The 3 Biggest Mistakes on Learning Data Science

Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.

Hello! It’s me again. I’ve discussed parts of what I’m going to mention here in other articles, but now I want to give a few directions on what’s not data science and how not to learn it.

So let’s start with the basics.

What is Data Science?

Updated by memes_and_science
Data science not just knowing some programming languages, math, statistics and have “domain knowledge”.

The time has come. We’ve created a new field, or something like that. There’s a lot of things to say and study in this field. It doesn’t matter the name, maybe data science is just a temporary name for a bigger field, but the scientific study of data, getting insights from it and then be able to predict something is the present and future of the world.

I’ll focus on business related definitions and proposals for data science, maybe these can apply for the field as a whole, but the ideas in this article are about data science for business.

I’m going to propose three things:

  1. Data science is a science
  2. There are awful ways to learn data science
  3. Using well created cheatsheets can help you do data science in a systematic way

Data Science is a science

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I know this maybe controversial for some people but stick with me. What I want to say here is that data science is of course linked to the business, but it is a science in the end, or in the process of becoming one.

I defined data science before as:

[…] The resolution to Business / Organizations problems through mathematics, programming and the scientific method that involves the creation of hypotheses, experiments and tests through the analysis of data and the generation of predictive models. It is responsible for transforming these problems into well-posed questions that can also respond to the initial hypothesis in a creative way. It must also include the effective communication of the results obtained and how the solution adds value to the Business / Organization.

I’m stating here a description and definition of data science as a science. I think it could be very useful that data science can be described as a science because if that’s the case, every project this field should be at least:

  • Reproducible: Necessary for making easy to test other’s work and analysis.
  • Fallible: Data science and science doesn’t look for the truth, they look for knowledge, so every project can be substituted or improved in the future, no solution is the ultimate solution.
  • Collaborative: Data scientists don’t exists alone, they need a team, this team will make things possible for developing intelligent solutions. Collaboration is a big part of science, and data science should not be an exception.
  • Creative: Most of what data scientists do is new research, new approaches or takes on different solutions, so their environment should be very creative and easy to work. Creativity is crucial in science, is the only way we can find solutions to hard and complex problems.
  • Compliant to regulations: Right now there are a lot of regulations in science, not that much in data science, but there will be more in the future. Is important that the projects we are building are aware of these different types of regulations so we can create a clean and acceptable solution for the problems.

If we don’t follow those basic principles it would be very hard to conduct a proper data science practice. Data science should be implemented in a way that enables decision making to follow a systematic process. But more on that later.

How NOT to Learn Data Science. The big 3.

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If you are here it’s probable that you are learning data science right now, or you took some MOOCs or even classes on the field. I’m not going to talk bad here about platforms or bad courses, I think we something can learn even in the worst courses.

1. Seeing and seeing without practicing

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If you are taking a class on anything related to data science, like math, statistics, programming or something like that, and you are there just listening to the class with this face:

Well you are wasting you time. Data science needs practice. Everything you learn, even though if the professor doesn’t tell you, practice and try it. This is fundamental to really comprehend things and when you are working in the field you will be doing a lot of different practical stuff.

A good knowledge on statistics, math and python won’t make you a successful data scientist. You need more, you need to master your craft. Be able to use these tools to solve business problems. So if you are learning something new, and you want to understand it for real, find a scenario where you can apply it or play with it.

2. Creating models in a crazy way

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We get the data from the “outside world” and our body and brain analyze the raw data we got, and then we “interpret” things.

https://towardsdatascience.com/going-beyond-with-agile-data-science-fcff5aaa9f0c

What is this “interpretation”? Just what we’ve learned about how to react, think, feel and understand from the information we are getting. When we are understanding we are decoding the parts that forms this complex thing, and transforming the raw data we got in the beginning into something useful and simple.

We do this by modeling. This is the process of understanding the “reality”, the world around us, but creating a higher level prototype that will describe the things we are seeing, hearing and feeling, but it’s a representative thing, not the “actual” or “real” thing.

So think before you do this:

model_i_created_i_5_seconds.fit(X,y)

Modeling is something very important in the machine learning and data science space, but they must have a purpose. And you have to understand them before using them. Now what they assume from the data before training it, understand the different metrics they use to learn, how to evaluate them and more.

For that I can tell you, there’s no harm in reading the documentations of libraries like Scikit-Learn:

Apache Spark:

Tensorflow:

And more. They’ll lead you to articles, papers and more blog posts and most of them will even have practical examples on how to do modeling in machine learning and statistical learning.

Also there are great videos in the field that will take you from zero to hero in minutes like the ones from my friend Brandon Rohrer:

3. “ Yeah, I’m a lone wolf. I can study and do everything by myself”

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Remember that one of the characteristics I proposed before is that data science is a collaborative field. Well studying it too!

I’m not saying here that you need to start a course with your BFF but make use of what the online platforms give us today. We have forums, chats, discussion boards and more where you can meet people learning the same things you are learning. It will be much easier to learn with more people, and don’t be afraid on asking questions.

Ask as many questions as yo need to understand something, and don’t rest until you do it. Don’t harass people either, but if you ask politely most people will be more than happy to help you.

Here are great resources (apart from the ones the MOOCs and courses have on their inside) to find people learning the same things as you:

Systematic Data Science with cheatsheets

https://medium.com/personal-growth/all-strength-comes-from-repetition-1a95157e2c7c

Cheatsheets save you time by providing bits of knowledge about different pieces of the language, concepts or libraries. Some cheatsheets also contains hyperlinks to the documentation and package-level cheat sheets for the most important packages in R, Python, Scala and more.

In the end of last year, I created a repository that went viral about all the different cheatsheets you can use to do data science.

In the repo you’ll find cheatsheets about:

There you’ll find a PDF and a PNG version of every cheatsheet. Feel free to download the repo as a zip to get all the information, and if you find or create a new one that you think it’s useful please create a pull request with it.


Thanks for reading, and hopefully this can help you find the path to be a successful professional in the data world. More in the future :)

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