Northwestern Masters Of Data Science – In full disclosure, deciding to go back to school for another Masters happened to me after my wife started a business that kept her busy in the evenings. There I sat on the sofa, it was nothing for my wife, the young photographer, to learn Lightroom in the office and stream video from Netflix. I was a little tired when I sat on the sofa most evenings.
A colleague of mine was halfway through the Northwestern Masters program for Predictive Analytics and pushed me to consider it because he knew of my new romance with R and several Coursera classes. In the office, I was responsible for the team that priced OTC Energy Assets, and I explored how R or Python could be a more useful tool instead of traditional Quant-based languages like Matlab, VBA or C++. I also examined how some machine learning models have relatively strong abilities to predict the behavior of the OTC energy market.
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While I already had a hands-on background in statistics, quantitative finance, and computer modeling, I decided that the Northwestern program would help round out my knowledge, expose me to a deeper understanding, and ultimately provide a greater breadth of information. It was in 2012 that I started the Predictive Analytics program which eventually became their Data Science program. If you are considering such a program at a school, this article is for you!
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Northwestern’s program is clearly posted on their website, so I don’t need to rehash everything you can read there. Summarize; Expect to take about ten serious analytics courses (eg, some variation in math, coding, analytics, data visualization, and databases), a simple project management or leadership course, and either thesis or capstone courses. Your goal is a total of 12 courses; preferably in about three years OR, if you’re me, five years because you need a break and important work projects come up every now and then.
All courses follow a standard pattern; a weekly video/webshare session that is recorded if you don’t get time and a discussion topic about some of the week’s key concepts. Most courses have important assignments or tests every two weeks. In general, the professors keep the momentum going so that it can be dangerous to fall behind on a topic or concept. All classroom transactions utilize “Canvas” which is a reasonably good online learning and collaboration platform and far superior to what I remember the old Blackboard Masters systems being before me 15 years ago. All work is either done within Canvas, for example during discussions and tests (quizzes) or uploaded directly to Canvas when you submit an assignment or code. All your interactions with the professor and your grades are also in Canvas, which is useful for seeing how well you’re doing or not. Canvas outlines specific weekly deliverables such as reading assignments, special assignments, tests, and other information posted by the professor.
The key to success with each class? Get into the flow of approaching missions every week. For me, it was easiest to start on Sunday night by understanding what was coming next Sunday for the week in question. I would usually try to do the week’s reading work on Sunday evening and Monday evening, engage in the discussion forum on Wednesday and start on important assignments. Thursday was my night to do 90% of the important tasks either for that week or for long term projects. Friday drinking is reserved for a scotch – or three – and watching a movie with the wife (always make time for the wife!) An hour or two on Saturday wraps up everything that’s coming that week. As described, my flow as a professional, husband and father of two was where after 8 in the evening was the only time I had to do my chores. It worked well and generally meant I spent at least an hour each night Sunday to Thursday with some work on Saturday night. Some classes wanted more, but not more than double and too short shocks.
While this all sounds good and easy, given the online and self-paced approach of the program, it’s important not to fall behind. It will be these weeks, but don’t expect to slide through a week hoping to make it unless you know you’ll double your efforts the next week. Additionally, perspective can be a powerful thing for those of us who are already strong professionals with previous advanced degrees or in-demand skills. As my wife, a Ph.D. in developmental psychology, he added:
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Sometimes you have to cut corners or not do something at the level you want, and that’s okay – sometimes. I’ve never been asked about my GPA from past grades, and I’m sure no one will care about my 3.8 from Northwestern.
While in college 20 years ago, I walked into my mother’s office where she was the secretary to the director of state-supported job training. Although there were probably 15 people working in the office, only two were using Excel and I was the third. I was of immediate value to the office performing various audit tasks and budgeting automation work. Fast forward 20 years, I have hired over 25 interns and every single one of them knew Excel as it is now an expected commodity skill for anyone going into any business. A new trend is also happening, I now find interns from several bachelor’s programs at several universities who have experience in Python, R, and often Tableau either from courses or non-study.
The expected commodity skill of Excel will be replaced by Python, R or other analytical programming skills in the next ten years, will you be able to compete?
I do not make the above futuristic statement lightly. The current trends in big data management, analysis and machine learning push all knowledge workers to basic competence in that context. This means that current analysts, managers and executives using Excel-based paradigms must grow and learn new skills to compete. If you see yourself as a professional who wants to be a leader in your company or start your own, you will most likely need analysts with the skills you can learn from a program like Northwestern. It is also likely that you will need to manage people with these skills; What could be more effective leadership than getting the chops to do the job they do? Managers who can question the work of their teams, explain methods, expectations and finally be able to represent the analysis as something you understand and as a manager expect from every manager in the future
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What does all this mean to you? The program will expose you to multiple programming languages to manipulate, structure and generate machine learning models, mostly done in R and Python with a touch of SAS (for those working in government or companies with a large investment in SAS). You will dive deep into a variety of machine learning techniques/methods from traditional regression to neural networks. Finally, you will explore best practices for interpreting model results. You will also be able to explore various topics around databases, fast data and data visualization techniques. The most active part of the program is ‘output definitions’ where the majority of tasks required extensive work to explain the model’s methodology, explain the model’s implications, and most importantly, explain the model’s limitations. I found this to be the most important skill in the program. Being able to critically understand a model’s application and understand the results through such a lens helps ensure that I’m not on the wrong side of a bad model. There are a lot of bad models floating around because people want to prove themselves, to prove data science “stuff”, motivated only to succeed, resulting in managers who may be blind to the fact that business decisions from a bad model are worse than no model at all. .
My wife has now taken it upon herself to keep Aaron busy to ensure I don’t commit to another multi-year commitment! She supported my return to school and understood the value in the program with her background in statistics and analytics, but she also provided a strong perspective – ie no one has asked about my GPA in 15 years. At this point, I would do the program again with a stronger desire considering the growth of the field in the last five years. The value and credibility he brings to my professional career as a data product manager at CME Group (cmegroup.com) (a major derivatives exchange) is second to none. I have found that my approaches to product design and product creation focus on how data from customer interactions can lead to better products in the future. Modeling of customer requirements, price conditions and cost results can all be modeled based on other internal databases. Finally, the effect of this program created
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