How can you transition to a data science career without a technical background?

Self-assessment regarding what aspect in data science intrigues you the most and start with basics. I wouldn't suggest anybody for a transition of 20 days and so which is a buzz in social media rather build a foundation. Understanding your preferences will guide you in setting a goal and remain persistent.

1. Learn the Fundamentals: Start with math and statistics, focusing on key concepts like probability, statistics, linear algebra, and calculus. 2. Programming Proficiency: Master Python, a key language in data science. Learn data manipulation libraries like NumPy and Pandas. 3. Data Science Tools: Familiarize yourself with data science libraries such as Scikit-Learn, Jupyter Notebooks, and SQL for data analysis and manipulation. 4. Build a Portfolio: Create data science projects to showcase your skills. Document your work and share it on platforms like GitHub. 5. Online Courses or Bootcamps: Consider enrolling in online courses, bootcamps, or nanodegree programs to get structured learning and hands-on experience.

Starting a data science journey without a technical backdrop might seem daunting. Begin with core programming essentials like Python, R, and SQL. Beyond coding, delve into data manipulation, visualization, and foundational statistics. Familiarize yourself with basic machine learning algorithms, such as linear and logistic regression. Tackling real-world problems, like fraud detection, reinforces understanding. Use online courses and books to guide your foundational journey. The strong base you establish now will shape your future in data science, ensuring every concept you grasp integrates into the larger framework.

Entering data science from a non-technical field? Your portfolio becomes pivotal. To stand out, curate a diverse set of projects spanning different domains. Go beyond mere models; develop end-to-end solutions. For instance, if you create a machine learning model, ensure it’s deployable, with a user-friendly interface. This showcases not just technical know-how, but an understanding of real-world applications. Such a holistic approach demonstrates your commitment and versatility, making your transition into data science compelling and noteworthy for prospective employers or collaborators.

If one struggles to find project ideas or useful datasets, Kaggle can be a great platform to start. It provides datasets, competitions, and a strong community.

embarking on a data science journey requires more than skill acquisition. As you build diverse, end-to-end projects, it's vital to magnify your reach by expanding your professional network. Attend seminars, join data-centric forums, or partake in hackathons. Engaging with industry experts not only provides learning opportunities but can also lead to job referrals. When applying, highlight your unique perspective stemming from a varied domain experience, and how it enhances data interpretation. This combination of networking, a rich portfolio, and a distinctive background can make you a valuable asset in the data science realm.

Applying for jobs in the world of data science can be challenging, especially in cases where you need to showcase your coding and hard skills. A good tip for this is practice, LinkedIn Learning courses with exams, Kaggle and HackerRank can help you feel more prepared for technical cases and interviews.

Needing a STEM degree / background to have a career in Data Science is one of the biggest misconceptions out there. Some of the best Data Scientists that I have worked with came from a range of disciplines, including linguistics, psychology, sociology and arts. There is much more to being a successful Data Scientist than maths, coding, and engineering - anyone can learn those. Problem solving, creative thinking, communication, teamwork, etc. are more important than having a technical background.

Transition to data science or data analytics is possible to anyone who has a good understanding of math and willingness to use it. In fact, you might be using the most needed skills already - problem solving, forming hypotheses, collecting evidence, testing and validating. Data science in its core is not about technology. It's about knowing how to efficiently find the root cause of the problem and possibly automate the process. At the moment, the market is overfull with juniors that completed a few online webinars but lack understanding of any business domain. On the other hand, there is a hole in the market for senior domain experts who are willing to share their knowledge with tech experts to solve complex problems.

As much as it’s important to learn mathematics and statistics to become a data scientist, you must be inquisitive, a critical thinker and a good story teller. You need storytelling skills to pass your message across. You need to turn those figures and graphs into something that’s relatable and understandable by the decision makers.