I'm from the Philippines and have the end goal of being able to use big data/data science in marketing and/or creating a predictive analytics tool. Which master's degree should I get?

• Here are my choices and reasons why: Techno MBA Pro: Kinda like hitting two birds with one stone, MBA and Tech. Con: The program lacks focus on technical skills, it's more like learning them in general enough to be an all-arounder. MS in Statistics Pro: I believe data science, big data, and predictive analytics requires technical skills, plus I'm interested. Con: Not that good in statistics. Track deviation from business degree. MS in Applied Economics Pro: Still in line with my business degree. Feels like this one makes the most sense since, to a degree, analytics is like applied economics. (Feel free to contradict or support me on this view) Con: It still requires statistical skills and the degree is relatively a no-name in our country. MS in CS Pro: I feel like it will allow me an easier entry towards the field, plus it's the booming industry in our country. Con: Track deviation, insufficient knowledge regarding CS, and basic programming skills. Of course, other suggestions are welcome, including, but not limited to, MOOC courses and Post-Baccalaureate programs. But please bear in mind that most companies in the Philippines still prefer a degree over skills and experience.

• Answer:

  Interesting question. If I'm understanding the basic premise: Your feeling is that hiring people in the Philippines value educational credentials more than ability or work experience. You want to add credentials to your resume in order to be more desirable. Your existing degree is in business. You want to work in marketing, and utilize data science as a differentiator or build a software tool. If these premises are valid, there's two recommendations for me to make. If you want to do data-driven marketing: get a degree in Applied Statistics Justification: Data Science is at its heart statistics. If you want to do truly powerful things with data and information to inform your marketing work, you must develop an expertise in statistics. You already have the business credential. You don't need another to do work in business, but you'll look like a rock star with a STEM credential here. If your goal is to do things that most of your coworkers can't, you'll be doing novel analyses and building predictive models. You'll build those models so they can learn and get better when you give them new information. You can do some amazing things with data and statistics, and I think it'd be very much worth your time. I would look for a degree in Applied Statistics because it will be more holistic than a straight-math degree. You'll have that "pure" math foundation and experience applying it to a variety of fields. I recommend this above applied economics because the economics degree will be economies-focused (discussed more below). Preparation: Find MOOCs and online resources to develop a foundational understanding of statistic, if you don't currently have it. Find a program that is aimed at "career change" instead of "lane specialization", and it will be a little more forgiving at the start while still providing you a deep-dive into stats. If you want to build tools: get a degree in Computer Science Justification: This one is all about the credential. Normally, I'd recommend self-teaching a gateway-language (python probably in this case), building some tools, using available MOOCs for fill-in support and actually building tools. There is no better education in software development than actual experience. However... pursuing a degree in CS you'll both build experience and will earn that credential. Both are wins. Take one or two stat classes as part of your electives and you'll have credentialed foundation enough to apply statistical methods in computing engines that you build for your analytical tools. One caution: there is a particular mindset that is conducive to software development. Not everyone has that mindset, though it can be developed. You need to be the type of person that, when faced with a problem, will Establish a core understanding of the problem. Ask questions until that core understanding matches expectations and reality. Discover patterns in the problem. Design solutions that address those patterns. Search and be inquisitive when you don't know how to build part of the solution. Preparation: Go to a site like http://codecademy.com and work through both a language as well as a few "build your own" tutorials. Build a web site, build a simple web application. The goal: test if you have the mindset for software development. If you find the tutorials exceedingly difficult or if things really don't make sense, try a different site. If find a pattern developing, consider if you really want to develop tools, or if your desire to build them lies in the business/selling/success part of the idea. Find a program that, again, will be focused on professionals adding a new skill set rather than a hardcore specialization degree. Reasons to not select the others: Techno MBA: A master's of business administration trying to double as an information systems degree. This will be far too general to be of use for your specific desires, and will not be a good ROI unless your goal is actually to run businesses (at which point you need to reassess what you're actually asking here). Generally the MBA is focused on case study analysis and development, discussing theory and examples of business principles applied in real-life situations. the tech-focused MBA will look more closely at IT and technology principles as they're applied, but you're not going to get enough time learning the skills you need for your goals. Applied Economics: This one is murky. Some programs will be more stats, some will be more economic theory. What you should be looking for is statistical chops, because as you go, an Applied Stats degree will provide your more flexible future opportunities than Applied Econ. I think of Stats vs. Econ like Physics vs. Engineering -- the physicist can generally teach herself an engineering discipline quicker than an engineer can teach herself a new discipline, because the former is trained in the science of it and the engineer trained in the application of science. Econ will still require learning statistics, but it'll be focused in the specific application of that to economies, instead of from a mindset that looks at data agnostically. This is the reason above I recommend an applied stats degree, as you'll have that "pure" math foundation and experience applying it to a variety of fields. Final thought: My throwaway caveat at the end is that I'm neither a statistician nor an economist, so my view of those two fields may be imprecise. If I've misinterpreted something, I'd be glad to discuss it in the comments.