Are you wondering what kind of programming skills are required for data science roles? Or maybe you are interested in learning how you can improve your programming skills? Well either way, you are in the right place! In this article, we tell you everything you need to know to understand the role that programming skills play in data science.
First, we discuss whether all data scientists need to have programming skills. After that, we discuss whether it is important that data scientists have strong programming skills. Next, we provide examples of benefits that data scientists with strong programming skills enjoy. Next, we discuss what programming languages are used in data science. Finally, we discuss a few principles that any data scientist can follow to improve their programming skills.
Do data scientists need programming skills?
Do data scientists need programming skills to be successful at their jobs? The answer is yes, data scientists do need to have programming skills to be successful in their roles. It is important for data scientists to have programming skills because most data scientists use programming languages to interact with and manipulate the data they use. This is a core responsibility for almost all data science roles, so it would be very difficult for someone to succeed in a data science role if they did not have the skills required to interact with the data.
While there are some point and click tools that can be used to interact with data without doing any programming, these tools are not commonly used by data scientists. There are multiple reasons for this. One of the main reasons is that it is difficult to reliably reproduce work that is executed using these tools because the tools do not provide visibility on what actions were taken in what order. Another reason for this is that it is difficult to fully automate workflows using these tools, which generally require manual intervention to update the datasets that are feeding them.
While there are some individuals with data science titles that do not have programming skills, these situations are definitely exceptions rather than being the norm. These edge cases can happen because there are no regulations that govern how companies can and cannot use data science titles. That being said, programming skills are definitely a prerequisite for anyone who wants to be competitive in the data science job market.
Do data scientists need strong programming skills?
Do data scientists need strong programming skills to be successful at their jobs? Can a data scientist who has mediocre programming skills be successful in their role? This is a much more nuanced question that is not as easy to answer. The reason for this is that while it is true that strong programming skills are required to succeed in many data science roles, there may be cases where this is not a strict requirement.
One factor that affects the answer to this question is the definition of success. If you define success as meaning that the individual in question technically meets the bare minimum requirements that are laid out in the job description, then you are more likely to find cases where a data scientist can succeed without particularly strong programming skills. On the other hand, if you define success as an individual excelling in their role and being able to work effectively and efficiently in their role, then it will be hard to succeed without strong programming skills. Even if a particular data scientist does not write production code and only works on prototypes, they will still take more time to arrive at a prototype if their programming skills are not up to par.
Another factor that affects the answer to this question is the requirements of the specific role that the data scientist is working in. Data scientists that are working collaboratively on a shared code base with other data scientists will need stronger programming skills than data scientists who are working in isolation on their local computer. Data scientists who are working on models that will continue to be used over an extended period of time will need stronger programming skills than data scientists who focus on ad hoc work that is only used once.
How do strong programming skills benefit data scientists?
How do strong programming skills benefit data scientists? In this section, we will provide examples of benefits that data scientists with strong programming skills enjoy that data scientists with weaker programming skills might not.
- Similar outcomes in less time. Data scientists with strong programming skills are generally able to complete projects and achieve results in less time than data scientists who have weak programming skills. There are multiple reasons for one, but one is that it is not uncommon for data scientists with weak programming skills to write 500 lines of code to accomplish a task that could have been completed in 10 lines had the data scientist had a better understanding of fundamental programming concepts.
- Less duplicated effort. Data scientists who have strong programming skills are less likely to find themselves in situations where they are duplicating effort by repeating the same type of work over and over again. That is because data scientists with strong programming skills are more likely to create reusable components that can be used over and over again.
- Fewer bugs and less confusion. Data scientists with strong programming skills are less likely to introduce bugs into their code, which means that they spend less time trying to find the source of bugs or discrepancies. Even when bugs are introduced into their code, data scientists with strong programming skills can often identify the bugs and remove them faster than data scientists who do not have strong programming skills.
- Easily repeatable analysis. Data scientists with strong programming skills tend to produce work that is more repeatable than data scientists with weak programming skills.
What programming languages do data scientists use?
What programming languages do data scientists use? While there are many different programming languages that are used by data scientists, there are a few languages that are used much more commonly than others. Here are the programming languages that are used most commonly by data scientists.
- Python. Python is a common programming language that data scientists use to set up pipelines, manipulate data, and create machine learning models. Python is most commonly used by teams that write production code that needs to interface with code that is written by other teams. It is a particularly common choice at large tech companies and companies that have a strong engineering culture.
- R. R is another language that is commonly used to manipulate data and build machine learning models. R is more commonly used by teams who do not write production code. It is particularly common in government and research, as well as more regulated industries like banking and biotechnology.
- SQL. SQL is commonly used by data scientists to interact with and manipulate data that is stored in company databases. This language is used by all different flavors of data scientists, ranging from those who focus on descriptive analytics to those who develop machine learning models.
Characteristics of well written code
What are some characteristics of well written code that was created by data scientists with strong programming skills? Here are some common characteristics of well written code.
- Well written code is consistent. Well written code consistently follows the same naming conventions and patterns. This makes it easier for team members who are not familiar with a particular component, but are familiar with the broader codebase, to jump in and modify a new component. To learn more about the benefits of code consistency, check out our article on standardization for data science teams.
- Well written code is expressive. Well written code contains expressive names that make it clear what function each piece of code serves. This makes it easier for newcomers to read the code and understand what it does without having to get bogged down in minute details.
- Well written code is simple. Well written code is simple and introduces complexity only where it is strictly needed. This makes it easier for newcomers to read the code and often reduces the amount of code that needs to be written.
- Well written code does not produce unexpected side effects. Well written code does not produce unexpected side effects that would not be expected based on the naming conventions used and functionality described. This gives team members confidence that they can use and modify the code without any unexpected side effects cropping up.
- Well written code minimizes duplication. Well written code does not contain excessive duplication, and rather favors the use of general components that can be reused from one situation to another. This reduces the amount of new code that needs to be written for each new project. To learn more about the challenges that duplication can cause, check out our article on avoiding duplication in data science teams.
- Well written code is modular. Well written code is modular and has clear boundaries that separate one component from the other. This makes it easier for team members to modify one component without having to worry about the details of the other components.
- Well written code isolates slow changing components from fast changing components. Well written code isolates slow changing components from fast changing components. This provides team members with confidence that they can modify the details of the fast changing components without introducing bugs into the slow changing infrastructure. One popular way to do this is to use configuration files to encode fast changing components. For more information on this strategy, check out our article on configuration files for data science projects.
- Well written code is version controlled. Well written code is often tracked using version control software that allows users to understand what changes were made to the code at what time. This software makes it easy to roll back to a previous version in the code when a bug is introduced and also makes it easy to reproduce previous analyses. If you want to hear more about the benefits of version control, check out our article on version control for data science teams.