In the late 2000s, data science surged to prominence and has since soared within the IT industry. Notably, the Data Science Course Fees has become a significant consideration, mirroring the acceptance it has gained. Data science empowers organizations to explore new markets, optimize costs, make predictions, and gain a competitive edge over others.
The Analytics Insight survey forecasts that data science will create 3,037,809 new job openings by the end of 2021. The role of a data scientist in any organization is significant, and companies are ready to pay top bucks for data science professionals. Nearly 60% of recruiters find it difficult to hire talented professionals for data science and analytics-related jobs due to the dire shortage and high demand for skilled data scientists.
Even though it may seem complicated, even with zero experience in the data science industry, you can land an entry-level data scientist role if you possess the correct skillset. With data science evolving fast and the increase in career options and job roles in this field, It is an excellent time to jump the bandwagon if being a data scientist is the right career for you.
Below are seven tips to help you land a job as an entry-level data scientist:
1. Learn and brush up on your math to build a solid foundation
Mathematics and coding form the heart and soul of data science. Make sure you brush up on the foundations of analysis which relies on your knowledge of finding trends and correlations and trends, plotting different graphs, and analyzing charts.
Some mathematical concepts like statistics and probability theory, linear algebra, hypothesis testing, statistical modeling, bayesian analysis, regression analysis will help you write efficient code and get better results.
2. Learn programming languages and explore different libraries
Basic knowledge of programming languages is vital for a data scientist. Learning programming languages like Python and R have libraries that enable you to filter and transform big data and unstructured data, perform complex mathematical calculations, and data visualization. Working with SQL will give you the added advantage of working with relational databases. Python and R have libraries and toolkits which help you build complex models and analyze data.
3. Get hands-on experience
Landing an entry-level data science job with no relevant work experience seems like an impossible task. But fear not, you can gain relevant real-world work experience by working on projects. Working on some simple and relevant data science projects will help you showcase your skills and understanding of data science concepts to hiring managers. Another excellent way to showcase your skills is to work and contribute to open-source projects. Contributing towards open-source projects helps you experience and learn at the same time this will help you be a part of the vast open source community, gain connections while having fun, and work on projects that pique your interest.
Hackathons and coding competitions are also great ways to gain recognition for your work, win monetary prizes, and apply your skills to solve real problems.
4. Get internships
Besides working on projects and participating in hackathons, internships are a great way to get real-world work experience. Internships can help you build your knowledge base and skill set while working on industry-relevant projects. You can use freelancing platforms like Upwork or social media platforms to apply for internships or part-time jobs.
5. Build a profile to showcase your skills
As an entry-level data scientist, it is imperative to showcase your work to prove your data science skills. Your portfolio is evidence of your work experience in projects and coding skills. Platforms like LinkedIn, Github, or personal websites help you display your work and talent set and give you a solid online presence. Writing blogs on the projects, you have worked on and explaining the technologies and libraries you used is another way to highlight your skillset. Blogging also allows you to showcase your writing and communication skills. It is also an excellent way to network, share your knowledge, and learn online from the data science community. Hiring managers look at your LinkedIn and Github profiles for coding activity and programming level, so making your codes and blogs available online is an excellent way to display your work and a ticket to landing your dream job.
6. Build connections and networks: join a data science community
Getting to know other data scientists is a great way to learn about different career options and opportunities. You can learn about your future company, team, work culture, industry requirements, and how to prepare for job interviews. Networking and talking to other people in the community can open doors for discussions and give you referrals for companies. Many companies offer incentives and monetary bonuses to their employees to look for emerging talent, and these employee referrals have a high ROI. You can also follow some notable data scientists on LinkedIn and Twitter for insightful conversations on trending data science technologies. Tweeting and retweeting is excellent way to contribute to these conversations and build your network.
7. Get used to rejections and do not give up.
Rejection by recruiters and companies is a part of the journey. Do not be disheartened. Keep yourself motivated, even when rejections pop up in your emails. There are plenty of data scientist roles. Hiring can be a slow process as the companies and recruiters find the right fit for a job role. Connect with other friends or alumni who are going through the same process, as it will help you share your frustrations and help motivate each other. You will get hired, and it is just a matter of time and constant effort.
Even though the road to being a data scientist seems complex and challenging, we hope to simplify the approach for an entry-level data scientist role. These tips help you understand the requirements for a data scientist job without prior work experience.
