In this second part of my interview with Randy Au, he discusses the techniques he used to teach himself to code and his approach to programming and data science as a social scientist.
Prior to joining Google, he spent a decade as a mixture of a data analyst, data scientist, and data engineer at various startups in New York City and before that, studied Communications. In his newsletter, he discusses data science topics like data collection and data quality from a social science perspective. Outside of work he often engages in far too many hobbies, taken to absurd lengths.
Click here to learn more about the Interview Series this is a part of.
Randy Au, a Quantitative UX Researcher at Google, explains how he leverages his backgrounds in communication, statistics, and programming as a quantitative UX researcher in Google Cloud to analyze and improve Cloud Storage products.
Prior to joining Google, he spent a decade as a mixture of a data analyst, data scientist, and data engineer at various startups in New York City and before that, studied Communications. In his newsletter, he discusses data science topics like data collection and data quality from a social science perspective. Outside of work he often engages in far too many hobbies, taken to an absurd lengths.
Click here to learn more about the Interview Series.
For my first interview in the Interview Series, I interviewed Astrid Countee. She is a business anthropologist and technologist with a background in anthropology, software engineering, and data science. She currently works as a user researcher at the peer-to-peer distributed company Holo, as a research associate at The Plenary, as an arts and education nonprofit, and as a co-founder of Missing Link Studios which distributes the This Anthro Life podcast.
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Newcomers to data science or artificial intelligence frequently ask me the best programming language to learn to build machine learning algorithms. Thus, I wrote this article as a reference for anyone who wants to know the answer to that question. These are what I consider the three most important languages, ranked in terms of usefulness based on both overall popularity within the data science community and my own personal experiences:
Best Programming Languages for Machine Learning:
#1 Choice: Python
#2 Choice: R
#3 Choice: Java
#4 Choice: C/C++
#1 Programming Language: Python
Python is the most popular language to use for machine learning and for three good reasons.
First, it’s package-based style allows you to utilize efficient machine learning and statistical packages that others have made, preventing you from having to constantly reinvent the wheel for common problems. Many if not most of the best packages (like NumPy, pandas, scikit learn, etc.) are in Python. This almost allows you to “cheat” when programing machine learning algorithms.
Second, Python is a powerful and flexible all-purpose language, so if you are building a machine learning algorithm to do something, then you can easily build the code for the other overall product or system in which you will use the algorithm without having to switch languages or softwares. It supports object-oriented, functional, and procedure-oriented programming styles, giving the programmer flexibility in how to code, allowing you to use whatever style or combination of various styles you like best or fits the specific context.
Third, unlike a language like Java or C++, Python does not require elaborate setup to program a single line of code. Even though you can easily build the coding infrastructure if you need to, if you only need to run a simple command or test, you can start immediately.
When I program in Python, I personally love using Jupyter Notebook, since its interface allows me to both code and to easily show my code and findings as a report or document. Another data scientist can simultaneously read and analyze my code and its output at the same time. I personally wish more data scientists published their papers and reports in Jupyter Notebook or other notebooks like it because of this.
If you have time to learn a single programming language for machine learning, I would strongly recommend it be Python. The next three languages, R, Java, and C++, do not match its ease and popularity within data science.
#2 Programming Language: R
R is a popular language for statisticians, a programming language that is specifically tailored for advanced statistical analysis. It includes many well-developed packages for machine learning but is not as popular with data scientists as Python. For example, in Towards Data Science’s survey, 57% of data scientists reported using Python, with 33% prioritizing it, and only 31% reported using R, with 17% prioritizing it. This seems to show that R is a complementary, not primary language for data science and machine learning. Most R packages have their equivalent in Python (and to some extent the other way around). Unlike Python, which is an all-purpose language, able to do other wonders other than analyzing data and developing machine learning algorithms, R is specifically tailored to statistics and data analysis, not able to do much beyond that. Saying this, though, R programmers are increasingly developing more and more packages for it, allowing it to do more and more.
#3 Programming Language: Java
Java was once the most popular language around, but Python has dethroned it in the last few years. As an avid Java programmer who programs in Java for fun, it breaks my heart to put it so far down the list, but Python is clearly a better language for data science and machine learning. If you are working in an organization or other context that still uses Java for part or all of its software infrastructure, then you may be stuck using it, but most recent developments, particularly in machine learning, have occurred in Python and in R (and a few other languages). Thus, if you use Java, you’ll frequently find yourself having to unnecessarily reinventing the wheel.
Plus, one major con of Java is that conducting quick, on-the-go analysis is not possible, since one must write a whole coding system before one can do a single line of code. Java can be popular in certain contexts, where the surrounding applications/software that utilize the machine learning algorithms are in Java, common in finance, front-end development, and companies that have been using Java-based software.
#4 Programming Languages: C/C++
The same Towards Data Science survey I mentioned above lists C/C++ as the second most popular data science and machine learning language after Python. Java follows them closely, yet I included Java and not C/C++ as third because I personally find Java to be a better overall language than C or C++. In C or C++, you may frequently find yourself reinventing the wheel – having to develop machine learning algorithms that others have already built in Python – but in some backend systems that have been built C or C++ like in engineering and electronics, you do not have much of an option. C++ has a similar problem with Java as well: lacking the ability to do quick on-the-go coding without having to build a whole infrastructure.
Conclusion
For a beginner to the data science scene, learning a single programming is the most helpful way to enter the field. Use learning a programming language to assess whether data science is for you: if you struggle and do not like programming, then developing machine learning algorithms for a living is probably not a good fit for you.
Many groups are trying to develop softwares that enable machine learning without having to program: DataRobot, Auto-WEKA, RapidMiner, BigML, and AutoML, among many others. The pros and cons and successes and failures of these softwares warrants a separate blog post to itself (one I intend to write eventually). As of now though, these have not replaced programming languages in either practical ability to develop complex machine learning algorithms and in demonstrating that you have the technical computational/programming skills for the field.
For a beginner to the data science scene, learning a single programming is the most helpful way to enter the field. Use learning a programming language to assess whether data science is for you: if you struggle and do not like programming, then data science where you would be developing machine learning algorithms for a living is probably not a good fit for you. Depending on where you work or type of field/tasks you are doing, you might end up using the language(s) or software(s) your team works with so that you can easily work jointly on projects with them. For some areas of work or tasks might prefer certain packages and languages. If you demonstrate that you can already know a complex programming language like Python (or Java or C++), even if that is not the preferred language of their team, then you will likely demonstrate to any hiring manager that you can learn their specific language or software.
