How to Become a Data Scientist

How does one become a data scientist?

Well, in truth, the path is most certainly clear. However, the work it takes to travel down the road is not for everyone. Before reading this you may want to have an understanding of where you are with your current analytic skills (e.g. MS Excel only, maybe a little bit of SQL, Crystal reports, etc). Use the rest of this article as a measuring stick for where you are and where you would like to go. In fact, it is best to begin with the end in mind and work backwards to the most basic skill you will need and start building from there…

Recently DataCamp posted an infographic which described 8 easy steps to become a data scientist.

How to become a data scientist

How to become a data scientist A portion of the infographic posted on the DataCamp blog

What is a Data Scientist

It’s important to understand what this infographic is based on:

  1. Drew Conway’s data science venn diagram that combines hacking skills, math and statistics knowledge and substantive expertise.
  2. A graph showing the survey results on the question of education level, not unlike the graph in O’Reilly’s Analyzing the Analyzers.
  3. Josh Wills’ quote on what is a data scientist.

Become a Data Scientist

Using the infographic, the 8 steps to becoming an data scientists are:

  1. You need to know (there is a spectrum here) stats and machine learning. The fix – take online courses for free.
  2. Learn to code (not everything, but very specific things). Get a book or take a class (online or offline). Popular languages are Python and R in the data science space.
  3. You should understand databases. This is important because for the most part this is where the data lives.
  4. Critical skills are data munging (data clean-up and transformations), visualization, and reporting.
  5. You will need to Biggie-Size your skills. Learn to use tools like Hadoop, MapReduce, and Spark.
  6. This part is extremely important – get experience. You should be meeting with other data scientists in meetups or talking with people in your office about what you are learning and accomplishing with your enhanced skills. Do yourself a favor obtain a data set online and start exploring them with your new found techniques. I recommend Kaggle and CrowdAnalytx for interesting data sets.
  7. Get yourself one of these: internship, bootcamp or a job. You can’t beat real experience.
  8. Know who the players are in this space and why. Follow them and engage with them, and be a part of and engage with the data science community.

My thoughts…

In my judgement, look at the data and the algorithms first then get busy with the math and programming. However, I do agree with the idea of moving steps 1-5 for familiarity sake of the discipline. Steps 6-7 I would categorize as working the problem and the final step would be plugging into a community.

It may be important to go another step forward. 

It is more intuitive to minimize steps 1-5 into one (this could be a crash course of terms and themes relevant to data science). My preference (its what has worked for me) is to jump in with the data and the tools of the trade as soon as possible. More need to develop just-in-time learning mechanisms, rather than learning the entire universe of a topic. Approaching data science in this way allows an individual to build on a combination of theory and practical experience. This done by encountering problem sets over and over again.

Learn the art of relevance…what makes sense for my situation right now. Obtain a solid data set and get learning. This sort of action works to build context for the tools you are using.

The fastest way to become a data scienist is to recognize where you are with you current skills, grab a data set, pick a language (R,Python, Julia, C++, Matlab,etc) and start working through a problem end-to-end.

What do you think it takes to be a data scientist?

 

10 Trends You Will Continue to See In 2014

Many businesses ask me what do you see happening in the next 12 months. They ask me question like:

What should we expect?

Where should we be investing?

What should we be thinking about to keep ahead of the curve?

The list below is not particular to anyone industry, rather a general overview of the state of the analytics ecosystem at a particular moment in time. For many industries, if they were to focus on a single item below it would perform wonders for their business, and yet others would need to adapt for more.

  1. Data science move to the every-man.
  2. Analytics will drive cloud-based business solutions.
  3. Cloud data warehouses transform the process from months to days.
  4. Business individuals began to have expectation of flexibility and usability in their dashboards.
  5. No longer is retrospective views of the data enough, so the addition of prospective views become important.
  6. Embedded analytics begins to to come into mainstream business.
  7. Dashboards with context become important, hence narrative around the data becomes key.
  8. Business users began to seek information wherever they are and not just at their desktop.
  9. Social media becomes a measures of competitive advantage for organizations.
  10. NoSQL will become increasingly more important as organizations attempt to work with unstructured data.

It is fabulous time to be involved in analytics and organizations of all types. We are at a new frontier of business that we should all be excited by rather than intimidated by.

The Death of the Data Scientist?

There has been a lot of chatter recently around the notion that data scientist are soon to be replaced by a 30/hr specialist from places like Odesk, Freelancer, and Elance.  Before we go down the path of can we replace a data scientist, let us take some time to hone in on exactly what a data scientist does? Being candid, there is a plethora of answers to this question.  If we mean, a person who pull together a data summary or modeling task that has been well-defined before they even encounter the problem, then I think it is absolutely possible to come in at a30/hr price. In truth, I see that time of data scientist being replaced by automated software without having to deal with a freelancer at all. Look to how other scenarios like this have occurred, such as online marketing or site development.

But we need to focus on the concept “the data problem was previously well-defined”.

Data scientist who achieve higher salaries happen to be in either two distinct camps:

1) The Engineer:

This individual knows how to choose the proper tools and infrastructure to solve a specific, technology laden data problem. These individuals usually work on the leading edge of a problem or at times there may be very few examples of this problem being worked in global community. This is markedly different than the well-defined problem of the freelancer situation we defined earlier.

2) The Communicator:

This individual knows the technical side of what data science is and how to get at solutions, but there strength is in the story telling. Many times business leadership is unknowing about what is possible with data science and for that they need a translator of sorts. These types of individuals encounter organizations that know they have a problem to solve, but they do not necessarily know how to frame the question so that it can be satisfied by the data. These business look for someone who is personable and not thousands of miles away to guide  them through what they feel is incredibly difficult and important.

While it is certainly true that there may segments of data science which are automated, there will certainly always be a place for problem solvers – think physicians, attorneys, developers, consultants, etc. Like these roles just mentioned, data scientist is not simple a role.

Not all data scientist are performing rote tasks.

There will always be a place for individuals skilled at solving leveraging technology to solve complex business problems and we will have to invest more than $30/hr to garner their expertise.