It was only twelve years ago that the title ‘data scientist’ was coined. The practice of data science has not existed for much longer either. As a young field, though growing rapidly around the globe, the role of a data scientist is often misunderstood. What exactly do data scientists do, how do they contribute to the business, what skills do they need are all commonly asked questions among aspirants — be it fresh graduates, entry-level professionals or mid-level managers seeking a career transition. Among all the questions we hear, there is one that’s heavily loaded: Is data science hard? While there is great enthusiasm and interest in the opportunities opened up by data science, there is also scepticism. In this blog post, we endeavour to answer all the questions around building a data science career.
Is Data Science Hard?
Our mentors and counsellors believe that this question comes more from a place of misunderstanding. Given that this field is still maturing, there are only a few mentors/ seniors in the field, and most traditional colleges still don’t offer data science as a structured course, it leaves aspirants with many unanswered questions. Let us answer them one by one.
#1 Will a data science job be hard?
A generalist data scientist is a jack of many trades. Their job includes:
- Identifying business problems
- Writing hypothesis
- Performing data mining and data wrangling
- Cleaning and classifying data
- Conducting statistical analysis
- Building algorithms and testing them
- Writing code and deploying in production
- Making presentations and visualisations
- Collaborating with business teams etc.
This might appear hard because it often is. As a data scientist, you’re responsible for creating the data-driven intelligence and predictions that are needed to make business decisions. This means that your output has to be both error-free and rooted in a real-world business context. For beginners, this can be challenging.
A good data scientist will take the challenge in their stride and develop the skills and aptitude needed for it.
#2 Is it hard to gain data science skills?
People ask this question because data science is not a singular skill. As far as technical skills go, data scientist jobs combine mathematics, statistics, programming and domain expertise. Being able to meaningfully bring together the relevant skills across these fields can be a hard task for young professionals. However, you must understand that these are all related skills, building on top of one another, making them a coherent whole.
For instance, let’s say you’re a data scientist at a bank. You need not know everything there is to know about banking. But you need to understand financial concepts, within the context of your project. If you’re using DS for fraud detection in credit card purchases, you need to have in-depth knowledge of the credit landscape and fraud patterns.
Data science skills are a lot more than that. Some other skills you need are:
- Critical thinking and problem-solving: Being able to critically analyse the situation at hand and device innovative ways to solve business problems.
- Business acumen: Understanding the real-world context in which the business works to make data-driven decisions to business problems.
- Communication: Making presentations, building consensus among stakeholders, persuading team members, etc.
- Self-learning: Being able to adapt to changing circumstances and keeping oneself updated on technological developments.
Once you have a foundational grasp of each of these skills, you will develop an intuition for data science, which you can hone with practice and experience.
#3 Is it necessary to have experience to start a data science career?
While experience is non-negotiable, it doesn’t have to be ‘job experience’. Hands-on practice with concepts of DS is good enough. You can gain this kind of experience in myriad ways:
- Data science projects, which you can use to showcase your skills/interests.
- Competitions and hackathons, which offer opportunities to solve real-world problems with data.
- Data science internships, which give you a professional environment to learn the practice of this field.
- Freelance projects, which allow you to take on small assignments and work independently.
- Online programs that include portfolio projects solving real-world problems.
The only way to show that you’re a data scientist is through practical application. Gaining this can be a little hard, but it is crucial.
#4 Is getting a data science job really hard?
Data science is a competitive field — recruiters are looking for the best candidates, making the interview process rigorous. The qualifications needed for these positions are also stringent. It is very unlikely that you go to a walk-in interview and walk away with an offer. This doesn’t mean that getting a job is hard.
- Make a CV that presents your strengths confidently
- Customise your CV to suit the job you’re applying for
- Present a portfolio of projects you’ve worked on, based on the job you’re applying to, present the relevant ones prominently
- Prepare for commonly asked data science interview questions and practice your answers
- Follow up diligently and seek feedback
Remember that sending the same templated CV or portfolio to all job vacancies is unlikely to get you interview calls. Customising your resume and presenting relevant portfolio is crucial.
#5 Is it hard to grow as a data scientist?
As an emerging field, career growth as a data scientist is still not set in stone. Without a clear growth path to follow, it might appear like an adventurous career path. However, this field opens the door to myriad opportunities.
- You can lead data science teams, training and mentoring young professionals in your team. If you have leadership aspirations and a knack/patience for teaching the next generation of data scientists, this path is perfect for you.
- Become a super-specialist in specific domains or use cases. For instance, being a data scientist in medicine might need you to gain in-depth knowledge of legalities and compliance. But once you do, you can consult with multiple organisations at high levels. If you already have domain experience, this is a great path for you. Here are some tips for making a seamless career transition.
- Build products leveraging data, like analytics engines, predictive tools, etc. If you have an entrepreneurial spirit in you, the world of data science is your oyster. You can build, scale and grow your own startup in the field.
- Move into machine learning/artificial intelligence and so on. After a few years in data science, you can take on more complex projects in predictive analytics, internet of things or edge computing too.
Figuring out how to grow within the data science field can seem difficult. Once you learn the possibilities and decide what you want, you can follow a clear path to success.
‘Hard’ is a subjective concept. What might appear hard for someone without the necessary skills/qualifications might be easy for someone with many years of experience. However, in data science, ‘hard’ takes a different meaning. As an emerging field, data science can be challenging and ambiguous. To navigate this, you need a mentor, who can answer your questions, identify gaps, offer career advice, and guide you in the right direction. It is for this reason that Springboard’s Data Science Career Track includes 1:1 mentorship, project-driven approach and career coaching. At Springboard, you will not only learn data science as a subject but also become a data scientist by the end. Check out the program now!