How to launch your data science career in 2022
With the growing demand of data science roles, from Data Engineers to Data Architects, companies are offering big bucks for top talent, reaching averages well over six figures in the US (Hong Kong salaries can be viewed here).
Data science involves collecting, analyzing and interpretating data in order to derive relevant insights for a given problem. So how does one go about getting into data science? Continue reading to find out!
Is Big Data right for me?
Data Science screams practicality. Building good machine algorithms is great and all, but data scientist focus on real-world application. Models must produce meaningful results in real-world situations. It takes patience, perseverance, critical thinking, problem solving, life-long learning, teamwork, ethics, and good communication to excel as a good data scientist. Hard skills include statistics, data organization / wrangling, machine learning and AI and data visualization.
How can I get started?
It generally takes 3-5 years to learn data science. Students can learn it during university, nonstudents can join coding bootcamps, fast tracking the skills and knowledge you need to get your foot in the door.
- Do your research and figure out a data science career path that you’d like to take: There are many data applications and roles in data science.
- Choose your learning path: Are you good at self-studying? Do you prefer university or your own paced bootcamp? There has been a lot of debate between university degrees, certificates and coding bootcamps pros and cons. Regardless, get familiar with coding / data science languages like Python (and its useful libraries like pandas and NumPy), excel, SAS and R.
- Build your portfolio: The best way to prove yourself is to work on projects, whether that be for work or for passion. Create a GitHub account to share your projects and build up your portfolio to share with employers / clients.
- Apply for jobs: Get internships, volunteer and work experience in junior roles to build up your experience and skillset. Check out our 7 essential tips to securing a job interview article for advice on job hunting.
- Prepare and tailor your technical resume.
- Showcase your best in your portfolio.
- Practice for the technical portion of the interview to demonstrate your skills.
- Continue your learning and stay up to date with trends: Subscribe to data science newsletters / alerts, read books, listen to podcast, watch videos, talk to people.
Top 13 Data Science roles and salaries (according to Springboard)
- Enterprise Architect – Average Salary = US$163,667 / year
- Data Architect – Average Salary = US$152,184 / year
- Big Data Engineer – Average Salary = US$124,699 / year
- Machine Learning Engineer – Average Salary = US$122,525 / year
- AI Engineer – Average Salary = US$118,195 / year
- Data Scientist – Average Salary = US$117,212 / year
- Data Engineer – Average Salary = US$113,011 / year
- Data Modeler – Average Salary = US$106,268 / year
- NLP Engineer – Average Salary = US$88,551 / year
- Business Intelligence Analyst – Average Salary = US$86,731 / year
- Database Manager – Average Salary = US$75,640 / year
- Database Developer – Average Salary = US$75,640 / year
- Data Analyst – Average Salary = US$74,081 / year
Top 7 paying companies for Data Science
- Airbnb – Average Salary = US$188,000 / year
- Apple – Average Salary = US$158,000 / year
- Meta – Average Salary = US$152,000 / year
- Uber – Average Salary = US$147,000 / year
- Amazon – Average Salary = US$137,000 / year
- Microsoft – Average Salary = US$139,000 / year
- Booz Allen – Average Salary = US$94,000 / year