#1 Career Advice: Understand the Roles and Industries
Data science is an all-encompassing field. There are various roles within the field. Some of the predominant ones are:
Data scientist: Focus on statistical analysis and research; they also model the algorithm and prototype it for testing. Skilled in programming languages like SQL, Python and R, data platforms like Hive and Spark.
Data analyst: Works on finding patterns and drawing insights from data; it has a significant impact on business decisions. Skilled in tools such as Excel, SQL, R, and Tableau; also well-versed with the business context.
Data architect: Designs, creates and implements the architecture of data — creating models, defining rules, and organizing information at macro and micro levels. Skilled in database design, cloud computing, data mining, and ETL tools.
Data engineer: Builds the platform that captures, analyzes and stores data in an efficient and retrievable manner; Also writes ETL queries to enable data scientists to make sense of data. Skilled in SQL, Java, Hadoop, Spark.
AI/ML engineer: Uses ML techniques to solve real-life problems; they pick the right algorithm, and build, implement, test and optimize AI models. Skilled in Python, Scikit learn, Keras, PyTorch, etc.
Before picking a career in data science, explore what each role does:
- Carefully read job descriptions for roles across various companies.
- Think about the industries that interest you. Some industries, like finance and healthcare, are mature in applying data science while other sectors, like government, for instance, are still reluctant. Carefully consider where you might be the right fit.
- Compare job descriptions and see if there are differences across industries.
- Follow your dream companies and keep an eye out for their blogs, press coverage, etc.
- Follow bloggers/speakers in the sector.
- Find professionals in your network and speak to them.
Data science is a nascent field, the same designation might have completely different roles in different organizations. What one company calls an analyst, might be called an engineer/scientist in another. What would be a highly specialized role in, say, the fraud detection department of a bank, would be a generalized role in a product startup. So, ask deeper questions and understand the intricacies
#2 Career Advice: Pick the right focus area
Be a specialist, not a generalist. While it is good to have a broad knowledge of all areas and tools, the problems of the future will be more and more challenging. Specialists who have deep knowledge will be better equipped to handle these. So begin by narrowing your focus right away — ask yourself “what career is right for me?”. This could be speech analysis with NLP or data modeling with predictive analytics or self-driving cars with computer vision or any of oodles of possibilities.
A data scientist who has highly specialized skills in any of these or other emerging areas will be extremely valuable in 2020. So, equip yourself: Take courses in specific areas that appeal to you, and begin your pet projects.
#3 Career Advice: Equip yourself with the right tools
In your data science career, you’ll be working with a wide range of tools on a daily basis. This may range from programming languages, database management systems, data frameworks, libraries, and data wrangling tools to business intelligence and visualization software. Depending on the career path that excites you — after following the steps above — identify the tools you need and begin learning them.
To get you started, we have written about the top open-source tools to learn in 2020. You can learn them using resources on their respective websites — documentation, tutorials, code samples, datasets, etc. You might also find code base on GitHub for specific use cases. When in doubt, do not hesitate to reach out to the community online / social media.
#4 Career Advice: Join the conversation
Speak to people, both online and offline, about areas you know or want to become a part of. Online forums like /r/datascience and Data Science Slack community are great places to start, to keep yourself updated on the happenings in the field, find inspiration for projects, meet collaborators or seniors who can help you troubleshoot.
Offline, attend data science meetups happening in your locality. This will open your eyes to the real-world and also help you build your network.
#5 Find a mentor
For a beginner, the world of data science can be overwhelming. To help you navigate the ecosystem and ensure you don’t sink into the abyss, find a mentor and seek career counseling. A mentor will give you a glimpse of what to expect in the field — what roles exist, how they work, what they value, how much they pay, what you need to flourish etc.
#6 Test the waters
Even if you don’t find yourself entirely ready, apply for jobs to get experience of the market.
Build a strong resume. As you write your resume, make sure you mention all your relevant skills — Mathematical and statistical skills, programming skills, certifications, etc.
Build a portfolio. If you don’t yet have one, start immediately. Make use of the many datasets available for you to practice on. Write up a sample report and description for each of your projects and add your project profiles on your resume. This is the kind of forethought and initiative that will impress potential employers.
Get active on GitHub: Start posting code from your personal projects to GitHub. Add a README, which outlines your project, goals, approaches, specifications, etc. This way, you will be able to share your work and collect relevant feedback on it from experts. You must also keep updating your code and contributing to other projects regularly. Over time, it will not only demonstrate your coding skills but also your ability to collaborate effectively with the community.
Prepare for interviews. Keep in mind that many interviews today begin with a telephonic round. Read up, have opinions on the developments in the data science field, and treat interview questions and your own resume as talking points that you can elaborate on to impress the interviewer. Here are some of the most asked questions in data science interviews in India. In fact, Springboard’s data science career trackprogram is structured keeping in mind all these top data science career advice and more. We’ve made all our courses 1:1 mentoring-led and project-driven to give aspiring data science professionals the perfect combination of theory, practice, and guidance needed to shine in their careers. If you’re contemplating a data science career, explore Springboard today!