I have enrolled in a Data Bootcamp in Taipei which starts from next week. The program takes 5 months and it covers basic toolboxes that a data engineer usually needs. Indeed, 5 months is quite long if you compare it with other Bootcamps in the U.S. or Europe, which usually take 2-3 months. However, this program I enrolled in is quite cheap (3000€ for 5 months ) and its applicants can even be fully funded by the government if they are eligible (Taiwanese citizenship, <30 yrs old & has been jobless > 3 months).
So, what are covered during these 5 months? In the first 17 weeks the courses are primarily taught based on names of programming languages, for example, Java, JSP, NOSQL, R (including using R to do some basics of Data Mining), Hadoop, Hive, Spark, HTML, etc. , whereas in the last 3 weeks students will be split into groups and are expected to use what they’ve learned to embark on a complete data project (data retrieval, data analysis and data visualization).
Actually, at a first glance, I think it’s a joke that they are unable to give intensive courses and seminars on algorithms/techniques of Machine Learning. Instead, they mentioned a very general term called Data Mining, which covers Statistics, Machine Learning and many other kinds of stuff. The basics of Data Minding, according to what they say, will be taught within 7 days (42hrs) only. This is potentially not good considering that the whole program contains 20 weeks and skills in Machine Learning are vital for a data scientist. However, if you read the title of the program carefully, you will notice that it is the program for those who want to be a Data Engineer instead of a Data Scientist.
Hence, the question now would be, what’s the difference between these two career paths? Here’s a quote from the Blog of Big Data University:
Data Engineers are the data professionals who prepare the “big data” infrastructure to be analyzed by Data Scientists.
Therefore, having a profound knowledge of Machine Learning under your belt is probably not necessary, if you’d like to pursue a career in data engineering.
Then, what’s the problem? Indeed, I’m motivated to become a data scientist instead of a data engineer because I enjoy not only programming but also finding features from unknown things through analytical reasoning. This kind of tendency is probably due to my background in physics. Hence, to fill the gap, I will have to teach myself Machine Learning from time to time, during this five months. Let’s see whether I can manage to learn those things myself while attending to the intensive program. I think it’s probably doable as the program is open for any graduates regardless of their background (so the program won’t be challenging to me, I assume).
For the next post, I’ll write something about my final project.