five tips to begin your career in the field of data science

Data-driven job roles are growing extensively lately, encouraging the youth to educate themselves and skill up for different domains such as logistics, business intelligence, machine learning, data architecture, and data science. Data science is one such core job that has witnessed visible growth and given career opportunities to various professionals skilled in coding, analytics, maths, statistics, and data visualization. Opting for a career in data science seems lucrative today as it is in-demand across retail, government, banking, media and communications, transportation, healthcare, education, and various other industries. 

Although, freshers are not expected to demonstrate immense expertise and work experience in the field, few important things could strengthen your chances of landing your first job as a data scientist. Here are 5 tips that you must take into consideration to kick-start your career in the field of data science and make your application stand out. 

1. Hone relevant soft and industrial skills: Data scientists are expected to solve complex real world problems based on data trends and patterns and thus, a combination of soft skills and specific profile or job-related skills is required. Understanding of data science fundamentals, statistical skills, programming knowledge, predictive modelling, data visualization, data manipulation, and data analysis, is what you must excel in. Basic knowledge of machine learning, deep learning, big data, and software engineering is also essential. You should also possess team work, time management, collaboration, communication, structured thinking, problem solving, and management skills to justify your candidature for a fresher job in data science. 

2. Learn from the experts - choose a beginner level course or specialisation: During your undergraduate period, if you are uncertain about your interests and are still on an exploration spree, you could enrol in a beginner friendly, short term, and affordable data science training and acknowledge your confusion. Whereas, in case you are extremely sure and enthusiastic about pursuing a career in data science, a 4-6 months long comprehensive specialisation would be a great choice. It will strengthen your skills and give you hands-on experience while you work on projects and deal with continuous practice and assessments. You will also get an industry recognised certificate, placement assistance, and insightful sessions with industry experts that will validate your skills as a professional in the said domain. 

3. Build a professional portfolio: An incomplete, casual, or unorganised portfolio could hamper your chances of getting hired. Your portfolio contains many elements other than your CV and cover letter and each of these must vouch for your candidature. Relevant to the data science profile you are applying for, build a digital professional portfolio that can be easily shared across with recruiters. Showcase your familiarity with datasets, structures, statistics, models, and insights. Expand your portfolio by adding your career summary, personal information, list of skills, accomplishments, major and minor data science project details, resume, work samples, educational qualifications, professional development activities, and a reference list that could convince the recruiter why you are suitable for the role. 

4. Gain practical exposure through internships: After learning and practicing the beginner level concepts of data science by doing an internship would help you develop and refine your skill in data science. You get to apply your theoretical knowledge, build self-confidence, get the feel of working in the industry, increase your practical skills, and boost your motivation. An internship is the best way to earn an extra income while you are still learning and polishing your job-specific skills, improve your CV, build a network, get a pre-placement offer or land jobs in other companies with the help of your seniors, gain work experience, and receive a recommendation from your employers. Internships give you an edge and benefit over your co-applicants as recruiters usually gauge your ability to multitask, commit, own your work, and excel in your field based on your performance with the previous employers and your practical work ability in technical fields like data science. 

5. Keep yourself updated with the latest industry trends: If you are an aspiring data scientist, it is essential for you to stay up to date with the latest industry trends, technological advancements, best practices, customers’ behavioral changes, and global activities in your field. Stay connected with the programming and data science communities, watch tutorials and take inspiration from experts’ work, read and share relevant articles, provide valuable feedback, and attend webinars and conferences by tech leaders. Your understanding of general happenings, knowledge of the field, and familiarity with different data science leaders impresses your recruiters and helps you get your favourite job opportunity. Moreover, this also helps you get recommendations to improve your work, build lifetime relationships with peers in the industry, get direct job opportunities, and potential leads of recruiters.

Courtesy: Internshala Trainings (training.internshala.com) - e-learning platform to learn new-age skills from Internshala