Category: Northwestern University MSPA

Becoming a Healthcare Data Scientist, Data Science, Data Scientist, Data Visualization, Northwestern University MSPA, Predictive Analytics

Northwestern University MSPA 402, Intro to Predictive Analytics Review

Posted on by ERDataDoc

Summing this course up in one word = WOW.  This course should be taken early on because it is extremely motivating, and will help motivate  you to get through the other beginning courses such as Math for Modelers and Stats.  This course is a high level overview of why and how analytics should be performed.  It describes not only predictive analytics but the whole analytics spectrum and what it means to be an “analytical competitor”.  While you do not perform any actual analytics, you will understand why getting good at this is so important.

I took this course from Dr. Gordon Swartz, and highly recommend him.  Interestingly, he has bachelor degrees in nuclear engineering and political science from MIT, an  MBA from Northeastern University and a doctorate in business administration from Harvard.  His sync sessions were very informative and practical, and he provided on-going commentary in the discussion boards.

The course description is –  “This course introduces the field of predictive analytics, which combines business strategy, information technology, and modeling methods. The course reviews the benefits and opportunities of data science, organizational and implementation issues, ethical, regulatory, and compliance issues. It discusses business problems and solutions regarding traditional and contemporary data management systems and the selection of appropriate tools for data collection and analysis. It reviews approaches to business research, sampling, and survey design.”

The course is structured around required textbook reading, assigned articles, assigned videos, weekly discussions, one movie (Moneyball) and 4 projects.

Readings

The reading requirements are daunting, but doable.  You will (should) read 6 books in 10 weeks – a total of 1,590 pages.  There are 14 articles to read.  Each week has a short video as well.

These are the assigned books.  At first glance, this list will not seem to be a little odd with seemingly unrelated books.  However, they all help create the overall picture of analytics, and are all valuable.  I will provide just a brief overview of each, and plan to post more in-depth reviews of them later this summer.

Davenport TH, Harris JG.  2007. Competing on Analytics:  The New Science of Winning.  Boston Massachusetts: Harvard Business School Publishing.

This is the first text you read, for good reason.  It provides the backbone for the course.  You will learn about what it means to be an analytical competitor, how to evaluate an organizations analytical maturity, and then how to build an analytical capability.

Siegel E.  2013.  Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die.  Hoboken New Jersey: John H Wiley and Sons, Inc.

This is a must read for anyone going into predictive analytics, by one of the pioneers of this field.  It describes in detail what predictive analytics is, and gives numerous real life examples of organizations using these predictive models.

Few S.  2013.  Information Dashboard Design: Displaying data for at-a-glance monitoring.  Burlingame California: Analytics Press.

I will admit that when I first got this book I was very confused about why it was being included in a course on predictive analytics.  However, this turned out to be one of the best reads of the course.  For anyone who is in analytics and has to display information, especially in a dashboard format,  this is a must read.  This describes what dashboards are really for, and the science behind creating effective dashboards.  You will never look at a dashboard the same way in the future, and you will be critical of most commercially developed dashboards, as they are more about displaying flashiness and fancy bells and whistles rather than the functional display of pertinent data in the most effective format.  I can’t say enough good things about this book, a classic.

Laursen GHN, Thorlund J.  2010.  Business Analytics for Managers: Taking Business Intelligence Beyond Reporting.  Hoboken New Jersey: John H Wiley and Sons, Inc.

This is a great overview of business analytics.  This is especially valuable in it’s explanation of how the analytics needs to support the strategy of the organization.

Franks B.  2012.  Taming the Big Data Tidal Wave: Finding opportunities in huge data streams with advanced analytics.  Hoboken New Jersey: John H Wiley and Sons, Inc.

This was an  optional read, but I recommend reading it.  It is written in a very understandable way, and provides a great overview of the big data analytics ecosystem.

Groves RM, Fowler FJ, Couper MP, Lepkowski JM, Singer E, Tourangeau R.  2009.  Survey Methodology.  Hoboken New Jersey: John H Wiley and Sons, Inc.

I will admit this was my least favorite book, but having said that, I learned a ton from it.  For anyone who will even think about using survey’s to collect data, this is a must read. However the 419 pages make this a chore.  It would be nice to have an abridged version.  What it does, though, is wake  you up to how complex the process of creating, deploying, and analyzing surveys is.  I grudgingly admit this was a valuable read.

Articles

There are some really great articles included in the reading list.

Videos

There are videos that were developed by another professor that review the weeks material.  I did not find these especially helpful, but they did provide an overview of the weeks information, and might be  helpful if you are having some trouble understanding the material.

Weekly Discussions

Again, the weekly discussion are where it happens.  There are one or more topics that are posted.  There are usually some really great comments posted, and you can gain a lot of insight if you actually think about what you are posting, and what other people have posted.  If you post on the last day a brief paragraph, then you are missing out on some valuable information.

Moneyball

The first course I have taken where a movie was required.  There are discussions around this movie and one of the assignments involves creating an analysis of the Oakland A’s and how they used analytics.  I enjoyed the movie and thinking about this.

Assignments

There are four assignments where you must create a paper of varying lengths.  You must create this using the appropriate APA format, so it is useful for refining these skills.

I found these to be challenging, fun, motivating, and extremely enlightening.  These called for the application of what we learned to some real world situations.  For one of these, I performed an in-depth analysis of our organizations analytics which involved interviewing our senior leadership.  As a result of these interviews, it really started the process of moving our organization to the next analytical maturity level in a very meaningful way.

Another project involved the creation of a draft dashboard using the best practices outlined by Stephen Few in his text.  This was a great learning experience for me, and one that will translate into much better dashboards at our organization.

The last project involved creating a meaningful and valid survey.  This was informative as well, and I actually might send out my survey.

Summary

Overall, this was a fantastic course.  This will make it clear why we need to do this well, and what doing this well looks like.  After this, the actual work of understanding and developing predictive models begins.  Again, I feel as if got my money’s worth (not an easy thing to say since these courses are pricey!).

Summer Activities

I am taking the summer off and am trying to catch up on the projects that have been piling up.  For fun I am learning SQL (great book – Head First SQL by Lynn Beighley) and working my way through several Python Udemy courses.  I will be attending the SciPy 2016 Conference in Austin Texas in July as well, and am super excited about this. I will be going to tutorials on Network Science, Data Science is software, Time Series analysis and Pandas. If you are attending, give me a shout out.

 

 

 

 

 

 

 

 

 

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Data Science, Northwestern University MSPA, Predictive Analytics

Northwestern University MSPA 401, Introduction to Statistics Review

Posted on by ERDataDoc

I finished this course last week, and thought I would post my thoughts before I forget them.

I was in Professor Roy Sanford’s section, and I HIGHLY recommend him.  He is an extremely experienced practitioner, and very knowledgeable of statistics and in using R for statistical analysis.

The course is focused on several aspects – learning basic statistics, learning R to perform statistical analysis, and engaging the students to participate in discussions that are pertinent to the material being learned.

Learning Statistics

The core text for the course is Ken Black’s Business Statistics For Contemporary Decision Making, 8th Edition.  It is a loose leaf binder text so you can remove the sections you are studying, which makes it nice.  It is a very down to earth text, with plenty of examples and problems.  Their is a companion website called WileyPlus that has videos to watch and a variety of problems/exercises.

A second supplemental statistical text is Rand R. Wilcox’s Basic Statistics: Understanding Conventional Methods and Modern Insights.  There are selected readings which highlight some contemporary issues.  Not as easy to read as Black’s text, but still informative.

Learning R

The coursework is presented using R.  You don’t HAVE to learn to use R, but you would be an idiot not to take advantage of this opportunity.  There is a great deal of effort putting into devising the curriculum to help you learn R.   This is well thought out, and I feel very confident that I have obtained a good working knowledge of R on which to build.  I was astounded to read a comment on the LinkedIn group – Networking Group for Northwestern University’s MS in Predictive Analytics Program –  from a previous student who took this course, who commented he didn’t really learn any R because he didn’t do any of the R reading or assignments.  To me, learning R was just as important as learning the statistics.  Plus I don’t know how you could do the Data Analysis Projects without learning R. Learning R is accomplished through reading various text’s, watching weekly video’s on R produced by Prof. Sanford, and then doing exercises.  Plus there are R resources and lessons, including links to Lynda.com.

I did the work in both RStudio and in a Jupyter Notebook using the R kernel. The Jupyter Notebook was my favorite way of doing the assignments because I could refer back to them.  But some things are way easier to do in RStudio, like installing packages and data sets, so sometimes I switched between the two.  See my other blog posts for information about Jupyter Notebooks.

The first R text is Winston Chang’s R Graphics Cookbook.  This takes you through the R basics and gets you up to speed quickly visualizing data.  There is a little bit about using the base plotting function in R, but most of the book is about visualizing using the ggplot2 package.  If you follow the exercises, you will get good at plotting and visualizing data.  You will learn scatter plots, line graphs, bar graphs, histograms, box plots (a lot – I finally understand what to do with a box plot), functions, QQ plots (I finally understand these as well).  All of these are extremely helpful in what you will spend a lot of time learning, Exploratory Data Analysis (EDA).

The second R text is Jared P. Lander’s R for Everyone: Advanced Analytics and Graphics.  This dives more deeply into using R for things other than data visualization and graphics, although it includes this as well.  This is a very easy to read and follow text.

The third R text is John Verzani’s Using R for Introductory Statistics: 2nd Edition.  This book is a very deep dive into R’s capability to do statistical analysis.  Although very detailed, it is understandable with great examples.

The last R text is downloadable from the site, Sarah Stowell’s Using R for Statistics.  This is also a very practical book on both statistics and visualization.

Don’t be overwhelmed by the number of text’s and reading, it is doable, and I would do it all.  If you do that, you will not be able to say you did not get your money’s worth.

In addition there are beginning videos and lessons about learning R, including links to Lynda.com.   There are weekly Calculations with R assignment, which include a video with examples.  There are exercises with these weekly assignments as well.  Finally there are R lessons which take you through learning R in an organized manner.

Sync Sessions and Videos

Professor Sanford holds a sync session every other week.  These are extremely informative and helpful.  You don’t have to watch live, but you need to watch later.  The sync sessions in Predict 400 were optional and you could get by fine without watching them.  Not the case here.  You will learn a lot from these.

The same holds for the videos he has created to go along with the weekly R exercises.  These are must watch videos.

Data Analysis Projects

There are two data analysis projects.  You will learn how to apply what you are learning to a hypothetical data analysis project.  These are pretty challenging, but VERY worthwhile.  These show the applied focus of the MSPA program, and I found them beneficial.  The first one really focused on doing some exploratory data analysis.  The second one was twice as long as the first, and you applied what you learned later in the course, including the creation of a linear regression model.  You will definitely want to start early on these, and put in the effort to do these correctly, as together they constitute 2/5’s of your grade.

Bi-weekly Tests

There are 4 bi-weekly tests which are very fair and doable.  Together they constitute 1/5 of your grade.

Final Exam

The final exam is also very fair and doable.  Much easier if you have paid attention to learning R, as you can use R to do the exam.  This is 1/5 of your grade.

Communications and Discussions

There are Communications discussion sections set up for statistics and R.  You can post a question anytime in either and get a rapid response from either Prof. Sandford or the R TA.  Our R TA was Todd Peterson, and he was extremely knowledgeable, helpful, and responsive.

Every week there are two discussions around topics you are learning.  These are student driven, and if taken seriously, you can learn a lot from each other.  There are some extremely bright and talented students in these classes who have great real world experience in a variety of sectors.   The final discussion section is a recap of what you learned that week, and Prof. Sanford participates in that discussion.

Overall

I spent between 20-30 hours per week doing the coursework.  You wouldn’t have to spend that much time, especially if this material is not new for you.  But I wanted to really learn the material, not just pass the class.

I really enjoyed this course on many fronts.  I found learning about statistics and R together was very complementary.  In fact, I cannot imagine doing any kind of statistical analysis without using a language such as R.  I am now trying to recreate what I learned in R using Python.  I really feel as if I got my money’s worth.

 

 

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Becoming a Healthcare Data Scientist, Northwestern University MSPA

Northwestern University MSPA 400 Math for Modelers course, final thoughts

Posted on by ERDataDoc

 

 

I had previously posted my interim thoughts on this course, and now that the course is finished, thought I would add my final thoughts.

The final examination was fair and a mixture of the math and Python.  You could certainly pass the course if you didn’t keep up with the Python, and do the exercises, but would be much more difficult.

The last section of the course was on calculus.  Weeks 6 and 7 were devoted to a review of differential calculus and weeks 8 and 9 were devoted to integral calculus.   Dr. Goldfeder continued to stress the real world application of the concepts learned.

We had a week off over the Thanksgiving holiday, which allowed us to catch up and review before the final examination.  I took this time to both review the math (a little), and review Python (a lot).  I went back through each weeks Python assignments to make sure I understood the concepts and could work through the code.  I HIGHLY recommend this.  Looking back I wished I would have spent more independent time applying Python and writing code to do the problems as much as possible as we were going through the course.  I encourage future students in this class to attempt to do this.

After the class ended I started catching up on my to do list, which included how to use Jupyter Notebooks.   After doing more exploring of the Jupyter Notebook, I wished I would have found them earlier.  They are very useful for learning code, and taking notes at the same time.  I would encourage students to look at these when they start this course.  I wish Northwestern University would do what several other universities have done, and that is start teaching the class using these notebooks.  This would be extremely useful for the Python part of the course.  I have now been brushing up on R using the same Jupyter Notebooks, with an R kernel installed.  I plan on using this notebook as I go through my next class, statistical analysis, which uses R.  Here is the link to Project Jupyter’s webpage.

My overall assessment of the math for modelers course is highly positive, and I feel as if I learned what I set out to learn, and got my money’s worth.  It is a very demanding class time wise, but for those interested in analytics, this is a foundational set of knowledge that must be learned.

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Becoming a Healthcare Data Scientist, Northwestern University MSPA, Predictive Analytics

Interim Review of Northwestern University’s MSPA Math for Modelers course.

Posted on by ERDataDoc

Predict 400, Math for Modelers Course, Northwestern University MSPA

I am going to summarize my experience to date with Northwestern University’s Master of Science in Predictive Analytics program. I am past the halfway point (week 7 of 9) of my first trimester in this program. I am enrolled in one course, Predict 400, Math for Modelers. This is being taught by Professor Philip Goldfeder.

I will first describe the outline of how the course works. This is an asynchronous learning experience, for the most part. We have had one live session with Prof. Goldfeder. The coursework is presented through the online platform called Canvas. There are three main components to the class, which I will describe in greater detail below. The first component is learning the actual math. The second is participating in discussions about questions posed each week by Prof. Goldfeder. The third is learning Python.

What I really love about this program is how it brings together the book work, homework, learning python, and getting help for problems/questions, into one place. I have been trying to informally do this on my own, and it was frustrating for me to try and learn math/machine learning/etc. using either books or other online courses, learn python a separate way, and then have difficulty getting my questions answered. It is a 1000% easier when this is all rolled into one. Even though this is a lot more expensive than doing it on your own, to me it is worth every penny.

Professor Philip Goldfeder.  He is a great Professor for this course. He received great reviews in the CTEC (Course Teacher and Evaluation Council, these are visible when signing up for classes) and I see why. Not only is he extremely knowledgeable, he is also very engaged with the students and seems genuinely interested in making sure we learn and understand the material. He is also great at challenging the students to think of ways to apply the concepts learned to real world examples. I highly recommend him.

Canvas platform. This is where you go to do everything. It has sections for Announcements, Syllabus, Modules (describe each week’s assignments and is where you download things), Grades, People (section where everyone gets to describe themselves and you get to know your classmates), and Discussions.

The math. The introductory course, Math for Modelers is designed to be a “Review of fundamental concepts from calculus, linear algebra, and probability with a focus upon applications in statistics and predictive modeling. The topics covered will include systems of linear equations and matrices, linear programming, concepts of probability underlying both classical and Bayesian statistics, differential calculus and integration.” This is a very aggressive review of linear algebra, probability, differential calculus and integral calculus. This would be easier for someone who has taken these courses recently, but is challenging for me since it has been decades since I learned this (not sure I really learned some of this the first time around). You are assigned 1-2 chapters a week from the textbook “ Lial, Greenwell and Ritchey (2012). Student Solutions Manual for Finite Mathematics and Calculus with Applications, 9th Ed.”  Prof. Goldfeder prepares a high level video that reviews the material in the chapters. He also posts PowerPoint presentations of the material in each chapter.

Homework. You are then required to complete a homework assignment each week, which covers the material in the chapters. This is typically 20-30 questions. This is completed through the Pearson educational application. This is a FANTASTIC resource. The textbook is online here. Each chapter has it’s own section, and you can do problems in each sub-chapter of each chapter. If you struggle with the solution, you can actually have the application walk you step by step through the problem, and show you similar problems. There are links out to the textbook that takes you right to the section dealing with the problem you are working on. There are also videos available to view on the topic as well. I almost always do all of the study problems. The homework is another section in here, and that is how you submit your homework. Homework is worth 25% of your grade.

Discussions. This is a surprisingly difficult section. The NU MSPA program is designed as an applied program, and designed to use real world examples and learning. To that end, Dr. Goldfeder challenges us each week to come up with real world examples or explanations of the material we are learning. To formulate a response to this can take a surprising amount of time if you take it seriously, but in doing so I have learned a lot. The process makes you think about how these concepts could be used in the real world. You are supposed to post your discussion response by the middle of the week so that you can participate in the discussions about what you posted, as well as what your classmates posted. The kicker is that you can’t see what other students have posted, until you post your submission. I have learned a lot from these discussions. The other students in the course have such a wide background, that they can weigh on the topics in a meaningful way. We have students with backgrounds in sports analytics, actuaries, people working in industry, medicine, computer science, etc. The discussions are worth 25% of your grade.

Python. This could be extremely challenging if you have not had any exposure to Python or programming. I knew this would be a challenge, so I did take a few Python courses (Codecademy’s Python course at http://www.codecademy.com/learn/python, How to think like a Computer Scientist at interactivepython.org) prior to enrolling in the class. However, I would label myself still a beginner in Python, and the exercises challenged me to expand my knowledge of Python. However, I personally think this is one of the most gratifying portions of the course. I really enjoy combining what we are learning with Python. We cover the basics of Python, creating graphs and plots, using NumPy and SciPy. I love this part of the course. This is done through the Enthought Canopy platform. This has the interactive editor, the package manager, and of great value, the “Training on demand”, which is a very comprehensive series of instructional videos. These cover basic and advanced functionalities. Well worth the money, just for access to these videos. There is no grade each week for the Python assignments, however, you need to keep up with these. There were questions on the midterm that specifically required the use of Python to analyze the question and display the results. We have a Python TA assigned to the class who is very responsive to questions. In addition, students post code and help provide input on any questions.

Tests. The midterm is worth 25% of the grade as is the final examination. The midterm was a take home test, and required a substantial investment of time to complete. In addition there was the regular homework/reading for that week, although the discussion that week was optional. This is a week when you would want to cut yourself some slack and allow extra time. I had a heavy work week that week, and regretted not thinking about this ahead of time to give myself a lighter work schedule.

Time requirement. I am finding that I am devoting 20-30 hours per week to do all of this. You could devote less time if you were more up to date on the math or Python. But remember, I am doing this to learn and retain the information. So I am doing all of the reading in the textbook, doing all of the example problems and “your turn problems”, and almost all of the chapter problems in the Pearson application. I have not had time to do all the problems in back of the textbook however. I also try to provide meaningful input into the discussions, both in my submission, and commenting on what other students have posted. I have also been trying to continue to dive deeper into learning Python.

Typical week. I usually try to do the textbook reading on Monday and Tuesday. (All of the assignments are due midnight Sunday night, so Monday starts a new week). I don’t do a lot of problems initially as I want to get through the reading, so I can apply it to my discussion. Then on Wednesday I like to start working on my discussion submission and try to get it in by Wednesday, or Thursday at the latest. That way I can participate in the discussions in a meaningful way. After I get my discussion submitted, I go back and work through the chapter problems in Pearson. I like to get to the homework section on Saturday. Ideally I like to have Sunday to do the Python reading and assignments.

My overall assessment of this course is that I am extremely satisfied. I think this is very professionally done, I am learning the math, I am being challenged to think about applying this to the real world, and I am learning Python. There is definitely a lot going on, but that is why I signed up for this. I feel as if I am getting my money’s worth.

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