Voice of the DBA

Tag: data science

  • The March of AI

    Machine learning and AI systems are becoming more and more popular all the time. The constant onslaught of media articles and hype is pressuring more and more companies to experiment with AI systems. Whether these techniques work or not, no manager wants to be the one that has ignored the trend. A failed machine learning venture might be preferable to the lack of any project in the eyes of many in upper management.

    Our vendors press as well, after all, sales are on the line. I wasn’t sure how useful the R language would be inside SQL Server, but I am amazed at the effort and popularity of R among SQL Server professionals. R, and possibly Python soon, are becoming integrated into every product and tool from Microsoft, as well as other vendors. Every month I see more and more people experimenting and learning how to use R for a wider variety of tasks, from analyzing disk space and database performance to extrapolating business measures.

    Most of us aren’t skilled enough in math and statistics to really develop and build intelligent systems. We don’t have the background, or we haven’t worked in those areas for a long time. However, many of us can learn to work with ML and AI systems, managing the models, integrating the code others write into our applications. I do think there is value in learning a bit about these technologies, though I don’t think you need to become an expert. Some of you might want to be experts, and I wish you the best of luck on your journey.

    I do believe that AI will change the world. It’s going to become a larger and larger part of many systems and processes throughout the world. These intelligent systems will press and push humans in ways that we don’t expect and that we might not like. For us data professionals, we will be at the heart of many of these changes, with opportunities to grow our careers we might never had considered.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 3.0MB) podcast or subscribe to the feed at iTunes and Libsyn.

  • Becoming a Data Scientist

    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 east 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.

    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 data.

    Steve Jones

  • Data Science Education

    One of the good skills to have for a data professional is how to analyze data. Most of us could learn more about data science and data analysis for some aspect of our jobs. We are data professionals, so we should understand how to analyze data. I’d expect that a competent data professional would be able to put together a report on some set of data that means something to an end user. If we administer systems, then analyzing usage (index, space, etc) is a skill we need. If we write code, sooner or later we’re going to write some report for a client. Either way, we need to perform some sort of analysis.

    How can you learn more? There are lots of resources available. I thought it was interesting that Microsoft has teamed up with edX for their own data science degree. While there are mixed feelings on this, I think edX has a good platform and strong partnerships for teaching. There are other places, such as Coursera, that are doing the same thing, offering a variety of courses online.

    In fact, it appears that quite a few educational institutions and businesses are starting to increase their data science related offerings. There’s a good summary of some of the options in a piece from Dataversity. In response to all the demand, or at least perceived demand, there is everything from a boot camp getting you up to speed on some quick analytic techniques and tools to full degree programs. Some programs have different levels, depending on the amount of statistical knowledge you have.

    Learning something about statistics is probably the best way for most people that would like to get started. I’ve seen quite a few people start learning by reviewing some statistics techniques. From Buck to Mala, putting your own learning down in a blog will force you to ensure that you actually understand the principles. As for getting started, there are lots of great books you can buy, or read information online. One note, be careful and double check the information written on random blogs. Or check out places like the Khan Academy.

    In some sense this reminds me to the rush to get a CNE in the early 90s, or the many boot camps that appeared to help people a MCSE certification in the mid to late 90s. Lots of people passed the tests, but weren’t very qualified, which has contributed to the general distrust of certifications today. I hope that doesn’t happen too much today, though I’m sure it will to some extent. The chance to make more money, or just find a job, will drive lots of people to look for quick wins.

    While I’m sure some of you will get better jobs, perhaps making a lot of money with minimal data science experience, I hope many of you continue to learn and improve your skills in the data analysis area over time, whether you are paid to be a data scientist or not.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 3.5MB) podcast or subscribe to the feed at iTunes and Mevio .

  • Are you a Data Scientist?

    It seems that there’s no shortage of re-branding attempts being made in all industries and by all types of people. I still remember when most of us were called computer programmers instead of developers. Not many people writing C# or Java code would want to be called “programmers” today.

    One of the latest fads is the call for more data scientists to work on big data, another equally, poorly defined term. However it seems that he definition of what a data scientist is has been so ill defined that almost anyone that can write a query using aggregates might define themselves as a data scientist.

    A good thing if you are looking for a job. Many of you might find opportunities (and raises) if you convince a hiring manager that you are a data scientist. However I’d be wary of living on just the new brand without growing your skills. If your company comes to expect more, especially with regards to advanced statistical analysis, you might find yourself in a bind.

    I ran across a piece that looks at the skills that a data scientist might actually need. I don’t know how many managers might understand the difference between simple discrete rules engines and more subtle, complex, multi variable, adaptive algorithms, but there can be a big difference in how well the system actually performs for your company.

    No matter what you choose for your carer, I’d certainly encourage you to continue to learn more about how to work with data. Whether you want to learn more about statistics, pick up R, or improve your visualization skills. Keep Learning. Keep your brain active and work to improve before you find yourself without a job and in need of training. Every little bit you learn helps and the practice of continuous improvement builds a habit that will serve you well over time.

    Steve Jones

    The Voice of the DBA Podcast

    Listen to the MP3 Audio ( 2.7MB) podcast or subscribe to the feed at iTunes and LibSyn.