Struggling with data science? 5 Common Startup Mistakes

Data science, first done right.

You have chosen one of the most rewarding and fastest growing careers in Tech.

But here's the truth: most students spend months (even years) bending their wheels with the wrong things. Avoid these mistakes to fast-track your Science career.

After 4 years + working in the field, I have seen exactly what separates those who enter for the first time in science work quickly … For those who have never done endless tutorials.

In this article, I'll break down the five biggest mistakes that hold novice scientists back so you can actively avoid them.

Not learning basic math

Statistics are so important … and yet so overlooked.

Many people, even practitioners, think that you don't need to know the basic math behind data science and machine learning.

It's really good to perform backpropagation by hand, build a decision tree from scratch, or build an A/B test from scratch.

Therefore, it is easy to take this for granted and avoid reading any background opinion.

However, this is dangerous and I do not recommend it.

Sure, you can build a neural network with a few lines of Pytorch, but what happens when it behaves strangely and you need to debug it?

Or what if someone asks you what forecast period is closest to your output from a linear regression model?

These situations come up more often than you think, and the only way to answer them is with a solid understanding of the underlying math.

Think of Maths as your brain's data science app. Every model, every algorithm, every productive intelligence runs on it.

If your OS is buggy or outdated, nothing else runs smoothly, no matter what your tools are.

Lay the foundations now while you are in the learning phase, as this will allow you to move much faster later in your career.

Trying to find the “best” courses

I am often asked:

What is the best course?

I really like you, but this question needs to go.

As a complete beginner, the best course is the one you choose and complete.

Most introductory courses in data science, machine learning, and Python will teach you the same things.

You may find a teacher or teaching style better than another, but basically, you will get the same experience as someone else doing another course.

Discriminating in action and going to the beginning, you may change your direction if you feel it is not done right. Stop betting.

like A famous saying GO:

The best time to plant a tree was 20 years ago. Today is the best time.

Everyone's journey and background is different, and there is no “How” to get into data science.

So, take everyone's advice (even mine) with a grain of salt and adapt accordingly. Do what feels right and good to you.

Do not do Project Based Learning

In this article, another common rider is hell.

Trust me, that's not where you want to be.

If you don't know what the hell course is, this is it blog post It explains very well:

It's a tutorial where you write code that others tell you to write, but you don't understand how to write it yourself when you're given a blank slate. At some point, it's time to take off the training wheels and build something yourself

You follow lesson after lesson and don't try to build anything yourself.

To learn concepts, you need to practice them and apply them independently in your work. This is a way to strengthen your understanding, as well – it's true Learning is done.

Imagine you've only ever seen them XGBoost Model following online tutorials.

If you are then offered a takeaway cake course as part of an interview, you will really struggle as you have no models to build without walking around.

What I encourage is “project-based learning.”

You want to learn enough, and quickly do the project.

Trust me, this method is much better than doing many tutorials (speaking from painful experience here!).

Quantity for Quality Projects

While making projects is the best way to learn, don't overload your github with loads of “Easy” projects.

If all your projects are compatible with pre-generated data from kagle and using Sci-Kit Read on .fit() and .predict() Ways, maybe it's time to try something harder.

Now, I'm not bashing these entry-level projects, as they are a great way to get your hands dirty.

However, sometimes, the quality of your projects will outweigh the price.

Big, deep projects will be what actually hires you. Employers don't want to see another Titanic data crisis; If anything, it would be a red flag these days.

Some ideas to try:

• Build ML algorithms from scratch using native Python.
• Reusing a research paper and trying to replicate the authors' results.
• Create a basic recommendation system for something personal in your life.
• Fine-Tune Allm.

This is not an exhaustive list, and the best project is your own, as I always say.

Jump straight to AI

I will be honest with you.

I am a hater.

No, I don't think it will replace a data scientist.

No, I don't think it's as good as people think.

And I sure as hell don't care about it all in the next 5 years.

The reasons I don't care could fill an entire video, so I'll leave it to you for next time. But it's actually funny, almost how worried I am about it.

However, the reason I say this is that it worries me when I see startups jumping straight into AI and LLMS.

This is a prime example of Shiny Object Syndrome.

As a start, focus on fundamentals of mathematics and statistics, and on classical algorithms such as decision trees, regression models, and support vector machines.

These are evergreen and will last for a long time, so it is wise to invest in them early.

AI is still an unknown business, and whether it will be popular and useful is a few years away.

If a topic is popular now and really helpful, it will be 1 year, 3 years, even a decade from now. So, don't worry, you have plenty of time to read the articles on the margins.

Remember what I said earlier about all the projects that hired you?

Long term, do they have deep ones that make a difference?

But what do these projects actually look like?

Yes, see my previous article, which goes through some projects that We help you stand out (and what a complete waste of time).

We'll see you there!