Additional tips; turning in report

(updated 11am June 12) Where to turn in your paper: You may put it under the door of my office (room 1315, 3rd floor), or in my faculty mailbox. Submit the PDF version on TritonEd, in the TurnitIn link.

Improving the course:  I think I have approximately the right mix of topics in Big Data Analytics, but there are other areas that I would like to improve. For example, how can I better smooth the workload in the projects, to reduce the end-of-year crunch? Here is a BDA End-of-year questionaire that asks a series of questions about what I should change.

Thanks for taking the class – I certainly enjoy teaching it.  Enjoy graduation! Enjoy life after the university!

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We are finished with formal classes, so I will post here some additional advice for your projects. Most of this is based on things I noticed either in interim reports or in the final presentations on June 6. It’s going to take a few days to write and edit all of these notes, so keep checking this page through Saturday.

Some of this advice was written with one specific project or team in mind, but all of it applies to multiple projects.

  1. Don’t use loops in R. Most computer languages make heavy use of FOR loops. R avoids almost all uses of loops, and it runs faster and is easier to write and debug without them. Here is an example of how to rewrite code without needing a loop, taken from one of this year’s projects. In some cases, R code that avoids loops will run 100x faster (literally). BDA18 Avoid loops in R
  2. Don’t use CSV data when working with large datasets. CSV  (Comma separated value) files have become a lingua franca for exchanging data among different computer languages and environments. For that purpose, they are decent. But they are very inefficient, in terms of both speed and using up memory. One team mentioned that they were running into memory limits, but the problem was most likely due to their keeping CSV files around! Solution: Use CSV files with read.csv to get data into R. But after that, store your data as R objects (dataframes or some other kind). If you want to store intermediate results in a file, create the object inside R, then use RStudio to save it as an R object. (File name ends in .Rdata.)  When you want it again, use RStudio File/Open File command to load it. No additional conversion will be needed.
    I will add something about this to the notes on Handling Big Data. Dealing with the “big” in Big Data.
  3. Fix unbalanced accuracy in confusion matrices. Yesterday, I noticed several confusion matrices with much higher accuracy for one case than the other. Reminder: We spent 1.5 classes on this topic, and there are multiple solutions. It’s usually due to having much more data of one type than the other. See:
    1. Week 8 assignments and notes
  4. Good graphics. Because graphics are a concise way to communicate, even with non-specialists, I recommend having at least one superb image in every report. (See BDA18 Writing your final report ). I will try to post some examples from your projects, with comments on how to make them even better. On Friday.
  5. Revised discussion on how to write a good final report. Writing your final report June 8
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Author: Roger Bohn

Professor of Tech Management, UC San Diego. General blog https://Art2Science.org Rbohn@ucsd.edu

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