Top 9 languages for Data Science in 2020

Of 256 programming languages, know the ones for Data Science!

Data Science has been a big deal for quite some time now. In the rapidly expanding technological world of today, when humans tend to generate a lot of data, it is quintessential that we know how to analyze, process, and use that data for further knowledgable business insights.

There has been enough said on Python vs R for Data Science but I am not talking about it here. We need both of them and that’s about it. I have created a list of Top 10 programming languages for Data Science that you can learn in 2020 and also while there is still some time to hit back outdoors 😐

The languages made to the list on the basis of their popularity, number of Github mentions, the pros and the cons, and their relevancy to Data Science in 2020.

1. Python

All you need is Python. Python is all you need.

I can write tens of stories on why Python is THE language for Data Science.

Because of its versatility, Data Scientists can use Python for almost any problems associated with the data science processes.

Why Python?

The object-oriented nature of Python facilitates data scientists to execute tasks with better stability, modularity, and code readability. While Data Science is only a small portion of the diverse Python ecosystem, Python is rich with specialized deep learning and other machine learning libraries and popular tools like scikit-learn, Keras, and TensorFlow. Undoubtedly, Python enables data scientists to develop sophisticated data models that can be plugged directly into a production system.

Per Python developers' survey results, 84% of respondents used Python as their main language, while for 16% it was their second language.

Data in Python

For data collection, Python supports CSV, JSON, SQL tables, and web scrapping with beautiful soup.

The data analysis library for Python, Pandas is hands down the best you can get for data exploration. Organized into data frames, Pandas can filter, sort, and display data with all the ease you can imagine.

For data modeling,

1. NumPy — numerical modeling analysis
2. SciPy — scientific computing and calculation
3. scikit-learn — access numerous powerful machine learning algorithms. It also offers an intuitive interface that allows Data Scientists to tap all of the power of machine learning without its many complexities

For data visualization, matplotlib, plot.ly, nbconvert to convert Python files to HTML documents spells out beautiful graphs and dashboards to help Data Scientists express the findings with force and beauty.

2. R

R is an open-source tool that allows Data Scientists to work with many operating systems across platforms. Statistics is the core strength of this technology. R is not just a language but an entire ecosystem in itself to perform statistical calculations. It facilitates to perform operations on data processing, mathematical modeling, data visualization with built-in functions.

Data in R

R supports Excel, CSV, text files, Minitab or SPSS file formats, web scrapping with Rvest, and such file formats for Data collection.

R was built to do the statistical and numerical analysis of large data sets and therefore, there are a plethora of operations that can be performed for Data Exploration— sort data, transpose tables, create plots, generate frequency tables, sampling data, probability distribution, merge data, variable conversions and much more. Explore dplyr, tidyr for best results.

R is a robust environment suited to scientific visualization with many packages that specialize in a graphical display of results for data visualization. We can have the base graphics, charts, and plots with the graphics module. The visualization can also be saved into image formats such as jpg., or separate PDFs. ggplot2 is a boon for advanced plots such as complex scatter plots with regression lines.

R vs Python

It’s a never-ending debate on Python vs R for Data Science but we as Data Scientists need to understand that while both have its strong points, there are weaknesses too.

Most programmers recognize either one or the other programming language as their “go-to”. Say, R users sometimes crave object-oriented features built into the Python language. Similarly, some Python users dream of a wide range of statistical distributions available in R. This implies that it is quite possible to combine the two leading technologies in one project to get a unique complemented set of functions.

3. Scala

Scala is a combination of object-oriented and functional programming in one concise, high-level language. This language was originally built for the Java Virtual Machine (JVM) and one of Scala’s strengths is that it makes it very easy to interact with Java code.

Why Scala?

One of the prime reasons to learn Scala for Data Science can be attributed to Apache Spark. Scala used in conjunction with Apache Spark to deal with large data volumes (Big Data) makes it invaluable for the Data Scientists.

Many of the high-performance data science frameworks built on top of Hadoop usually are written in and use Scala or Java. The reason Scala is used in these environments is because of its swift concurrency support. As Scala runs on JVM, it is almost a no-brainer when paired with Hadoop.

Why not Scala?

The only downside for Scala is its learning curve. Plus, the community is not so wide therefore, it becomes tedious to look for answers to the questions on our own in case of errors.

Scala is great for projects when the amount of data is sufficient to realize the full potential of the technology.

4. SAS

SAS — Statistical Analytical System

Just like R, SAS is a tool developed for advanced data analysis and complex statistical operations. It is a closed-source proprietary tool that offers a wide variety of statistical capabilities to perform complex modeling. SAS is mostly used by large scale organizations and professionals due to its high reliability.

Why SAS?

Mind you, SAS is not a tool best suited for beginners and independent data science enthusiasts, because SAS is tailor-made to meet advances business demands. However, if you are looking to Data Science as your career, it is to the good practice to have operating knowledge of SAS for a gleaming profile.

SAS is good at performing statistical modeling through SAS Base — the main programming language that runs the SAS environment.

Why not SAS?

While SAS has been an undisputed market leader in the enterprise analytics space, to compare its capabilities with Python or R, SAS may seem difficult to model and visualize data with. The learning curve is tricky and is mostly used by large corporations with huge budgets.

SAS offers multiple certification programs for Data Scientists. A few of them:

5. Julia

Julia language works with data faster than Python, JavaScript, Matlab, R, and is slightly inferior in performance to Go, Lua, Fortran, and C. Numerical analysis is the strength of technology, but Julia also copes well with general-purpose programming.

Why Julia?

Julia is faster than other scripting languages, allowing Data Scientists to have rapid development of Python/MATLAB/R while producing code that is fast.

With the Julia data ecosystem, multidimensional data loading is quick. It performs aggregations, joins, and preprocessing operations in parallel. Julia includes various mathematical libraries, data manipulation tools, and packages for general-purpose computing. In addition to these, integrations with libraries from Python, R, C/Fortran, C++, and Java is extremely easy.

Why not Julia?

Due to the fact that Julia is not a fully mature tool, the community is still narrow. While searching for errors or malfunctions, the limited set of options or solutions can be a hindrance. There is great hope among industry experts that Julia will be able to compete fully with Python and R when it becomes more mature.

6.a MATLAB

MATLAB is the first tool is used for Data Science. I started learning Data Science in 2017 with Andrew Ng’s course on Machine Learning on Coursera where he used Octave in the class. At the same point of time, I was learning MATLAB in my undergrad classes and there I was practicing MATLAB for the Coursera class.

I know MATLAB as the best programming language when it comes to performing profound mathematical operations. While Data Science is also a lot about math, it makes this technology a powerful tool for mathematical modeling, image processing, and data analysis.

Why MATLAB?

It holds a vast library of mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, numerical integration, and solving ordinary differential equations. MATLAB provides built-in graphics for visualizing data and tools for creating custom plots.

Why not MATLAB?

Now, Data scientists rarely use MATLAB having said that it is great for math and modeling. With the advent of R and Python in the Data Science domain, MATLAB has been on a fall. It is also more popular amongst academia given the high licensing costs.

The language you use for Data Science largely depends on the problem that you are solving. If your problem requires complex math calculations, there would be no better starting point than MATLAB, at least for the initial data exploration and preliminary results.

6.b OCTAVE

It is the main alternative to MATLAB. In general, both of these technologies do not have extremely fundamental differences, just some minor exceptions. Like MATLAB, Octave can be used in projects with a relatively small amount of data if strong arithmetic calculations are needed.

7. Java

Java is perhaps one of the oldest object-oriented language used for programming and business development. The majority of the well known Big Data tools on the likes of Hive, Spark, and Hadoop are written in Java. Java has an extraordinary number of libraries and tools for Data Science that we might not be aware of such as Weka, Java-ML, MLlib, and Deeplearning4j.

Why Java?

Java might definitely not come across as an obvious language for data science, but it is one of the top programming languages for data science thanks to data science frameworks like Hadoop that run on the Java Virtual Machine (JVM).

Hadoop is a popular data science framework for managing data processing and storage for big data applications. Given its ability to handle limitless tasks at once, Hadoop enables the storage and processing of large volumes of data.

To conclude, Java is one of the best data science programming languages to learn if you want to enjoy the capabilities of the Hadoop framework.

8. Perl

Perl is a family of two high-level, general-purpose, interpreted, dynamic programming languages. Perl is capable of handling data queries much more efficiently as the language relies on lightweight arrays, which don’t require much attention from the programmer.

Why Perl?

Thanks to its versatility as a general-purpose scripting language, it shares a lot in common with Python, being a dynamically typed scripting language. Perl finds its use in quantitative fields such as bioinformatics, financial, and statistical analysis.

With the release of Perl 5, the ability to handle large data sets much better than its predecessors. It is making its way as the ‘big-data lite’ with Perl 6. Boeing, Siemens, and some more Fortune 500s are ready to experiment with Perl actively for Data Science.

Perl map or reduce terabytes of data with simple, maintainable architecture by orchestrating data inserting and querying on large scales. With Perl 6, the plan is to provide a modular, pluggable architecture with the flexibility and customization for Big Data management.

Why not Perl?

Perl is not a language whose learning alone can make you an effective and efficient data scientist. It isn’t stand-out fast and the syntax is famously unfriendly. Since it is a relatively unpopular language, community support for Data Science has been less. The community for “Perl Developers” is growing affluent, to mention. Overall, there hasn’t been a drive towards developing Perl as a data science language.

9. Haskell

Haskell is a general-purpose, statically typed, purely functional programming language with type inference.

Why Haskell?

Haskell has a strong base for financial code while Haskell can easily interact with Excel for computations. It is good for encoding mathematical concepts. More generally, Haskell excels at abstraction, and so data science benefits from coherent abstractions of Haskell as much as any other math or software tool.

Haskell can also operate directly on values from R with HaskellR.

Haskell has DataHaskell, an open-source resource for reliable and reproducible data science and machine learning development by leveraging the Haskell programming language. The community for Data Science in Haskell is surely growing with DataHaskell. Do check it out here!

Principal AI scientist at Target says “Haskell is expressive, faster, safer. Haskell has not traditionally been used for data science and so the library selection is limited. Haskell has an affinity for math but, in the end its type system and mathiness help more with domain-specific business code than anything else.”

Why not Haskell?

Haskell has an application as a language for Data Science, however, just the knowledge of Haskell is not enough like Python or R. Haskell is not robust in terms of data science capabilities. The learning curve is essentially difficult and time-consuming.

Thank you for reading! I hope you enjoyed the article. Do let me know what language you have been using and which one are you looking forward to discovering over the summer in your Data Science journey?

Happy Data Tenting!

Disclaimer: The views expressed in this article are my own and do not represent a strict outlook.

Know your author

Rashi is a graduate student at the University of Illinois, Chicago. She loves to visualize data and create insightful stories. When not rushing to meet school deadlines, she adores writing about technology, UX, and more with a good cup of hot chocolate.