How Beginners Can Get Real World Job Experience Of Data Science
Data science is a lot like driving. To understand it comprehensively, one has to get down to the field to gain real-world experience and get their hands dirty with real professional challenges of data science. However, it gets challenging for beginners to gain real-world experience without getting an actual job in the industry. In fact, even landing on entry-level jobs can be daunting for beginner data scientists, as it comes with heavy requirements for professionals to have experience in data science. Thus, it becomes imperative for these beginners, recent graduates as well as professionals who are looking to translate into data science to find ways to gain some practical hands-on experience of the industry to stand out in the crowd. Here are a few ways beginners can get real-world job experience of data science before applying for an actual job: Also Read: Things Junior Data Scientists Need To Know Before Going For An Interview Building Personal Projects Building personal projects has always been key to creating a robust portfolio for data scientists. Not only it will help beginners to enhance their data science skills and confidence but also assist in exhibiting their practical knowledge and creativity to the potential employers. GitHub could be an excellent platform to showcase personal projects, and highlighting the same on the resume can improve the chances of beginners to land on a job. Handling a project involves tasks ranging from generating hypotheses, collecting, cleaning and analysing appropriate data, to creating predictive models and sharing data with organisation leaders. Thus, working on projects can give a comprehensive understanding of various topics to beginner data scientists. To enhance their skills and gain real-world experience, data scientists should focus on scripting clean codes with accurate data visualisation for making insights understandable to stakeholders. Beginner data scientists can also try their hand on projects based on computer vision, facial recognition technology, machine learning and GANs, to name a few. Also Read: How Does Project Management In Data Science Look Like? Contributing to Open-Source Projects Another way for beginners to gain real-world data science experience is by contributing to open-source projects. Participating in the open-source community can be overwhelming at the start, as data scientists would be required to provide code or solutions for a running project. However, it will indeed enhance the coding and technical skills of the individual and give them the experience to work on projects along with a team which would require constant communication. By contributing to open source projects, data scientists will be able to give back to society by providing code, which can benefit millions in building their projects. Open-source projects also require working with many data science libraries, like Pandas, NumPy, Scikit-learn and open-source distributed version control system, Git. Thus, working on these projects will make beginners familiarise with these aspects of data science. Contributing to open-source projects will also make amateurs a part of a community where they can network with individuals with similar interests, which will again help in enhancing the real-world experience. freeCodeCamp is one such community that helps nonprofits to code and build projects. Also Read: How Open-Source Projects Make Money? Start Small Although getting a full-fledged job can be difficult for beginner data scientists, freelancing or starting an internship could be an excellent way for them to gain some real-world experience. In fact, the COVID-19 pandemic has drastically revamped the gig economy, and many companies are now looking to hire freelancers as well as interns to avail data science skills for specific projects. Therefore, freelancing will not only help them enhance their technical skills but will improve their communication skills by engaging with different teams of the organisation. Freelancing and internship will allow these beginners to work on real-world business problems that companies are facing and will urge them to come up with a solution that is practical for the real world. This, in turn, will enhance their experience and make them more industry-ready. Freelancers even get a chance to accommodate many projects at once, which will make them work on diverse datasets, improving their multitasking skills. However sometimes, it gets challenging to market oneself enough to get a freelancing job, and therefore there are prominent platforms to search for freelance data science jobs are — Upwork, Toptal, Data Science Central to name a few. Freelancing and internship not only allows beginners and amateurs to experiment with different data science roles but also puts them in the limelight among potential employers. Also Read: How The Freelance Boom Will Impact The Position Of In House Data Scientists Participate in Hackathons & Competitions Participating in hackathons is another way of gaining real-world experience for data scientists, as these competitions force the participants to build applications that would be running in the real world. Thus, it will help beginners to learn the practical skills required for the actual job as well as will help them connect to a network of professionals with similar interests and experts of the industry. These coding competitions and hackathons require participants to turn hypotheses into action which will provide immense benefits if mentioned on the resume. Alongside these hackathons and competitions runs for hours and hours at a go, which forces participants to work under pressure, thus teaching them to handle extreme stress. This, in turn, gives them an experience of the real-world scenario, where data scientists work on a deadline to solve their business problems. Apart from building models, data scientists also are required to bring in business value. Thus, these competitions would help them understand the criticality of their work for business outcomes. Hackathons and competitions also act as a testimony for these professionals’ skills and knowledge, and this makes it easier for companies to filter out the best. One such platform is the MachineHack by Analytics India Magazine, which hosts some of the exciting business problems for participants to solve using ML and data science techniques. Also Read: How Much Is Kaggle Relevant For Real-Life Data Science Get a Mentorship Lasts but not the least, getting a mentorship can prove to be beneficial for beginner data scientists to gain hands-on experience of the real world. A comprehensive mentorship can provide one-on-one sessions, which will allow the data scientists to pick the brains of experts from the industry. Also, a good mentor will give all sorts of real-world guidance that will be required for a beginner to thrive in this competitive market. Having a mentor is an open door into the data science community, which will provide an opportunity to network with data science professionals and potential employers from the industry. Apart from building skill sets, the mentor’s expertise will also help in gaining relevant feedback on the growth and development of the individual. This, in turn, makes them industry ready in no time. These mentors can also help them find jobs, and gain work experience, which will help these beginners to speed up their career graph. These industry experts are also equipped with real-world tools and techniques in AI and data science, which will expose mentees to relevant skill sets required to survive the job. Join AIM Mentoring Circle.
Data science is a lot like driving. To understand it comprehensively, one has to get down to the field to gain real-world experience and get their hands dirty with real professional challenges of data science. However, it gets challenging for beginners to gain real-world experience without getting an actual job in the industry.  In fact, […]
Here are a few ways beginners can get real-world job experience of data science before applying for an actual job.
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Sejuti currently works as Associate Editor at Analytics India Magazine (AIM). Reach out at sejuti.das@analyticsindiamag.com