Programming Languages Needed to Become a Data Science Master
Job opportunities for data scientists are expected to nearly triple during the decade ending in 2026, according to the U.S. Bureau of Labor Statistics. As computer technology allows businesses to collect larger volumes of data more quickly, the greater the demand will be for scientists who can find useful information in that data. To be successful, data scientists need to be proficient in the types of programming languages used to work with data and develop programs to track and analyze data.
What Data Scientists Do
Data scientists develop algorithms to identify patterns in large amounts of data. They then are able to analyze those patterns. Data that needs to be analyzed can originate from anywhere. Websites collect data, for example, about when people visit and from where, and high-traffic sites easily can have millions of data points. Data does not have to originate from websites. It also can come from research that has been conducted over generations. For example, data from different types of medical research can be vast and needs to be analyzed.
Data scientists develop software or use software developed by others to help with the process of analyzing datasets. They also seek ways to present their findings to others in visually appealing or easy-to-understand ways.
Data scientists use computers and computer software because of the large volumes of data they are dealing with. To be effective at the job, it is important to be proficient in at least one relevant programming language and probably more than one, depending on specific needs. SQL is a good place to start because it is so common, but there are several other programming languages worth learning.
If you really want to boost your marketability as a data scientist, learn as many relevant programming languages as possible.
These are some of the most popular programming languages that are useful for data scientists.
SQL: SQL, which stands for “structured query language,” focuses on handling information in relational databases. It is the most widely used database language and is open source, so aspiring data scientists definitely shouldn’t skip it. Learning SQL should equip you to create SQL databases, manage the data within them, and use relevant functions. Udemy offers a training course that covers all the basics and can be completed fairly quickly and painlessly.
R: R is a statistics-oriented language popular among data miners and not overly difficult to learn. If you want to learn how to develop statistical software, R is a good language to know. It also allows you to manipulate and graphically display data. As part of its Data Science Specialization program, Coursera offers a class on R that teaches you how to program in the language and apply it in the context of data science/analysis.
SAS: Like R, SAS is used primarily for statistical analysis. It’s a powerful tool for transforming information from databases and spreadsheets into readable formats like HTML and PDF documents or visual tables and graphs. Originally developed by academic researchers, it has become one of the most popular analytics tools worldwide for companies and organizations of all kinds. The language is not open-source, so you likely will not be able to teach yourself for free.
Python: One of Python's main perks is its wide variety of libraries (Pandas, NumPy, SciPi, etc.) and statistical functions. Since Python, like R, is an open-source language, updates are added quickly. Another factor to consider is that Python is perhaps the easiest to learn, due to its simplicity and the wide availability of courses and resources on it. The LearnPython website is a great place to start.
MATLAB: This option was developed by MathWorks and is designed to handle the types of calculations professionals in mathematics might need. It is a popular option in academia.
Julia: Marketed as a high-performance option, Julia is good for analyzing large volumes of data rapidly. One of its features is the ability to perform online computations on streaming data. Julia is an open-source option.
TensorFlow: TensorFlow is a well-known commercial option because it is used to help run many of Google's functions, including its search engine and databases for programs like Google Photos.
Scala: Scala is a popular option that handles large datasets and works well with Java.