photo by Ga on Unsplash

Prefer to watch this? Check out my Streamlit tutorial on YouTube.

Having a portfolio is crucial to land the data science job of your dreams. And as long as you get your hands dirty working with data working with interesting use cases, you will impress your future employer. But you can always go the next mile when it comes to presenting… and make an interactive web app out of the project you built.

Until very recently this required one to learn web development and start complex React or Angular projects but no more!

Streamlit is an amazing tool that makes it extremely easy to build an interactive front-end. It is specially made with data science projects in mind and thus has a lot of useful functionality to show off your projects. …


for your job search and career

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Photo by inlytics | LinkedIn Analytics Tool on Unsplash

LinkedIn is a great source of people who can help you with your professional life. This could be getting advice, acquiring information about a company, hearing about open positions and much more. Though, in order to get the most out of it, you need to follow a smart approach rather than a shotgun approach of sending connection requests and copy-paste messages to everyone. This involves who you should connect with, how you should approach them and what you should say in your messages. Let’s start with what kind of people you can get in touch with.

1. Make a list of the profiles you’re looking for

It’s very tempting to see a lot of experts, thought leaders and people who are experts in areas you want to work in. But trying to reach a person who gets thousands of replies to their posts might prove to be in vain. They will likely not reply or even see your message. Instead, you should choose people who are accessible and who still have value for you. It is harder to find those people because they will not pop up on your feed as often as the popular thought leaders but it’s still possible. It will only take a little bit more thinking and planning. And the first step of this planning is thinking of your goal: “What are you currently trying to do?” The answers could…


And people who claim it is dying, are only after your clicks

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Photo by Icons8 Team on Unsplash

I would understand it if you were worried about data science dying. You’re getting interested in a new field, looking into how to study it or maybe you’re already studying it and then there is this talk of it dying. You see questions on Quora or on Reddit, tons of articles written as clickbait, seemingly naively asking “Is data science dying?”.

Let’s see why not by going back to when the term first started becoming a fact of everyday life.

Data science was born out of a need

There are tons of unnecessary jobs out there. Created by unfunctional, inefficient corporate dynamics. People who manage people who manage people… Positions that do not actually produce things but are there for some reason that is not based on logic. You can read more about it in David Graeber’s book Bullshit Jobs. But data science is not one of them. …


Focus on team structure

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Photo by Helena Lopes on Unsplash

Job hunting sometimes feels like looking for a candle in a dark room we’re unfamiliar with. We don’t have complete information, every company has different standards, and it’s unclear where this position might take you in the coming years. This is true, especially in data science because data science work is far from being standardised. There are many layers and levels of how different your work might be than another data scientist’s.

Previously I talked about what type of positions you can end up in (in-house/consultant data scientist/freelancing/etc.) and the difference between the seniority of positions. …


Solve errors faster and have more time for creative work

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Photo by Christina Morillo from Pexels

Debugging is a funny-sounding word. The word originates from an actual bug getting in a computer and impeding the computer’s function back in the first computers’ times. Since then it has taken a new meaning. Now, it means finding the source of a problem in your code and resolving it.

When you’re first starting out with coding, debugging your code or resolving errors can be one of the hardest things to do. After all, the courses that teach how to code do not provide you with the tools you need to find the source of a problem and fix it. …


Especially if you don’t have a background in computer science

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Photo by ThisisEngineering RAEng on Unsplash

If you’re new to data science, you might be struggling with the coding. Maybe you sometimes get an error that makes you feel like you might not be able to ever solve it. Maybe you feel like it takes you way too long to solve arising errors. Well, I’m here to tell you that is okay. And in fact, it is actually good. Let me tell you why.

Some of you may know that I launched my online course Master the Data Science Method nearly two weeks ago. That course is aimed at guiding students through the journey of building their first-ever project. …


Getting Started

And set yourself apart from the competition

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Photo by ThisisEngineering RAEng on Unsplash

It is pretty much a stereotype that data scientists can’t write clean and understandable code. This doesn’t have to be the case for you. By learning a few principles of how to write code properly, you can use the stereotype to your advantage and set yourself apart from the competition.

To find the best practices, we should look no further than the reliable practice of software engineering. Here are some software engineering principals to get you writing clean, readable and easy to work with code as a data scientist:

Read documentation

When it comes to working with new technologies, one of the biggest mistakes I see data scientists make is to ignore the documentation. The documentation of a tool or library is solely prepared to help users have an easier time applying the tool to their work. …


Quick tips and simple rules I follow

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Photo by Michaela from Pexels

As a data scientist, my work has many layers. I need to keep track of project requirements, demands from stakeholders, code development and new ideas I have that I want to try out. On top of this, I would also like to stay on top of new things I want to learn.

It’s easy to see why if you don’t have a plan that works for you, things might get out of hand. Luckily, my boyfriend is a productivity teacher. …


Or why practising makes you better

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Photo by ThisisEngineering RAEng on Unsplash — Your workspace doesn’t have to look as cool as hers though. Mine certainly doesn’t.

There are tons of courses out there to learn data science. Unfortunately, most of them teach you data science skills only in theory. To become a fully formed data scientist you need to go further than theoretical online courses, you need practical knowledge.

This might sound familiar to you if you have taken my data science kick-starter course where I mention the three crucial levels of data science knowledge; theoretical knowledge, technical knowledge and practical knowledge.

Theory and technical knowledge are things you can simply pick up from online videos, courses and instruction. On top of these, you need to practice to acquire practical knowledge. I define practical knowledge as knowing how to bring together everything you’ve learned and being aware of the data science way of doing things. …

About

Mısra Turp

Data scientist at myTomorrows, previously at IBM. Moonlighting as a guide, helping people switching careers to data science on soyouwanttobeadatascientist.com

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