I have a business degree, but I’ve been interested in machine learning since my second year of university. And so, I sel

However, if you have some knowledge in calculus and integrals, I strongly recommend that you go through Georgia Tech’s course called “Statistical Methods”. It is a little more difficult as it goes through more proofs, but it will help you understand the intricacies of each idea.

Remember that this is a long-term goal and thus you should expect results in the long-term. If and when you’re willing to commit 100% of yourself, I would give it at least a year before you decide whether to continue or not.

You have to start with the basics, the building blocks, whatever you want to call it. But trust me when I say this, the better your fundamentals are, the smoother your data science journey will be.

Once you’ve learned the fundamentals, you’re ready to specialize. At this point, it’s up to you whether you want to focus on machine learning algorithms, deep learning, natural language processing, computer vision, etc…

Statistics and Probability: if you’ve read my previous articles then you’ve probably heard this for the MILLIONth time, but a data scientist is really just a modern statistician.

Kaggle is one of the world’s largest data science communities with hundreds of datasets that you can choose from. With Kaggle, you can compete in competitions or simply take advantage of the datasets available to create your own machine learning models.

Pandas practice problems: this resource is a repository full of practice problems specifically for Pandas. By completing these practice problems, you’ll know how to: filter and sort data, aggregate data, use .apply() to manipulate data and more.

Linear Algebra is especially important if you want to get into deep learning, but even then, it’s good to know for other fundamental machine learning concepts, like principal component analysis and recommendation systems. Surprise surprise, you can guess what course I recommend for this. The link is provided here.

Leetcode is a great resource that has helped me learn skills and neat tricks that I never thought was possible. It’s something that I leveraged significantly while job searching and it is a resource that I will always go back to. The best part of it is that they normally have recommended solutions and discussion boards, so you can learn about more efficient solutions and techniques.

Learning data science is one thing, but something that people commonly forget is marketing themselves — you’ll eventually want to show what you’ve learned. This is especially important for you if you don’t have a degree related to data science.

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rdner, writers all employ the same trick: creating a dream and putting it in the reader’s mind. Bad writing pulls the reader out of the dream and makes them aware that they are reading. Gardner identifies these seven common forms of clumsy writing in his book The Art of Fiction:

Not too long ago, I wrote an article, “How I’d Learn Data Science if I Could Start Over.” In this article, I segmented what to learn by subject, i.e. statistics