This is a very common question: "My background is not Data Science, but I'm passionate about it. What is the best way to get my foot in the door?"
"I learned about DS recently, and I fell in love with it. What is the best way to get my foot in the door and land my first AI / Data Science job?"
The Total Data market is projected to cross the $157 billion mark to $268 billion by 2026. In 2020, the global revenues for business analytics and big data were $210 billion. Over the years, the demand for data-focused professionals has increased.
Recruiters are openly reaching out and embracing these professional data science individuals to join their companies. But the problem is the gap between the current and required skill sets, which is broad - increasing the demand more than ever.
It doesn’t matter if you don’t have a data science background, it is still possible to get a job in Data Science. You're on the right path as long as you are willing to become a data scientist and close this skill gap. This blog will focus on how aspiring individuals like you, with no technical background, can become data scientists.
1. Acquire Data Science Skills
Okay, you love data science. However, the data science career path is complex as it involves different industries. The best way to become a data scientist is to upskill your skill set. The data scientist requirements state that you must master the fundamentals of the field.
This can only be done when your curriculum includes:
- Deep learning
- Big data handling and manipulation
- Programming basics (R, Python, Java)
- Statistics and probability
- Data visualization
Don’t, for once, think that you can do all this alone without guidance. Start looking for free data analytics courses with or without certificates to acquire experience-based skills. Industry leaders design these courses for aspiring individuals.
They also offer other perks like mentorship programs, career counseling, and more. Earning a data science degree typically takes 2 to 4 years, which is a long time. But there are different, more beneficial ways to learn all the essentials and gain skills and experience - [from 6-month online data science programs and bootcamps](<https://saeedmirshekari.com/blog/202
Before you choose any course, do ask yourself the following questions:
- Which of these programs extensively covers the topics you want to learn?
- Which courses/programs have the best reviews?
- Which courses/programs are affordable, and are they worth it?
- What are the success stories of the ex-students of these courses?
2. Find a Mentor
One-on-one, industry-leading mentors are the best ones to get you started and make you successful in the data science and analytics field. As a student, you can directly learn data analytics from these certified mentors.
Before you choose one, identify the techniques the mentor uses and the course topics they will cover. Additionally, check for added benefits, if any. Having a mentor when you don’t have a data science background will never let you deviate from the right track.
Go for mentors with more than 5 years of industry experience. Here’s why you need a mentor:
Networking implies building strong connections to gain visibility and benefits for your career growth. A stronger support network will highlight you in the eyes of recruiters quickly and increase your chances of getting interviews.
Mentors like Saeed Mirshekari can teach novices the mojo of networking. He will tell you how to talk, socialize, use networking apps to the fullest, and make better relationships with potential big names in the industry.
Check out Saeed’s results and accomplishments in the Data Science industry here.
Inside Industry Tips
Mentors are like a burning volcano or goldmine of industry knowledge. With years of hands-on experience, they know how to put crucial skills to use. They will teach you how to master soft skills, collaborate with others to achieve business goals and other tips some of which you never thought you needed to know.
A mentor becomes a guide not just throughout the program but also during the career span. Whenever students need strength or guidance, they turn to their mentors - no matter how much time passes! This shows that having a mentor means investing in a long-term, highly beneficial relationship.
3. Gain Real World Experience
Getting practical experience is the key to getting your first data science job in the best companies. This doesn’t mean you have to work on a futuristic project. Instead, you can work on smaller projects where you tinker and experiment with ideas and tools.
The portfolio you create will show your passion for the field and your knack for solving problems. Create your portfolio on GitHub and promote it on platforms like LinkedIn and Medium, where you can write about topics surrounding these projects.
This will help you gain visibility and attract recruiters. These projects you work on can come from:
- Courses you took - the best data science programs offer hands-on experience along with theoretical knowledge to develop technical skills. Typically, an industry leader will grade the project, which adds weight to your resume.
- Personal coding projects - mastering the required data science languages and working on personal coding projects is another great way to build technical skills. You won’t have to worry about grades or deadlines this way.
- Projects with your mentor - working on DS projects with a mentor means a foolproof way to enter the industry. Mentors offer invaluable inputs throughout your project lifecycle and spark your creative thinking even when you hit roadblocks.
- Internships - you can also get hands-on experience while interning for a firm. This will give you a lot of exposure while you work in a fast-paced environment.
4. Join Data Science Communities & Set Your Resume
You can be working in secret. Join communities and forums like Quora, where you interact with like-minded people. Answer queries to enhance your knowledge and ask questions to which you don’t have the answer. Don’t forget to work on your resume. Your resume will be the key to shortlisting you for interviews. So, add pointers to it carefully.
The Bottom Line
Not having a data science background should not stop you, if you want to enter the DS field. Your resilience, commitment, and hard work will define your success. If you’re looking for the best data science programs and data science curriculum, joining O’Fallon Labs will be the decision.