Data Science (DS) has given us a unique insight into the way we look at data. There is a huge demand for Data Scientists who can extract useful insights out of large and complex datasets to influence business decisions. This is the right time to make the career transition from Software Developer to Data Scientist. You are at leverage for your next role with your passion and vision for data, backed up by your programming background and problem-solving attitude to business challenges.

Software developer to Data Scientist – logical approach

“A career transition from Software Developer (SD) to Data Scientist (DS) requires 3 aspects:

  1. Knowing your potential and present role
  2. Understanding of the responsibilities of a Data Scientist
  3. Bridging the knowledge gap.”
    Knowing your potential helps you focus on your key skills and responsibilities. After you learn what a Data Scientist does, you must analyze why you want to become one. What are the common tasks and goals you both share? Identify the data science skills that give you leverage and the ones you need to acquire. It’s easier to fill the knowledge gap once you realize your goal and what you are missing. Let’s dive in to explore these aspects from a Software Developer’s perspective transitioning into a Data Scientist.

Software Developer to Data Scientist Aspect#1: Focus on skills and responsibilities of a Software Developer

A Software Developer builds an enterprise software program. Manages end-to-end Software Development Life Cycle (SDLC) in a cross-platform agile environment.

Job responsibilities:

  • Design, code, develop, test and implement new software programs
  • Develop solutions and integrate them into products for real-world problems and drive better user experience.
  • Setup system and OS infrastructure.
  • Documentation and process improvements.
  • Work seamlessly as part of a multi-site, multi-cultural team.

Skills:

  • Technical:
    • Programming in Python, Java, C, C#, C++, and JavaScript
    • Data structures and algorithms
    • SDLC: Data gathering, Requirement analysis, coding, testing, and deployment
    • Methodology: Agile and SCRUM
    • Cloud Technology: Virtualization of Amazon AWS, Microsoft Hyper-V, and VMWare
    • Developer tools: Git, GitHub, Jira, Azure, and Atom
    • Database architecture and design: RDBMS, SQL, Pl/SQL
  • Analytical and problem solving
  • Computer Science fundamentals: Data Structures and Algorithms
  • Communication and visualization skills
  • Business knowledge

Many of the tasks already mimic that of a Data Scientist.

Aspect#2: Skills and job description of a Data Scientist

Data Scientist is a nerd who uses their analytical, statistical, and programming skills to collect, analyze, interpret and visualize large data sets.

They develop data-driven solutions to complex business challenges and make future predictions that affect business decisions. 

They usually have a degree in Math, Statistics, Computer Science, or the research field. A Master’s or Ph.D. is a plus.

Source: Glassdoor
Courtesy: KDnuggets

Leverage of being a Software Developer

As a Software Developer, you already have 2/3rd of the equation in place to become a Data Scientist, you:

  • Are a good programmer with the best coding and testing practice.
  • Have knowledge of SDLC in an agile environment
  • Maintain and collaborate code using VCS like Git.
  • Can build CI/CD data pipelines from DevOps practice.
  • Have good problem-solving and analytical skills
  • Are a Subject Matter Expert (SME) and understand the business process and user requirements.
  • Understand system infrastructure and architectural design

Shweta Bhatt, a Data Scientist at Youplus, talks about how her Software Developer background helped her career transition –

“As a Developer, your programming skills are going to be valuable, as you would be integrating your ML models (solutions) with the product. Knowledge of how the industry works using SDLC is an advantage.”

It’s essential to question yourself why you intend to be a Data Scientist? Is it the hype on various business magazines and job sites? Or is it the salary and career growth? Or does the nature of work excite you? The answer might be a collective yes, however, staying focused and consistent is the key.

Aspect#3: Bridge the knowledge gap by acquiring the missing Data Science Skills

For the remaining 1/3rd part of the equation, you need to –

  • Learn about backend Data management and database architecture and design.
  • Get involved in Data ETL (Extract, transform and load) methods to build continuous data pipelines.
  • Analytic SQL such as SQL for aggregation, analysis, and modeling 
  • Big Data, Hadoop
  • Scala
  • Learn programming in R and Python (libraries)
  • Data Science concepts such as Data Manipulation, Data Visualization, Statistical Analysis, and Machine Learning (ML).
  • ML techniques: K-NN, SVM, Decision Forests, Naive Bayes and Clustering.
  • Computer science concepts like performance complexity and implications of computer architecture like I/O and memory tuning.
  • Mathematical and Statistical concepts – Algebra, Calculus, Probability, Statistics, Regression
  • Business level end-to-end know-how

A shift from C programming to Python helped Shweta develop insights and interest towards datasets that inspired her to indulge in ML and DS courses. She further did a Master’s in AI and at present works on ML and NLP. 
Shweta says for non-technical professionals, domain knowledge is an added advantage, as DS is a multidisciplinary field.

Career transition – the final step

It is not tough to shift career from a Sofware Developer to Data Scientist, says Shweta. Clearing the myth about tools she says – “DS or AI is not all about tools. It is essential to understand and apply the concepts. Tools are essential to implement solutions and integrate them into your product.” You must put your knowledge into practice by solving problems with real-time datasets on popular sites like Kaggle and KDnuggets. Companies like Google and Facebook conduct competitions to prove your Data science skills, and bag the job based on your scores. The Data Science projects are evidence of your knowledge that makes your CV stand out. Proceed by applying for jobs on Company websites and popular job sites like LinkedIn, Glassdoor, Monster, Indeed, and Kaggle. 

While being interviewed, you must be prepared to justify your resume.
She says – “If you are from a technical background, you must be good at programming, ML concepts and must have proven knowledge in complex Data Science projects.” Business expertise with good communication and visualization techniques is also mandatory.

“As a Software Developer you inherently connect with product design, architecture, and infrastructure that you will deal with in a Data Scientist role,” says Shweta

Career Transition Path

“Breaking into DS requires you to be passionate about the field, have a stronghold of DS fundamentals. Choose an industry that interests you. You must be willing to constantly learn and upgrade your knowledge as it is an ever-evolving field. Your curiosity and the drive-in you is the right path towards Data Science.” Check out Springboard’s Data Science career track course to help you build your skills, develop a professional portfolio to grab your dream job. The Career Track is a self-paced, 1:1 mentoring-led and project-driven program that comes along with a job guarantee.