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Five Ways to Prepare for Data Science Bootcamp

Posted in Tech on September 27th, 2017

After getting a little taste of what data science entails though an online course, I decided to delve deeper into data science by signing up for a 12-week course “bootcamp.” Some schools also call these courses “immersives.” At times while attending the bootcamp, I felt overwhelmed during the lectures and felt lost during the code-alongs. Here are some advice that I would give to my younger self before taking this delightful and sometimes dreadful journey.

Five Suggestions to Prepare

Learn some basics of programming. The curriculum will provide details on the main programming language focus. There are tons of resources out there for free that can help you get a jump start on learning a language, if you do not know one already. Although the bootcamp prepares you in many ways, I have found that I will get way behind, when I am not familiar with some of the basic commands. I have to search online what I am trying to accomplish. Good foundation on structure and concepts of object oriented programming can be beneficial. You may want to print out some cheat-sheets before the class begins.

Follow people on twitter. I find that developers are very friendly and active twitter users. Also, following people has led me to follow other people and organizations that provide me useful information. Here are some people that are fairly active on twitter:

  • @BecomingDataSci – Renee offers some great insights on the field. She also provides some helpful resources through her website, including interviews of data scientists.
  • @chrisalbon – Chris drew up some awesome machine learning flashcards on twitter. These reinforce what I learned at bootcamp. He is now selling electronic set of them now for only $10 on machinelearningflashcards.com.
  • @AngeBassa – Angela has put together a list of resources who are willing to mentor and provide guidance through datahelpers.org.

Listen to some podcasts. This allows you to be aware of different views and discussions, becoming more familiar into the world of data science. It seems to me that this field is rapidly changing. It is not necessary to listen to everything. Some of podcasts that I listen to are: Not So Standard Deviations, Hanselminutes, Data Skeptic, and Podcast.__init__.

Get familiar with some of the environments that you will be working on. The syllabus provides you the opportunity to look-ahead. That way, you do not need to wait for the instructors to teach you. Most likely, they will encourage you to learn more on your own. They will teach you enough to be dangerous, but the onus is on the students to really investigate into the fine art of data science.

Install as much software as early as possible. Installing software has been a major nuisance and time consuming. Sometimes, my machine ran into difficulties while installing machine learning packages. Other times, I would be able to install, but then the machine will not be able to run those libraries.

For example, my machine could not run pymc3 and multinomial naive bayes. It was frustrating that my computer would hang and I would just have to sit and watch the instructor program on the projector. Eventually, I was able to run both models on Azure notebooks. Guess where I was able to find about Azure notebooks? On Twitter, thanks to @DynamicWebPaige.

It is a beta version, running a Jupyter notebook instance on Azure. Majority of the libraries are installed, but not everything worked for me. However, it ran perfectly on the pymc3 and multinomialnb models.

Bootcamps Are Not For Everyone

There many ways to learn, including traditional routes like universities, MOOCs, books, and videos. But, being a little more prepared can go a long way in making the learning experience manageable. I hope this has been helpful to you. If you are heading in the direction of bootcamps, make the most of your time and good luck!

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