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Becoming a Data Scientist

By Steve Jones,

Data Science is hot. There are lots of companies excited by using machine learning and AI to enhance their applications. There are new jobs, some of them well paying, and certainly not enough people to fill them. In many ways this reminds me of previous "hot" areas, such as Novell Networking in the late 80s/early 90s. Companies wanted new CNEs and paid dearly for them. The same thing happened in the mid 90s with MCSE's for Microsoft networks. Many of the people hired weren't remotely qualified, having just completed some multi-week boot camp.

You could go to school. If you have completed college, there are a list of data science graduate programs that you could choose from and pursue a masters degree. There's even a blog where someone is documenting their masters degree path to becoming a data scientist. This isn't a quick or easy path, but it is one way to gain data science skills.

If you don't want to spend the time or expense of a formal college program, Microsoft has a data science curriculum on the EdX platform that you can complete. These are low cost programs that you can complete to get a certificate. The value of that certificate is debatable, but the same could be said for any program. A few people that are working through this program have found it to be a good set of resources that is teaching them valuable skills. Again, this isn't easy or necessarily quick, but it does put you on a path to understand more about the topic and decide if it is something you find enjoyable.

There are other options, no shortage of books, blogs, and other resources on data science and data analysis techniques. It's up to you, however, to learn what you need to know and become competent at a level that is useful for some organization to pay you. I dislike people choosing to study a topic for a job, so I would say that if you wish to go down this path, do so because you enjoy the work and find it interesting. Build some skills, build a portfolio of data science projects, and best of luck.

Our industry has thrived for a long time on simple analysis, and I think there will be jobs in this area for some time to come. I do expect that better looking reports and dashboards are going to be expected rather than simple tables, so I'd suggest everyone work on their visualization and report polishing skills. I also think that more complex data science techniques will be in demand, though I wouldn't expect job growth here that overwhelms current jobs. Tackle data science if you like, but be aware this isn't a simple or easy chore. There are lots of math and statistics involved and it looks like this is more science than just reporting on data.

 
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