Do Data Scientists Code? Here’s the Thing…

Data scientists are in huge demand in the United States, as well as in the rest of the world; and at the same time, there is a shortage of qualified professionals in the field. The demand for data scientists is much greater than the supply. The field requires a heavy combination of computer science, mathematical, and analytics skills, including coding. 

Data scientists code using R, Python, and SQL to collect, manage, and analyze data in large and complex databases. They also sometimes use programming languages like C, C++, Java, and Javascript to analyze and present their findings once they’ve collected data.

This article will discuss the skills necessary for data scientists, including coding languages and other computer science skills, as well as ways of learning those skills and finding a career in the field.

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

What Do Data Scientists Do?

Data scientists collect and store data, then analyze that data to find trends and insights. This allows them to better understand things such as customer shopping patterns, projections for business growth, and many more useful findings for businesses.

They must be highly skilled in statistics and mathematics, and able to use computer programs and software to solve problems. Data scientists also need to be able to understand a business’s needs and communicate with colleagues about relevant trends that they’ve found.

Some data scientists leverage artificial intelligence as another way of capturing and exploring data. Through machine learning, a common form of artificial intelligence, a computer can automatically take in new data and adjust its algorithms based on that new information.

Can’t Computers Do the Same Thing as a Data Scientist?

Machine learning and computer automation simplify the data collection and analysis process once established but need to be developed on a case by case basis by a data scientist who understands what questions to ask and why. Otherwise, using the same basic and repeated algorithms would fail to produce meaningful data for particular business needs.

Target recently developed a tool to determine whether or not a customer is pregnant based on an automated analysis of shopping patterns. It is a great example of a program written for a particular dataset with a particular question in mind. Although this tool could be reused, it needed to be developed by a team of data scientists with creativity and business intuition.

Is Coding Necessary for Data Scientists?

Data scientists use a wide range of tools to analyze databases and model their findings, including several coding languages. A data scientist typically uses at least one coding language built for computing statistics, like SQL, R, or Python. Sometimes, other programming languages may be necessary for the job.

Coding Languages Used by Data Scientists

List below are some of the most popular coding languages used by data scientists:

• SQL. Also known as Structured Query Language, this is used to input, analyze, and retrieve data from databases.

• R. This is used for statistical analysis, modeling, and creating graphs and reports. 

• Python. A widely applicable programming language that allows the user to create functions and algorithms to perform analytics. It is also used in web development and software development.

• Java. A language similar to Python and also used in web development. It is used to clean data, create machine learning algorithms, and perform analytics.

• C++. A versatile language that can be used to read data and perform mathematical analysis, as well as creating graphs.

In addition to coding, there are several software programs and soft skills that data scientists use every day.

Other Important Skills for Data Scientists

Listed below are some other important skills that data scientist must have:

• Data visualization

• Microsoft Excel

• Ability to draw relevant business insights

• Knowledge of different machine learning techniques weigh their pros and cons

• Knowledge of A/B testing techniques

• Presentation skills

• Communication skills

• Critical thinking

Do Data Scientists Need to Know Web Development?

Although data scientists and web developers use many of the same tools and languages, data scientists do not need to know web development outside their areas of expertise. They are not typically responsible for building the user interface or background architecture of a web application. 

A data scientist’s focus is on handling data and uses coding languages simply as a way to transform and better understand data. Although some data scientists do create programs for artificial intelligence applications, they are not responsible for the entire web development process in building an application.

How Do You Become a Data Scientist?

Data scientists are the most in-demand professionals in the world, making an average annual salary of $113,000. The field is expected to grow by 27.9% by 2026, although there is a significant shortage of professionals with a high level of training necessary to be a data scientist.

Graduate Degrees

Most data scientists (88%) have at least a Master’s degree, while many have a Master’s degree plus a Ph.D. (46%). This is partly due to the depth of knowledge that a data scientist needs to have across several different fields. Successful candidates most often have a degree in data science, mathematics, statistics, or computer science.

A data science degree typically involves coursework in statistics, data engineering, machine learning, and hands-on experience as a data science professional. 

Undergraduate Degrees

Even though undergraduate degrees in data science or related fields are not sufficient on their own to land interested candidates a data science job, they certainly provide leverage and make things a lot easier for interested candidates. Additionally, there are also employers who are open to hiring candidates that are skilled in data analysis and interpretation, but have pursued their undergraduate degree in a field that is outside of data science.

Boot Camps

While data science boot camps cannot replace a graduate program’s advanced training, they can at least help professionals with a STEM-related Bachelor’s degree break into the field of data science. This is especially true for candidates with a bachelors in data science-related field, as with their focus on teaching coding languages and basic data-science skills, various boot camps can help close the gaps restricting these candidates from landing a data science job.

Practical Skills

In addition to formal training, it is important for aspiring data scientists to be able to prove their ability to perform the necessary functions of the job before being hired. Often, this means a practical skills analysis crafted by the employer.

In the past, these skills analyses involved on-the-spot brain teasers and coding challenges, but in recent years, more employers have shifted to a take-home approach that more accurately represents an on-the-job project. 

These skills tests can even be the playing field for candidates with varying levels of formal training and reduce bias that may come through during an interview. Although qualifications and demeanor remain important qualifiers for the job, the skills tests give applicants a chance to prove what they can do.

Books like Cracking the Coding Interview or Be the Outlier: How to Ace Data Science Interviews can help candidates prepare for the technical and non-technical parts of a data science interview. These include foundational knowledge about the field, as well as practice challenges.

Careers in Data Science

Data science is a broad field and can be applied in several different ways. Some professionals are simply called data scientists, and work on the technical side of data analysis to inform company decisions. However, others take their training in alternative directions.

• Machine learning engineers – create software that allows computers to gather and integrate new data into algorithms. They must have strong programming skills, as well as statistical expertise. 

• Machine learning specialists – research new approaches to machine learning algorithms, and may also be called research scientists or research engineers.

• Applications architects – program and analyze applications used by businesses, building user interfaces, and infrastructure.

• Enterprise architects – determine the technology necessary to meet an organization’s objectives and have strong business acumen and technological expertise.

• Data architects – create the applications used for analytics and ensure that database systems are built to perform efficiently and accurately.

• Infrastructure architects – oversee an organization’s technological strategies and make sure that their systems are able to support any planned developments.

• Data engineers – process data and create systems to make their results accessible to data scientists.

• Business intelligence developers – help non-technical business users find information, sometimes building applications that simplify the data analysis process.

• Statisticians – perform in-depth data analysis to identify trends and relationships between different factors, informing and advising business stakeholders about their findings.

• Data analysts – track website analytics and manipulate large datasets to aid in a company’s decision-making processes.

Conclusion

Data scientists use several coding languages daily, mostly for accessing and using databases. The field requires a deep level of theoretical knowledge as well as a broad range of practical skills, including computer programming.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous 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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