You have to start with the basics, the building blocks, whatever you want to call it. But trust me when I say this, the

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.,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.,The reason why we’re all so afraid of being vulnerable is because it’s risky. Yes, it can lead to heartbreak. It can put you in a position where you might be rejected, and it can certainly feel uncomfortable.,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.,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…,First, leverage your resume to showcase your data science projects. I recommend creating a section called “Personal Projects” where you can list two to three projects that you’ve completed.,Programming: Just as having a basic understanding of math and stats is important, knowing the core fundamentals in programming will make your life much easier, especially when it comes to implementation. Therefore, I recommend that you take the time to learn basic SQL and Python before diving into machine learning algorithms.,Common sense tells us we can’t stay up half the night and then also have the energy to exercise hard and prepare healthy meals. Poor sleep quality and increased risk of being overweight are linked and have been for quite some time. It’s now listed as a risk factor for obesity, along with a lack of exercise and overeating.,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.,I strongly recommend that you create a Github repository if you haven’t already. And while we’re on the topic of Github, it would be a good idea to learn Git. Here, you can include all of your data science projects, and more importantly, you can share your code with others to see.,Mathematics: Depending on how much attention you paid in high school will determine how much time you need to spend learning basic mathematics. There are three areas that you should learn: calculus, integrals, and linear algebra:,If you want to learn more about machine learning algorithms and implementation, I would check out Kaggle’s Intro to Machine Learning, Stanford’s course on Machine Learning, or Machine Learning A-Z on Udemy. Check them out and see what suits your preferences best!,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.,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.,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.