If you come from a background that does not involve computer science or high-level mathematics, the data science landscape may feel like a solid wall. It’s likely that you have mental models of algorithms, programming, and mathematical statistics that have made the transition to this fascinating set of interdisciplinary skills seem impossible. But, what if your non-technical experience could be your superpower? There may be unique insights from you or additional domain knowledge that organizations are looking for.
This article will serve as your complete guide for transitioning into data science from a non-technical background. We provide recommendation for you to think about, direction to new resources and show you how you can leverage your existing knowledge and experience. For those interested to transition into data science, we also provide you with information on how our Data Science Course can help you get there quicker.
Why Your Non-Technical Background is an Asset? (Not a Hindrance)
Before we dive into the “how,” let’s debunk the myth that a non-technical background is a disadvantage. In reality, it can be a significant asset.
- Domain Expertise: You’ve spent years immersed in a specific industry marketing, healthcare, finance, social sciences, even the arts. You have developed knowledge of that industry that is useful and valuable to data scientists, some of whom struggle to make sense of the implications of their models in the real world. You can parse complicated business problems into data problems, and also contextualize the results with meaning.
- Problem-Solving Skills: Most importantly, any role you have been you have been solving problems. This problem could have been finding the most optimal marketing approach to engage customers, streamlining the way a health organization handles a patient process, or, composing a great story. Whenever you put your thinking hat on you have been developing your problem-solving toolkit. At its core, data science is just problem-solving with data.
- Communication & Storytelling: Many data scientists are analytically brilliant, but weak in communicating and conveying the meaning of their results to a non-technical audience or decision-makers. Your experience brings clarity in the areas of presenting, persuading, and narrating (in the form of reports, presentations, or creativity), all important ways of bridging the data and the decision-makers. Often, people overlook your communication strengths but will seek them out when they need them.
- Curiosity & Learning Agility: The fact that you’re even reading this article demonstrates a key trait of successful data scientists: curiosity and a willingness to learn. Data science is an ever-evolving field, and continuous learning is paramount.
The Roadmap: Your Step-by-Step Transition
Transitioning into data science requires dedication and a strategic approach. Here’s your roadmap:
Step 1: Build Your Foundational Knowledge
This is where the “technical” part comes in, but don’t be overwhelmed. Focus on considerate the core concepts.
- Mathematics & Statistics: You may not have to be a full-fledged theoretical mathematician, but you will definitely need to have strong skills in basic statistics (descriptive statistics, inferential statistics, probability, hypothesis testing) and basic linear algebra and calculus (generally, algorithms, especially to understand machine learning and testing approaches). There are excellent sources of introductory material through sites like Khan Academy, Coursera, edX.
- Programming: Python is the best programming language for data science. Start with the fundamentals: variables, data types, control flow, functions, and object-oriented programming. Pay attention to libraries like NumPy for numerical work and Pandas for data manipulation. R is another programming language that is widely used, especially in academia and for statistical analyses; it also has some sections; but Python has wider applications overall.
- Databases (SQL): Data is stored in databases. Learning SQL (Structured Query Language) is imperative to accessing, manipulating, and understanding data locked in relational databases. This often is a first step for many budding data scientists.
Step 2: Immerse Yourself in Data Science Concepts
Once you have a programming and statistical foundation, dive into the specifics of data science.
- Machine Learning: This is the great exciting bit of data science. Discover the many types of machine learning (supervised, unsupervised, and reinforcement), common algorithms (such as regression, classification, and clustering), and how to evaluate model performance. You will get familiar with libraries like Scikit-learn (Python) and they will become your best friends.
- Data Visualization: “Raw data” is just numbers; data visualization gives meaning to those numbers. You will learn tools like Matplot and Seaborn (Python), or use a visualization tool like Tableau or Power BI.
- Data Cleaning & Pre-processing: With messy data, and messy data is realistic data, you must know to clean, transform, and prepare data for your analysis. This is called data wrangling, and is a vital, time-consuming, and an absolutely important skill.
Step 3: Gain Practical Experience (Projects are Key!)
Knowledge without application is just theory. Projects are your portfolio, your resume, and you’re learning ground.
- Kaggle: This platform is a treasure trove for budding data scientists. Compete, tackle datasets, and learn from others. It is a fantastic way to apply your skills to real-world issues.
- Personal Projects: Consider whether there are problems in your current or past industry that you could solve with data. Are there publicly available datasets that you can analyze in your area? Can you scrape data from websites and build a predictive model? These types of projects, especially ones that take advantage of your existing domain knowledge, will turn heads.
- Contribute to Open Source: Become involved with open-source projects. This is a great way to learn best practices and to learn from and work with other, potentially more experienced developers.
Step 4: Network and Learn from Others
Data science is a collaborative field.
- Join Communities: Attend meetups, webinars, and online forums. Platforms like LinkedIn, Reddit (r/datascience), and Stack Overflow are great places to connect, ask questions, and learn.
- Find a Mentor: A mentor can provide invaluable guidance, share their experiences, and open doors to opportunities.
- Attend Workshops & Bootcamps: Immersive programs can accelerate your learning and provide structured guidance.
Step 5: Consider a Structured Data Science Course
While self-learning is commanding, a comprehensive Data Science Course can afford a structured, in-depth, and efficient path to accomplishment, especially for those transitioning from non-technical backgrounds.
A well-designed Data Science Course offers several advantages:
- Structured Curriculum: It covers all the important topics in a logical development, ensuring you don’t miss any critical initial knowledge.
- Expert Instructors: Learn from skilled data scientists who can make available real-world insights and guidance.
- Hands-on Projects: Courses often take in practical projects that build your selection and provide hands-on experience.
- Career Support: Many plans offer career services, together with resume building, interview preparation, and networking opportunities.
- Peer Learning: Work together with fellow students, fostering a reassuring learning environment.
If you live in or are in the area of Boston or are searching for an acceptable online program, the Boston Institute of Analytics has a thorough array of Data Science Course options. Data science courses prepared to put you on a pathway to data scientist, will take you far beyond what others may say is data science.
They are programs that are inclusive and focus on where participants start and generally cover all the elements of data science, and teach further levels of insights. i.e. “You can take one exam with little or no background knowledge of data science and become a data scientist, with just this ONE experience of your course with data, and, its possible with (BIA)-. A Data Science Course from a level institution like the Boston Institute of Analytics may just be what you needed to reach your future goals, aspirations for career development, and an attractive employment qualification for a better job.
Leveraging Your Unique Story
As you build your skills and experience, remember to weave your unique background into your narrative.
- Resume & LinkedIn: It’s important not to be ashamed of your old experience. Instead, focus on the transferable skills. In your old role, were you solving problems? Interpreting data (even if it was in an unofficial capacity)? Engaging in strategic thinking? If so, you can position your previous domain expertise as a differentiator.
- Interviews: When someone asks about your non-technical background, articulate the reasoning behind your strength as a more well-rounded data scientist, specifically in your position to facilitate and “translate” between the technical and business team while leveraging your communication abilities and domain expertise.
- Portfolio: Build an impressive portfolio of projects that show your technical expertise, ideally showing how you can apply data science to challenges in relevant problems in your previous industry.
Final Thoughts
Making a career shift to a data science job from a non-technical position is more than simply possible. It is probable that many of the successful data scientists have actually made a similar transition. Making the leap is not a simple, “just do it,” move. A data science career transition requires commitment to learning, commitment to understanding, and a clear plan for action. Follow your unique thinking style, develop your programming and statistics skills, create your portfolio of projects, and follow a formal data science class such as the courses provided by the Boston Institute of Analytics.
The job market for data scientists is expanding at a rapid rate and employers are beginning to appreciate the value of different backgrounds and experiences. Your experience with articulating the “why” behind the numbers, your ability to express your findings distinctly, and your capacity to consider complex problems differently may have you as a worthy candidate. Go ahead, take a leap of faith. Start learning, start building, and take action on a new and exciting phase of your life in data science. Your data stories are out there waiting for you.
