Data Science continues to dominate as one of the most in-demand career paths in the world. Opportunities in data science continue to rise with the consistent demand for data scientists. A good data scientist should be able to analyze data, mine it, perform programming, and combine it with statistics to transform and visualize data.
With these insights, businesses can develop strategies to drive more profits or measure how well their company is performing. A data scientist role requires deep knowledge and out-of-the-box thinking from individuals to succeed individually and as a team.
But how can individuals get on this career path after graduation?
At O’Fallon Labs, many individuals frequently ask about the way forward after their data analytics degree. Even after graduating from a data science program/bootcamp or a 4-year data science college/university degree, does it make you job-ready? What should you do next?
What would it take to become successful if you’ve done the homework and are ready to hit the job market? Is success guaranteed overnight? What’s the first strategy to get your first job? If these questions plague your mind, here is our thorough guide to help you out.
Here’s What to Do Next...
1. Plan Your Career Path and Specialization
Data science is a vast field with multiple sub-niches. During your data science degree or data science program/bootcamp, you must’ve found what you love. So, it’s best to decide on a career path early on.
Ask questions like what do you want to become? Do your skills lean more toward data architecture or data analysis? Do you know the difference between a data scientist, data engineer, and machine learning engineer?
Solve all these queries, as it will help you build a specialized portfolio - which brings us to the next point…
2. Build a Portfolio and Resume
The importance of your data science portfolio can’t be neglected. As a fresh graduate, your portfolio will authenticate your skills. It’s the perfect way to showcase your skills and tell recruiters and hiring managers that you know what the field is all about. And most importantly, your skill set fits the requirements of the job.
Plus, it will polish your skills. Build your portfolio by starting with bootcamp projects and going all the way up to taking genuine, unsolved consumer problems and data sets from the various available resources (many of which are publicly available online) and solving them.
You can also work as a freelancer on platforms like Upwork and Fiverr. Remember to solve projects as close to the niche you want to specialize in as possible. This will highlight your core skills competitively.
Moreover, create a well-structured resume. A good resume should be short, tell your story, and highlight your core competencies magnificently. Most students don’t understand the art of creating a powerful resume.
Remember, a resume is the first step to qualifying for a job interview. So, hire professional help; a mentor. They’ll help you design the best resume that stands out in the eyes of recruiters.
Check out the best online data science programs, including a free course here.
3. Understand Yourself - Gain Self-Awareness
The million-dollar question is, “Would you hire yourself with the current skills and abilities you possess?” Be honest during this assessment.
If the answer is yes, you’re on the right path. But if it’s a no, you must work on yourself. You must understand your capabilities and limitations to improve yourself. Are your capabilities required in the data analytics job market? What type of problems can you solve with your skillset?
How will you prove that you can handle the job requirements if you aren’t self-aware? Are you technically strong? What about soft skills? A good data scientist should have a mixture of technical and soft skills, like:
- Soft skills - analytical mind, critical thinking, adaptability and flexibility, collaboration and teamwork, communication, problem-solving, and patience and persistence.
- Technical skills - algorithms, data engineering, NLP (natural language processing), deep learning, statistics, data analysis, data visualization, data mining, and programming languages.
You will answer most of these questions while working on your skills and portfolio. But for soft skills assessment, you will have to take mock or real interviews. The best way we suggest is to work with a qualified mentor who’ll highlight your strengths and weaknesses. That way, you can collect constructive feedback and regularly improve your technical and soft skills.
4. Understand the Job Market
Data science job opportunities vary from state to state. So, it’s essential to learn about your geographical job market. Furthermore, it would be an excellent approach to list the type of companies you are interested in working for.
Are you looking forward to working with startups or big corporations? What type of roles are you interested in? Technical or people-path? Conduct thorough research on companies and the people working in them before you decide to join them.
Plus, all this research will define your market value - in terms of salary and importance to the data science market.
5. Keep Applying for Jobs
Don’t stop applying for jobs. Chances are, you will receive a call from a few places. Go to these interviews and collect feedback. Don’t expect to get hired as a fresh graduate in the first few interviews.
These interviews will make you understand how this process works and allow you to prepare better for it in the future. Absorb the feedback. Learn from these failed job interviews and get better in the next one. Again, seek help from a great data science mentor who knows this process and will help you prepare and breeze through it.
A data science degree or online bootcamp isn’t enough to score your first job. Hiring a mentor who knows the process like the back of their fingertips is a plus for fresh graduates.
Instead of wasting time on the process of getting your first data science job and its intricacies, a mentor will cut right to the chase and help you get a job in less time. If you are new, passionate about DS, and want to learn data science, check out our free online course.