How to become a data scientist with non-technical training

The global market revenues from data science activities are expected to grow by leaps and bounds in the future. And hence, it is no wonder that the demand for data scientists in various industrial roles is increasing in proportion to the growth of the market. But the main question is, how do you start a career in data science?

While there are specialized technical courses that can be taken if one has technical training, things may not be the same for someone with non-technical (non-engineering) training. At the same time, given the gap between existing skills and required skills, it will be some time before a non-techie finds the perfect fit in the data science market. Nonetheless, those interested can still achieve professional success with or without technical training.

But why work as a Data Scientist?

As data becomes a critical asset for the digital transformation pipeline, companies are looking for talent with data skills that can help extract actionable insights from data for business growth.

Meanwhile, there is an acute shortage of people with data science skills. According to Fractal Analytics co-founder and CEO Srikanth Velamakanni, there are two types of talent shortages: data scientists – who can perform analytics and analytics consultants – who can understand and use data. He reiterates that the supply of talent for these job titles, particularly Data Scientist, is extremely scarce, and the demand is huge.

In addition, data science includes several sub-roles, each with lucrative salaries and very rewarding careers.

A slice of the daily work of data scientists

The job of a data scientist is to predict potential trends, explore disparate and disconnected data sources, and find better ways to analyze the information. Using a combination of programming, statistical skills, and machine learning algorithms, data scientists can explore large amounts of structured and unstructured data to identify patterns. A data scientist can explore and examine data from multiple disconnected sources and perform data mining using APIs or creating ETL pipelines.

Data scientists also perform the data set cleansing process to separate data relevant to a particular problem statement for better accuracy of results. They are also responsible for determining the most optimal models and algorithms for the problem based on the data requirements.

Basically, a career in data science may seem daunting at first, but it is not unattainable. With resources like video courses, eBooks, Stack Overflow, GitHub, hackathons, meetups, and more, most of which are free and open, it’s not hard to start at least. It all depends on passion, hard work and interest.

So what if you don’t have a technical background?

Start from zero!

While one may not have had the opportunity to work with data, one can begin by understanding how data is exploited by organizations and their industrial applications. Then a program can be organized to prepare for the required technical skills.

For example, to learn more about programming languages ​​and other key concepts, one can sign up for courses. Online platforms like Udacity, KDnuggets, Dataquest, and many more already offer online data science courses. You should also become familiar with basic mathematical concepts such as linear algebra, calculus, probability and statistics. This is important because while data science tools and technology will continue to evolve rapidly, the underlying mathematics will not.

One can also enroll in data science certification programs. Getting certified can improve your skills and increase your chances of being a better candidate in data science. Potential certifications include a Certified Applications Professional, a Certified Cloudera Professional: Data Scientist, EMC: Associate in Data Science, and a SAS Certified Predictive Modeler using SAS Enterprise Miner 7.

Help for real projects!

Gaining hands-on training and experience is the cornerstone of getting a data science job at top companies. This requires focusing on building a portfolio of projects focused on solving real world bottlenecks and inefficiencies.

Obviously, there will be many applicants for the same data scientist position. So opting for more focused project-based learning is a sure way to stand out in a crowd than the academic route. These projects also highlight its ability to transfer theoretical skills in the creation of data models with an impact on society and industry.

The internet is already full of free datasets that can be used for various types of projects such as criminal records, census reports, number of causes of death, etc. These projects can be based on an online course, a sole proprietorship, or led by a mentor. You can also host project work on GitHub to receive expert feedback or write content around it on Medium or on a personal blog. Either one can help increase visibility and increase the chances of being noticed by a recruiter.

Besides projects, one has to participate in various hackathons and other coding contests run by online sites like Kaggle. In addition, it is also necessary to invest time to attend data science events like Strata Conference, KDD and join data science communities like Datatau.

Find a mentor

Taking a course in data science can seem daunting and overwhelming, especially at the start of a new career. However, finding a good mentor can make a huge difference between trying to find a job, preparing for an interview, and working your first day as a data scientist. A mentor guides not only on all the actions to be taken in his career, but also helps to secure the candidate’s future through networking. They also offer insider tips to the industry after getting a high paying job in data science. They can serve as a bridge where one can channel ideas to senior executives and industry leaders and also receive feedback.

Therefore, finding good mentors can ensure long-term career advice when needed. And when the time comes, you can return the favor by being a good mentor to other newcomers!

To suppose, if you are planning to start a career in data science, don’t be afraid to explore your abilities. Figure out who you fit into and what your shortcomings are, and take the necessary steps to propel yourself into a competitive talent pool. Develop required skills, deploy learning in real use cases, receive healthy feedback, never hesitate to ask for help, and lastly, never stop learning!

Sean N. Ayres