Importance of Software Skills in Data Science
Data science is an extremely popular field right now, and a variety of people can make excellent data science candidates. Data science sits at the intersection of analytics and engineering, so a combination of mathematical skills and programming expertise is relevant. Data scientists with software skills are more desirable candidates. In fact, programming has been cited as the most important skill for a data scientist. A data scientist with a software background is a more self-sufficient expert who does not need outside resources to work with data. For example, they’re able to query the data on their own without using a blackbox tool or an engineer. For a variety of reasons, software skills greatly benefit a data scientist.
Data science is a discipline that spans multiple fields and uses scientific tools to extract meaning from data. The specific tools used-many of which are software skills-vary by data scientist and company, as does the outcome of the data analysis. Some data scientists are building software to update with new data information, while others are creating a visualization for business teams to use in decision-making. Additionally, some data scientists may be doing research, in which the outcome of the data science is information itself.
Programming means using a set of written rules that give instructions to a computational device (such as a computer) to perform a set of tasks, which will then change a function within the computer. Programming languages each have their own syntax and grammar, so a different set of words and instructions are analyzed depending on what programming language the engineer chooses. Additionally, the way a computer or computing device makes sense of the syntax varies by language, and a compiler translates this written code into information for a computer.
The job of a software engineer, data scientist, or any other technical person writing in a programming language is to accurately use the language or tools so the computer does the job it’s supposed to do. Sometimes, there are errors with the actual words, which is known as a “syntax error,” and the compiler can often tell the programmer what to change in order to make the code run effectively. Sometimes, even though the code fits the correct syntax, it is not performing the job that the technical engineer intended, in which case, debugging is necessary. This can often be a much more complicated process, and requires a more in-depth knowledge of computer science.
Key elements of software engineering
Technical skills are extremely important in data science, and there are a variety of applications for a data scientist’s programming skills.
Large datasets
A key function of the data scientist’s role is to analyze massive data sets, which cannot be manipulated manually. Although programs like Excel offer the chance for data scientists to study data without programming, the amount of data that can fit in Excel is one million rows, which is limiting for some data sets. 40 percent of data scientists know Python, and a knowledge of Hadoop was considered the 2nd most important skill for a data scientist. Using a language like Python to manipulate data in large data sets is extremely useful to data scientists.
Databases
Additionally, database tools often require programming. Using SQL to query a database is a key function of the data scientist’s role. While one can learn SQL without a software background, having the knowledge of programming that comes from developing software skills is useful in writing more efficient SQL queries.
Understanding blackbox tools
There has been a recent growth in tools that allow business users to work with data sets without having programming skills. In fact, “ Machine-Learning-As-A-Service “ startups have been created for the express purpose of eliminating the need for understanding a machine learning model. While it may seem like this would decrease the need for data scientists with programming skills, the opposite is true. Many business users do not have a comprehensive view of these blackbox tools and are therefore more likely to misuse them or draw an incorrect conclusion. A data scientist with a background in software is better able to understand how these tools work.
Data cleaning
One key application of data science software skills is data cleaning. Data cleaning is the process of removing errors or irregularities from a data set so that analysis can be performed on it. In order to apply a machine learning algorithm to a set of data, the data must be standardized in some way. However, depending on the source of the data, this might not be the case. For instance, if data are inputted through surveys that people wrote in, there may be spelling errors. If a data scientist is trying to perform Natural Language Processing on this data and see which words appear most often, spelling errors throw off their analysis. Therefore, having a way to eliminate these errors in the data cleansing process is critical.
Software skills are extremely beneficial in data cleaning. In fact, trying to manually clean data typically only leads to messier data. Using tools that perform the ETL (Extract, Transform, Load) step of the data work enables a data scientist to do their work with clean and comprehensive data.
Benefits of knowing multiple languages
Knowing one programming language is helpful, but knowing multiple languages is even more beneficial to a data scientist. Knowing multiple languages allows a data scientist to collaborate across a variety of teams. For instance, if the rest of the data science team is working in R but the software engineering team is working in Python, the data scientist who knows both is best able to bridge the divide between the two teams.
Furthermore, knowing multiple languages can give the data scientist a better understanding of the underlying product they’re working with. For a data scientist working at a large software company, they may be analyzing click-through rates at some point in the marketing funnel. If the data scientist understands Javascript and HTML, which are not typically taught in a data science curriculum, they’d understand how the website logs data. With this knowledge, they can more easily figure out if there’s some sort of error or bug in the data ingestion process that’s altering their data downstream.
Connections between data science and software engineering
Depending on the role of the data scientist, various technical skills are needed. For example, at a company with a limited software engineering team, a data scientist may be required to build their own analytics tools. A data scientist with programming skills is not limited by existing data science tools or the software engineering team’s bandwidth but instead can develop their own programs. Being able to run iterations on a big data set is much easier if the data scientist is capable of constructing the tool out of a few lines of code. Having a background in statistics is also incredibly helpful on a basic level.
Additionally, at some companies, a data scientist may take on a role more similar to that of a product manager. A key role of a data scientist is to understand the product they’re working with and anticipate questions. Knowledge of software skills is useful for a full understanding of a software product. For instance, if a data scientist is analyzing client-side data on how many clicks a user has on a given page, the data scientist will benefit from understanding what software engineers did to log that code. Sometimes, the product management role is designated to a product expert, but other times, the data scientist needs to fill this function, and software skills better enable them to do so.
A data scientist may also be in a role that’s more similar to that of a data engineer. Data engineers are responsible for maintaining the databases, ensuring data from the software tool is accurately logged, and making the data easily accessible for efficient analysis. A data scientist at a company without a designated data engineering team may be responsible for much of this work, and software skills are particularly crucial in this case. Not only does a data engineer have to know database tools like SQL or MongoDB to maintain databases, but they also must know lower-level languages to integrate the data collection into the software product. The data engineer must also understand how a data scientist intends to use the data, and for this, a knowledge of machine learning or AI is crucial.
Differences between data science and software engineering
There is some overlap between the roles of a data scientist and that of a software engineer, but a data scientist focuses more on analytics, while a software engineer has a stronger programming foundation.
Roles and skills
A data scientist is much more likely to work with Artificial Intelligence (AI) and machine learning than a software engineer. Artificial intelligence (AI) is becoming a more important part of a data scientist’s skill set, and software engineering is critical for performing AI. R is an important programming language for the data scientist working on AI or a machine learning model. However, only 43 percent of data scientists know R, so having this particular software skill will set a data science candidate apart from their competitors. Additionally, many data science master’s programs teach skills like this to manipulate data and work with machine learning and AI.
Software engineers use programming languages and tools to build software. They typically know many more programming languages than a data scientist, such as Python, Java, C++, and more. They may be responsible for building the front end of websites using tools like Javascript, or maintaining back-end systems. The software engineer might interface with the data scientist to ensure that the data is collected and streamlined into a database so that a data scientist can manipulate it. Overall, the software engineer spends their day building software rather than drawing analyses from the data.
Salary
The value of a software engineering background in data science is only expected to grow. “Machine learning” and “computer science” were two of the four most commonly cited skills in LinkedIn job postings for data scientists in 2018, and their rankings are projected to rise. The salaries of software engineers and data scientists are comparable. In 2017, both data scientists and software engineers earned an average of $137,000/year. However, software engineering salaries are rising faster than data science salaries, which is something for a student considering both fields to keep in mind. The tools and knowledge software engineers bring with them to any job become invaluable to any growing business and is reflected in the salary that companies and organizations are willing to offer.
Top software skills
The top software skills for data scientists are languages such as Python and R. While knowledge of black-box tools is also helpful for some working with machine learning and statistics, programming languages are among the most helpful software tools for data scientists to master. To understand which programming language is right for a data scientist, consider the options below.
R
R is an open-source statistical programming language for building statistical models and conducting numerical analysis. R is a language heavily used in academia, and many data scientists who come to the industry from universities use R. R is considered relatively easy to learn, and it’s a language specifically for data, so software engineers are unlikely to be proficient in it. R is especially well-known for its graphics and visualization capabilities.
Python
Python is a free, general-purpose programming language. It has an extensive array of libraries, including some of the top AI and machine learning tools. Python is particularly well-equipped for the ETL process, which makes it an excellent choice for a data scientist performing a great deal of data cleaning.
Java
Java is a programming language used cross-functionally between software engineering and data science. Java is an older language, so a wider variety of professionals have this skill, and it’s built into many legacy systems. Java also has a variety of packages for conducting machine learning.
SQL
SQL is a programming language specifically for accessing data from a database. As a language, SQL is relatively simple and has a very small syntax. SQL is specifically for relational databasess, so a knowledge of these databases are critical for learning SQL. Most data scientists use SQL in conjunction with other programming languages because once the data is extracted, additional tools are needed to analyze it.
Stata
Stata is a statistical analysis tool, rather than a programming language. It utilizes a command-line, so the programmer still has to have knowledge of the Stata syntax. Stata is used widely in academia and economics, but less-so in industry because of its high price. However, Stata is simple to learn, and many data scientists find it to be an efficient option when just starting out.
Matlab
Matlab is similar to Stata except that it’s used more widely in engineering, rather than economics. Matlab is a programming language for high-performing numerical analysis. It’s typically used for prototyping a data product, rather than building the whole tool. Matlab is one of the oldest languages used for data science, and as a result, it has a number of key features already built-in.
How to learn programming skills
Programming skills are extremely useful for data scientists. However, for someone entering the field without technical skills, learning software skills can be daunting. There are many options for how to develop these skills, and choosing the best one is an important choice.
What to consider before choosing a programming language
Before choosing which software skills to develop, there are several considerations. As mentioned earlier, learning multiple languages is beneficial, as it will enhance the desirability of a data science candidate. However, time is a scarce resource, and a prospective data scientist must choose how to best invest theirs.
Factors to consider before choosing which languages to learn include: previous software skills, cost, the necessity for machine learning and AI tools, what other team members are using, industry, and more. For a data scientist with a background in software engineering, picking up lower-level languages like C++ and Java is easier. For a data scientist coming out of a bootcamp without prior knowledge of programming, tools like R and SQL are much easier to pick up. For a candidate who may or may not have a strong programming background, a master’s program such as the University of Virginia’s Master of Science in Data Science solidifies software skills as well as teaching new ones, so prior programming knowledge does not have to be a limiting factor in choosing a new language.
Additionally, not all tools are equally accessible. Some are free while some require expensive licenses. Depending on the size of the company, getting a license for a whole team may be cost-prohibitive. For this reason, the languages and tools that a team is already working with is an important consideration in choosing what skills to develop. Researching the languages used by the companies a candidate is interested in can help them make a wiser choice when it comes to developing software skills and statistical analysis.
There are certain features to avoid when choosing a programming language. For instance, if you’re going to be working with massive data sets, a language like R that analyzes the data in memory (rather than in the cloud) will not be the best option, since the data manipulation will then be extremely slow. If you’re looking to work cross-functionally with software engineers or build a tool into an existing project, languages like Stata and Matlab that only focus on numerical analysis will not be the best option. Avoid languages that don’t fit in with the current company and product, as well as languages that are too time-intensive to learn relative to their utility. Being able to use these programming languages to support your data analysis is the goal.
Options for learning
There are a variety of ways and models for someone to pick up technical skills, including free online courses, a bootcamp, a master’s program, or on-the-job. Data science entrepreneurship is an exciting new field, and many data scientists are taking it upon themselves to develop new skills on their own.
Free online courses, such as Coursera and EdX, offer ways for data scientists to develop new skills in data analytics. While these options are extremely flexible and remote, they are generally better for someone with existing software skills to brush up on them. The short-term nature of the courses, as well as the lack of one-on-one mentoring, makes it difficult to learn a new language from scratch.
Bootcamps are an opportunity for prospective data scientists to develop their own programming skills. There are data science-specific bootcamps, such as Galvanize and Metis, that teach the fundamentals of statistics and machine learning. These programs also teach software skills like Python and R. However, for more general and rigorous software skills,computer programming bootcamps are an excellent option. These bootcamps are models that help you increase your ability to use these software languages in your work. Coding bootcamps, however, don’t delve as far into data-specific topics, but they do teach more languages. Learning to code is a fundamental that has wide-reaching impact on your career as a data analyst.
Pursuing a master’s degree can help a data scientist obtain the software skills that will best benefit their careers. There are many models for how to acquire this knowledge. An effective way of deepening your skill set is through taking a program. Master’s programs can take students with limited software knowledge and turn them into data scientists. For example, the UVA MSDS requires only introductory programming, and teaches them proficiency in Python, R, SQL, databases, and more. Master’s programs are typically longer than bootcamps, but they provide a solid foundation for learning software skills.
How to choose between a career in data science or software engineering
It can be daunting to imagine picking up as many technical skills as a software engineer, particularly for someone who doesn’t have a technical background. Fortunately, data scientists don’t require the same technical expertise as software engineers, while they might benefit more from business intuition and communication skills. Additionally, someone can enter the data science field without a background in software. Only 19 percent of data scientists hold an undergraduate degree in computer science. To gain the technical skills necessary to move into data science and data analytics, UVA’s Online MSDS program is ideal for helping a candidate build the tools they need. You’ll also learn the fundamentals of statistics and machine learning if this is knowledge you need to pursue your career goals. With courses in programming as well as data mining and statistical inference, a graduate of UVA’s Online MSDS is well qualified for a career in data science. To learn more, visit our website.