A career in data science could be financially and mentally rewarding. However, you’ll need to learn a specific skill set to become a successful data scientist, and one of the things you’ll need to conquer is basic programming. More specifically, you may need to master statistical programming languages.
Statistical programming languages are essential to data scientists for several reasons. Primarily, these programming languages allow data scientists to perform statistical analysis and reconfiguration on unstructured data. Data scientists also use statistical programming languages for projections.
Further in this article, we’ll explore what statistical programming languages are, and we’ll also discuss data scientists and their many roles. We’ll then answer the question of why statistical programming languages are vital to data scientists. After this article, you will definitely be in a better position to understand the relationship between data science and statistical programming languages.
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What Are Statistical Programming Languages?
Programming languages are essential to functional computers. They give commands that allow a computer to execute functions. You wouldn’t be able to open your browser, look at a picture, or type a document without programming languages.
However, the average consumer may be entirely unfamiliar with programming languages, even if they are adept at using their computer’s many functions. Many computers feature an operating system that makes it easier for users to use their personal computers, virtually eliminating the need for consumers to learn programming languages.
Operating systems act as taskmasters and translators. When you boot up your personal computer, the operating system typically engages and takes over, executing system applications to lead you to your home screen.
Whenever you click something, open a file, or search for something on an internet browser, your operating system is translating those various inputs into a programming language. Your computer receives these translated commands and promptly executes them.
The birth of programming languages can be traced back to the 1800s, but the programming languages we associate with computers wouldn’t come into existence until the 1950s. Early programming languages were primarily designed to assist in calculations and mathematical pursuits.
But soon, programming languages would develop into a diverse, often specialized grouping of languages. Today, there are several dozen types of programming languages. Object-oriented scripting and systems programming languages tend to a few of the most commonly used types.
These types of languages are often used for internet browsing and operating system engineering. However, other programming language types are useful for typical applications and tasks, especially when viewing and analyzing data.
Statistical programming languages, like R, tend to be particularly important to data scientists. But this begs the question—What are data scientists?
What Are Data Scientists?
Data scientists are often compared to data analysts and data engineers, but their job descriptions differ slightly. Data analysts perform many of the same analytics tasks that data scientists are expected to perform, but they’re not typically familiar with programming languages, software engineering, or machine learning.
Additionally, data engineers might work with programming language and software development, but they hardly ever get involved with actual data analysis and statistics. Therefore, data science is often described as one of the most challenging and dynamic forms of data work.
What Do Data Scientists Do?
Understanding what data scientists do can be tricky. That’s because data scientists do quite a lot! And because our tech is quickly outpacing many other fields and industries, a data scientist’s precise role is continually changing.
Still, there are a few things that are generally expected of data scientists. We can refer to these tasks as an excellent snapshot of the functions the average data scientist is expected to accomplish. Throughout a typical workday, your standard data scientist might:
- Acquire and import data from a specific source
- Process that data
- Organize data and prepare it for analysis
- Use analyzed data to create a model
- Utilize machine learning to test the models or structured data
- Measure the resulting data and organize it
- Communicate with employers and colleagues about results
Data scientists must feel comfortable with programming, mathematics, the scientific method, business, and data collection. They must also master multitasking, as they’ll likely need to switch back and forth between tasks while awaiting analysis completion.
What Skills Do Data Scientists Need to Master?
Becoming a data scientist isn’t a straightforward process. A concise degree program may help you complete all of the necessary coursework to earn a degree in data science, but this career path requires a specific set of skills.
Individuals that lack proficiency in even one of these skills may struggle to perform data science tasks. In general, data scientists need to prove that they’re highly skilled in many areas. Some of these areas include:
- Machine Learning
However, these are just some of the things data scientists are expected to know. Still, we can take this information and expand on it to discover what data scientists do and why skills in these areas are so crucial to becoming a productive data scientist.
The act of programming seems complicated on the surface. But it can be relatively straightforward. Programming is the act of translating a command into a language your computer can understand.
Programmers write codes that your computer understands as executable commands. Without programming, computers wouldn’t be able to process our requests and satisfy our needs. Data scientists need to understand programming to analyze data promptly and efficiently.
When people talk about statistics, they often think about percentages and recent studies. This association isn’t far off from the real depth and purpose of the statistics field. Statisticians collect raw data, analyze it, organize it, and then interpret it.
For example, if you want to know how many people in your community purchased a bidet in 2020, you could mail out a survey to all of your neighbors and wait for them to submit their completed worksheets.
Once you’ve collected all of the surveys (allowing for some that will never return), you can analyze the data. You may keep a notepad next to you to tally how many people answered yes, no, or prefer not to answer. In this way, you’re also organizing the data while analyzing it.
Finally, you can look at your tally and interpret the information there. If an overwhelming majority answered yes, you might draw several conclusions. Perhaps the supermarkets in your area had a toilet paper shortage during that time.
Maybe the residents of your community shared a deal on a bidet they found online. The interpretation process, unfortunately, can be subjective. Still, statisticians help us to better understand the world around us.
Data scientists must also become experts in machine learning. Though this is a rather broad term that refers to artificial intelligence (AI) processes, data scientists almost explicitly use machine learning for data processing, modeling, and analysis purposes.
Why Are Statistical Programming Languages Important to Data Scientists?
Statistical programming languages are vital to data scientists. Without an understanding of or access to statistical programming languages and their related software, data scientists would have difficulty analyzing unstructured data.
Because data analysis is a crucial part of what data scientists do, individuals in this field that lack programming language knowledge will likely struggle to complete the necessary tasks associated with this occupation.
In this way, understanding statistical programming languages is one of the many foundations of a successful career as a data scientist. Naturally, aspiring data scientists will also need to familiarize themselves with machine learning, data acquisition, and modeling software.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
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- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Programming languages have changed quite a lot since their introduction. There are several dozen types of programming languages, including statistical programming languages.
Data scientists use these languages to sort, organize, and analyze raw data. Consequently, in-depth knowledge of statistical programming languages is an essential aspect of becoming a data scientist.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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