CAREER 101: HOW TO BECOME A DATA SCIENTIST WITH NON-TECHNICAL BACKGROUND
Source – https://www.analyticsinsight.net/
Yes, having a technical degree helps, but you can also pursue a rewarding career in data science with non-technical background
The global market revenues from data science activities are set to grow in leaps and bounds in the future. And hence, it is no wonder that the demand for data scientists in various industrial roles will rise in proportion to market growth. But the main question is how to get started for a career in data science?
While there are specialized technical courses that can be pursued if one has a technical background, things may not be the same for someone with a non-technical (non-engineering) background. At the same time, given the gap between existing skills and required skills, it will be sometime before a non-techie finds a perfect fit in the data science market. Nevertheless, interested individuals can still succeed professionally with or without a technical background.
But why work as a Data Scientist?
As data becomes an essential asset to the digital transformation pipeline, companies are seeking talent with data skills that can help derive actionable insight from the data for business growth.
Meanwhile, there is an acute shortage of people with data science skills. According to the Co-founder and CEO of Fractal Analytics, Srikanth Velamakanni, there are two types of talent deficits: Data Scientists – who can perform analytics and analytics consultants – who can understand and use data. He reiterates that the talent supply for these job titles, especially Data Scientist is extremely scarce, and the demand is enormous.
Besides, data science includes several sub-job roles each with lucrative salaries and highly rewarding careers.
A Slice from Daily Work of Data Scientists
A data scientist’s work involves predicting potential trends, exploring disparate and disconnected data sources, and finding better ways to analyze information. By using a combination of programming, statistical skills, and machine learning algorithms, data scientists can excavate through large amounts of structured and unstructured data to identify patterns. A data scientist can explore and examine data from multiple disconnected sources, and carry data mining by using APIs or building ETL pipelines.
Data scientists also perform the cleaning process of the datasets for separating the data which is relevant to a particular problem statement for better accuracy of the results. They are also assigned to determine the most optimal models and algorithms for the problem according to the requirements of the data.
Basically, a career in data science can seem intimidating at first but it is not inaccessible. Thanks to resources like video courses, eBooks, Stack Overflow, GitHub, hackathons, meetups, etc., most of which are free and open, it is not difficult to get started at least. It all depends upon passion, dedicated hard work, and interest.
So What to do if you are from Non-Technical Background?
Start From Scratch!
Though one may not have had the exposure to working with data, one can begin with understanding how data is being leveraged by organizations and its industrial applications. Then one can curate a curriculum to prep oneself with required technical skills.
For instance to learn about programming languages, and other key concepts, one can register for courses. Online platforms like Udacity, KDnuggets, Dataquest, and more, already offer online courses in data science. One must also be acquainted with basic mathematical concepts like linear algebra, calculus, probability and statistics. This is important because while data science tools and tech will continue to change rapidly, the underlying math will not.
One can also enroll in certification programs for data science. Earning a certification can improve one’s skills and boost the chances of being a better data scientist candidate. Potential certifications include certified applications professional, Cloudera certified professional: data scientist, EMC: data science associate and SAS certified predictive modeler using SAS Enterprise Miner 7.
Real-Life Projects Help!
Gaining practice training and experience is the linchpin of securing a data science job in the top reputed companies. For this, one must focus on building a portfolio of projects that focus on solving real-world bottlenecks and inefficiencies.
Obviously, there will be many candidates eying for the same data scientist job position. So, going for more focused project learning is a sure way to stand out in a crowd than the academic route. These projects also highlight one’s ability to transfer theoretical skills into the creation of data models that have an impact on society and industry.
The internet already has vast troves of free datasets that can be used for various kinds of projects like criminal records, census reports, cause of death count, etc. These projects can either be online courses based, individual undertaking or mentor-led. One can also host the project work on GitHub to receive feedback from experts or write content around it on Medium or a personal blog. Either can help in increasing visibility and boost one’s chances of being noticed by a recruiter.
Apart from projects one must participate in various hackathons and other coding competitions conducted by online sites like Kaggle. Additionally, one must also invest some time in attending data science events like Strata Conference, KDD and join data science communities like Datatau.
Find a Mentor
Charting a course in data science can seem daunting and overwhelming, especially when starting a new career. However, finding a good mentor can mean a huge difference between trying to find a job, preparing for an interview and working one’s first day as a data scientist. A mentor not only guides on what set of actions one must take in one’s career but also helps in securing the candidate’s future via networking. They also offer insider industry tips after one has fetched a well-paying job in data science. They can act as a bridge where one can channelize ideas to top executives and industry leads and also receive feedback in return.
Therefore, finding good mentors can ensure long-term career advice when needed. And when the time comes, one can return the favor by being a good mentor to other new-comers!
To surmise, if you are planning to start a career in data science, do not be afraid to explore your abilities. Figure out which persona you fit in and which are your gaps and take necessary action to propel oneself ahead in a competitive talent pool. Develop requisite skills, deploy learning in real-life use cases, receive healthy feedback, never hesitate to ask for help and lastly, never stop learning!