Top Three Coding Skills for Data Science Professionals

A decade ago, the Harvard Business Review named Data Scientist as the “Sexiest Job of the 21^st Century.” Now, with artificial intelligence, cloud computing, and the democratization of data, the industry has completely evolved, but this fact hasn’t changed. Professionals who have programming and analytics skills are in extremely high demand.^1,2,3 According to the Bureau of Labor Statistics, the projected job growth for data scientists between 2021 and 2031 is 36%, which is much higher than for any other profession.⁴ But with technology changing at such a rapid pace, what are today’s top coding skills for data science professionals?

Keep reading to learn more about the key data science programming languages—and other technical knowledge—that you should be adding to your skills arsenal to be at the forefront of this fast-paced, lucrative industry.

Data science encompasses a range of fields and specializations, including mathematics, statistics, advanced analytics, specializing programming, machine learning (ML), and artificial intelligence (AI). These diverse areas of expertise are used to glean actionable insights from enormous amounts of collected business data to help guide strategic planning and decision-making. The factor that makes this such an in-demand field is the huge amount of data available from many disparate sources.⁵

Most data science professionals who are at the pinnacle of their fields do a certain amount of coding as part of their work. While data science involves more than coding, programming is an important tool that helps data scientists collect and examine data to solve business challenges. For software engineers, writing code is the end goal, but for a data scientist, coding is a means to an end and an important professional tool.⁶

For example, SQL programming may be used to work with databases during the data collection, data storage, and data processing stages.^5,7 A small amount of programming, or at least knowledge of how the program works, may be helpful when conducting data analysis. When data scientists are ready to communicate findings to business leaders, they will likely use data visualizations within reports for greater clarity. Data science programming languages such as Python and R can be used to conduct statistical inference (reaching a conclusion about a group based on calculated statistics from a sampling) or generate visualizations.^5,8

The data science field is increasingly focused on coding skills, with Python and R being the two most common programming languages.⁹ In addition to proficiency in analysis, communication, logical thinking, math and problem-solving, employers look for candidates with strong computer skills. This includes being able to write code, develop algorithms, analyze data, and use data visualization software.¹⁰

Although required programming skills will depend on the employer and the position, the most in-demand data science coding skills are Python and R. Structured Query Language (SQL) is also extremely popular for working with databases.¹¹

A free, open-source tool, Python is a programming language preferred by coding newcomers due to its readability and easy-to-understand syntax. It supports many libraries of pre-written code and web frameworks, in addition to many different industries, and it includes tools that support AI and ML.^12,13

Another of the open-source data science programming languages, R is optimized for data visualization and statistical analysis. It features complex data models and sophisticated tools for data reporting. A favorite among data scientists, R offers a wide range of tools and libraries to cleanse and prepare data, create visualizations, and train and evaluate ML and deep learning algorithms. The RStudio integrated development environment (IDE) can be used to simplify visualization, reporting and statistical analysis.¹⁴

Among the data science programming languages, SQL is used to query and manage relational databases, and data scientists use it to explore and visualize data sets. Easy to understand and use, it’s an invaluable tool for data science applications. SQL enables data scientists to manage and store Big Data and, most importantly, to extract meaning from all that data. It integrates with essential data science programming languages such as Python and R.¹⁵

In addition to the three top coding languages, there are other areas of data science knowledge that are in high demand.

• Big Data: the large quantity of highly diverse information, generated quickly and in great quantities¹⁶
• Cloud Computing: computer services (such as analytics) that are delivered over the internet to allow for greater storage and processing capacity; also referred to as cloud analytics¹⁷
• Data Analytics: the process of extracting useful information and insights from raw data¹⁸
• Data Modeling: a visual representation created to explain the connections between data points and structures¹⁹
• Data Visualization: a graphic representation of information that highlights patterns and trends in gathered data, used to explain the data for fast insights²⁰
• Deep Learning: a subset of machine learning, which attempts to simulate the workings of the human brain, with the ability to learn from large quantities of data²¹
• Natural Language Processing: a field of AI that teaches computers to analyze and understand spoken and written human language²²

Knowledge in these areas will set you apart in the data science field, now and in the future.

Data scientists earn a median annual salary of $105,980. The top-paying industries for data scientists are: ²³

• Computer and peripheral equipment manufacturing ($148,290)
• Semiconductor and other electronic component manufacturing ($142,150)
• Other information services ($139,600)

The state with the most employed data scientists is California, where employers pay data science professionals an annual mean wage of $133,110. The next highest employment level for data scientists is in New York, where they earn an annual mean wage of $122,540.²³

Overall top-paying locations for data scientists are in Washington State, California, Delaware, New York and New Jersey. ²³

With an Online Master of Science in Business Analytics (MSBA) from the Santa Clara University Leavey School of Business, you’ll have the data science expertise that puts you at the forefront of this rapidly expanding industry. MSBA alumni are working in the world’s leading tech companies, including Facebook, Apple, Cisco, Google, and Roku.

Unlike coding 'boot camps,' which teach proficiency in a particular programming language, the SCU Online MSBA program provides a comprehensive data science education. You'll learn key programming skills (Python, R, and SQL) as well as other important data science topics including analytics, big data modeling, cloud computing, data visualization, database management, natural language processing, and deep learning. SCU’s Online MSBA program is expertly curated by Silicon Valley’s top business leaders, and in just over a year, you’ll be able to gain the edge you need to succeed in this highly competitive field.

Don’t wait to start your career advancement. Reach out to an Admissions Advisor today.