Learning R, Jupyter Notebooks, and KNIME

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I currently have about a month off between my last course Math for Modelers, and my next course, Statistical Analysis in Northwestern University’s Master of Science in Predictive Analytics program.  I have started to catch up on some things on my to do list.

The first of these tasks is getting to know R better.  I have a little bit of previous R experience, but at a very, very basic level.  I am currently working my way through DataCamp‘s excellent series of R programming courses.  I completed the “Introduction to R” course which consisted of 6 chapters covering the basics, vectors, matrices, factors, data frames and lists.  This was very informative and done nicely.  I am currently almost finished with the “Intermediate R” course.  This has 5 chapters covering, conditionals and flow control, loops, functions, the apply family, and utilities.  I highly recommend these courses for people starting to learn R, they are very nicely done.

I am also using Jupyter Notebooks  as I go through these courses.  I just started using these, and wish I would have found them much earlier.  I would have done my Python work in them as I went through the math for modelers course.  I just added the R kernel and have been doing the code and taking notes on R as I progress through these courses.  I wish Northwestern University would consider using these for courses in which there is programming.

I am also starting to explore KNIME.  I was first introduced to KNIME earlier this year when I visited Dr. Randall Moorman’s predictive monitoring lab at the University of Virginia.  They were using KNIME on a very elaborate project and I was very impressed with the functionality of this platform.  KNIME is an “open-source, enterprise-grade analytics platform”, that can be used to “discover the potential hidden in their data, mine for fresh insights, or predict new futures”.  I am very early in my exploration of this platform, but I am very impressed so far, and am excited to get to work on a project using this.  I will post further updates as I learn more about it.

Lastly, a few words about what I am listening to and reading.  I am currently listening to the audio version of “The Master Algorithm” by Pedro Domingos.  This is a must read book for practitioners of predictive analytics and anyone who is interested in machine learning.  I am reading the print version of “Superforecasting: The art and science of prediction” by Philip E. Tetlock and Dan Gardner.  This is an excellent read as well.  I will try to review them in more detail when I am done.

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