# Top Resources for Learning Statistics for Data Science

Let’s take a look at the current state of statistics in data science, and what you can do to accelerate your learning.

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Statistics is at the heart of data science, and the link between the two fields keeps growing stronger. It’s important to have a deep understanding of statistical concepts if you want to progress far in your career in data science, and that foundation can take a while to build. Springboard’s Data Science Career Track is a great starting point, and it should be one of the first steps you take if you’re serious about building your skills in this area. Let’s take a look at the current state of statistics in data science, and what you can do to accelerate your learning.

**The Role of Statistics in Data Science**

Some people like to say that machine learning is simply statistics with additional layers, and while that may be an exaggeration, there is still some truth to the statement. And that extends to the general field of data science. Working with data, identifying patterns in it and predicting trends in future data sets, all boil down to applying various statistical techniques correctly. This is made easier with the help of modern digital solutions, which can churn through large volumes of data at a fast rate.

**Areas of Statistics to Focus on**

For most people, a good foundation in statistics will go a long way towards becoming a data scientist, even without digging deep into any particular subfield. You need to generally develop the ability to think like a statistician, and apply appropriate reasoning methods to the situations you encounter. This will help you much more than learning any specific tightly specialized fields of statistics.

What’s more, you need to develop the ability to understand what you’re doing, and how you’ve come to your conclusions. A large part of data science is a black box, especially when artificial intelligence is involved. This can make it deceptively easy to just apply a bunch of pre-made models to a situation, get some results, and call it a day.

But if you can’t explain your actual statistical reasoning that drove you to that point, you’re not going to be taken seriously by experts in the field. This will also help you understand the limits of the problems you encounter. You may not immediately know what the right solution is, but understanding the limits of the domain will at least help you steer your attention in the right direction.

**Implementing Some Basic Models**

There are some basic models that you should go through in your learning in any case, no matter what you’ve chosen to focus on in particular. Implementing those from scratch with your own resources and research will go a long way towards gaining a deeper understanding of how the field works, and how different concepts are connected in it.

You should start with basic linear regression. That’s one of the easiest concepts to understand in statistics and data science, and will open the doors to some additional areas that might interest you. Do a naive Bayes classifier. There are lots of resources on the subject online, and it should be another relatively easy task to go through, while still providing you with a lot of valuable knowledge about the fundamentals of statistics in data science.

**Useful Resources for Getting Started**

Online training courses are your best friend here. They can provide you with the in-depth knowledge you need in order to understand the concepts that follow. There are various places to learn statistics for data science, and you should take your time to compare some of the most popular courses out there.

Some useful books that can get you stated on the right track include “Practical Statistics for Data Scientists” by Peter Bruce and Andrew Bruce; “Bayesian Methods for Hackers” by Cameron Davidson-Pilon; and “Computer Age Statistical Inference” by Bradley Efron and Trevor Hastie. Those are easy to find and quite popular in the community, so you should have lots of opportunities to discuss what you’ve learned with others.

**Things to Keep in Mind**

The link between statistics and data science is strong, and you can’t get far in the latter field without putting in some effort in the former. At the same time, the level of understanding you’re going to need to progress far in data science isn’t as high as you might expect. For many people, going over the basic concepts that we outlined above, and completing a few training courses, should be more than enough to get them started and introduce the area in a comprehensive manner.

As we mentioned earlier, check out Springboard’s Data Science Career Track if you’re interested in pursuing a career in data science. It will give you one of the most in-depth overviews of the field you can find right now, and will help you understand the concepts you need to take your skills to the next level. Talk to as many people as you can, too – data science is going through lots of active developments right now, and it’s a great time to discuss recent findings with other specialists.