{"id":23499,"date":"2023-08-14T00:08:00","date_gmt":"2023-08-14T07:08:00","guid":{"rendered":"https:\/\/www.springboard.com\/blog\/?p=23499"},"modified":"2023-09-25T23:23:16","modified_gmt":"2023-09-26T06:23:16","slug":"software-engineer-data-scientist","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/software-engineer-data-scientist\/","title":{"rendered":"Switching Gears: Software Engineer to Data Scientist"},"content":{"rendered":"\n

Software engineers possess a strong foundation in programming, algorithm design, and problem-solving that gives them the potential to transition smoothly into a data science role. While pivoting your career is a huge decision, data science can be a promising new avenue to explore for analytical and detail-oriented software engineers. Trillions of gigabytes of data daily flow through corporate hands every day, and data scientists are needed to interpret it.<\/p>\n\n\n\n

When it comes to making the leap, you may have some pressing questions about how to do it and what it may mean for your career. Questions like: How do software engineering and data science intersect? What are some skills that I have that will carry over? What will I need to learn to pivot from software engineering to data science? <\/p>\n\n\n\n

If you\u2019re asking yourself these questions, then you\u2019re in the right place. Below, we\u2019ll tell you everything you need to know about making the career pivot from software engineering to data science. <\/p>\n\n\n\n

Is It Easy for a Software Engineer To Transition Into a Data Scientist Role?<\/h2>\n\n\n\n

Yes and no. Software engineers<\/a> can translate some of their overlapping knowledge in programming languages and development structures to data science. While data science is not hard to learn<\/a>, it uses industry-specific software, some with steep learning curves.<\/p>\n\n\n\n

How Similar Are Software Engineering and Data Science?<\/h2>\n\n\n\n

Software engineering and data science are quite different in their scope of work, but the skills and tools in each can translate to the other. Software engineers utilize both front-end and back-end programming languages to construct a website or an app. A data scientist, on the other hand, obtains, cleans, explores, models, and interprets big data that may be used by a corporation to make an informed decision. You can expect to use languages like Python and JavaScript as well as tools like GIT\/GitHub in both. The biggest differences are the ways they approach and solve problems.<\/p>\n\n\n\n

How To Make a Career Transition From a Software Engineer to a Data Scientist<\/h2>\n\n\n\n

A career pivot can be daunting, so this step-by-step guide will make a clear path from software engineering to data science. Here are the steps to take:<\/p>\n\n\n

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    Ensure It\u2019s the Right Move for You and Make a Plan<\/p>\n

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    Learn the Foundational Concepts<\/p>\n

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    Invest in Learning and Training<\/p>\n

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    Build and Hone the Essential Skills<\/p>\n

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    Familiarize Yourself With the Requisite Tools<\/p>\n

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    Gain Relevant Hands-On Experience<\/p>\n

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    Create a Strong Data Science Portfolio<\/p>\n

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    Find a Mentor<\/p>\n

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    Work Toward the Job You Want<\/p>\n

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    Ensure It\u2019s the Right Move for You and Make a Plan<\/h3>\n\n\n\n

    Before you dive into a career change, it\u2019s a good idea to have insights from those who have experience in the field. Communities and forums such as Reddit<\/a> are great for this. A community can be a great tool to ask questions that need a nuanced answer. You might even be able to get insight from someone that has made the transition from software engineering to data science.<\/p>\n\n\n\n

    After doing some research, make a plan to enter the field that takes your lifestyle and goals into consideration. To create a plan, you may need to unpack what your personal goals are. You can start by asking yourself, with community support, questions like, \u201cDo I have a specific niche, industry, or company in mind?\u201d<\/p>\n\n\n\n

    Understand What You Need To Learn<\/h4>\n\n\n\n

    See what a data scientist does<\/a> to find specifics of what you need to learn. Then, make a list of what you already know and what skills you’ll need to pick up. Though transferable skills will give you a leg up, there’s industry-specific knowledge that you’ll need to master too. Many data science skills aren’t used in software engineering because of their technical differences.<\/p>\n\n\n\n

    Give Yourself a Realistic Timeline<\/h4>\n\n\n\n

    Career pivoting takes time, so it\u2019s important to be gentle and patient with yourself. You’re learning some completely new skills, some that, even with parallel knowledge, can take time to master. This transition will only happen with consistent effort over time. The timeline that you give yourself will also determine your course of study.<\/p>\n\n\n\n

    Learn the Foundational Concepts<\/h3>\n\n\n\n

    When learning the fundamentals, a good place to start is with the data science process<\/a>. These steps range from being broad, like compiling data, to specific, like using certain modeling software. These workflows are the backbone of data science and will be necessary to understand.<\/p>\n\n\n\n

    Other foundational knowledge includes concepts from data analytics, machine learning, computer science, and artificial intelligence. Being familiar with each of these will set you up for success.<\/p>\n\n\n\n

    Invest in Learning and Training<\/h3>\n\n\n\n

    Data science learning and training vary in price and time to complete. While you can’t major in data science at the undergraduate level, a degree in computer science teaches important foundations. Some job postings prefer a Master’s or Ph.D. in related fields, meaning that many learning data science already have degrees. Despite an employer’s preference, though, having a degree isn’t mandatory to enter the field, as a strong portfolio can bridge the gap.<\/p>\n\n\n\n

    Here are a few ways to make the leap:<\/p>\n\n\n\n

    Bootcamps<\/h4>\n\n\n\n

    A data science bootcamp<\/a> requires an upfront investment, but is also the quickest and most structured way into the field. Oftentimes they’ll include a job guarantee upon completion.<\/p>\n\n\n\n

    Online Courses<\/h4>\n\n\n\n

    Online data science courses<\/a> differ from bootcamps in that they tend to be entirely online and asynchronous. Courses offer subtopics that you can study at your own pace to fill in gaps or niches down. You can find some free self-paced courses, but you probably won\u2019t be assigned to a mentor. They are a great way of getting a general grasp of the field before committing to a paid resource or as a means to specialize in a data science topic.<\/p>\n\n\n\n

    Certifications<\/h4>\n\n\n\n

    Certifications tend to cover more specific topics than you\u2019ll find in a bootcamp, and demonstrate your abilities to employers. Harvard,<\/a> MIT<\/a>, and, CodeCademy<\/a> all have popular certification courses.<\/p>\n\n\n\n

    Blogs, Websites, and Books<\/h4>\n\n\n\n

    Literature is always a friend when learning a new skill. Pick up a data science book, read a published data science blog<\/a>, or glean some insights from a trusted Substack newsletter.<\/p>\n\n\n\n

    Build and Hone the Essential Skills<\/h3>\n\n\n\n

    When transitioning to data science<\/a> from software engineering, it\u2019s essential to consider which skills will transfer, and which ones you’ll need to learn.<\/p>\n\n\n\n

    Transferable Skills From a Software Engineer Role<\/h4>\n\n\n\n

    There are numerous software engineering skills<\/a> that are transferable to data science. Here are several that will be an asset:<\/p>\n\n\n\n