Matthew Renze

Getting Started with Data Science

How to choose the right path on the road to learning data science.

Author: Matthew Renze
Posted: 2018-08-15

Getting Started with Data Science

The question I get asked most frequently these days is "How do I get started with data science". This question come not just from software developers but also managers and even C-level executives.

Unfortunately, there is no one-size-fits-all answer. So, I usually have to ask several qualifying questions to determine each individual's background, motivations, and constraints. However, over the years, I've noticed some pretty clear patterns emerge from my advice.

So, if you're interested in getting started with data science, here are a few general paths that you can take. All three of these paths are listed in order, from the smallest commitment of time, money, and effort, to the largest.

Online Courses

There are hundreds of online courses available these days. Many are excellent courses taught by highly qualified instructors. However, there are also a lot of low-quality courses, simply trying to cash in on the latest IT trends.

While it's hard to say which courses would be best for each of you, my recommendations generally fall into three categories, based on a person's background and motivations:

Data Camp - If you're new to programming and you'd like to take things at a gradual pace, I recommend Data Camp. They use interactive exercises to walk you through all of the basic concepts. While this is a great approach for novices, more experienced IT professionals, however, might find this pace too slow.

Pluralsight - If you're an experienced IT professional, I recommend Pluralsight. This option is best for those interested in quickly learning practical skills that you can apply at your job immediately. To help you get started, I created a course called Data Science: The Big Picturethat covers all of the basic topics.

Coursera - If you want to go much deeper into data science, I recommend Coursera's Data Science Specialization through Johns Hopkins. This 10-course series is taught by some of the top professors in the academia. However, this level of depth and mathematical background is definitely not for everyone.

On-site Training

If you have a team (or are part of a team) that wants to learn how to leverage data science, you might find on-site training to be a better fit. With on-site training, you can quickly get an entire group of people up to speed fast.

On-site training is generally done as full-day or multi-day workshops for groups of between 10-50 people. The main advantage of on-site training is that you have a live instructor, which helps keep you engaged. In addition, you can ask the instructor questions and get immediate feedback.

The main downside to on-site training is that it costs more than online training. You need to have enough people to make this option cost effective. In addition, the faster pace of learning may actually be a disadvantage for those on your team that prefer a more gradual learning pace.

I regularly provide this type of on-site training for start-ups to Fortune 100 companies. This includes companies like Microsoft and their key clients. In addition, you can also find these same workshops at various conference that I speak at around the world. You can learn more on my workshops page.

College Instruction

If you want to go really deep into data science, I recommend investing in a degree from a reputable university. However, you need to have sufficient time, money, and motivation to do so. While knowing the basics can get you quite far in your career, being an expert can take you to the top of your profession.

All of the top data scientists in the industry generally have a Masters or a Ph.D. in data science, computer science, or statistics. In addition, if you're going to be using data science in ways that could have significant financial implications or affect health and safety, this is the recommended way to go.

From my own personal experience, it's really easy to introduce errors or bias into the data science process. Having a deep understanding of the scientific method and statistics will help protect you from these errors in reasoning before you commit them.

Now Get Started!

Hopefully this is enough information to get you started down the right path. Once you get through the basics, you should have enough information to decide where to dig deeper.

However, if you're still not sure where to begin, I recommend starting with my introductory course Data Science: The Big Picture. It will walk you through the entire data science space from a very high level in a simple language anyone can understand.

In addition, feel free to contact me if you have any additional questions.

Share this article:  Share on Facebook Share on Twitter Share on LinkedIn Share on Google+