🖥 How to land your first Data job without prior experience.
Secrets from go-getters on paving their own golden path in Data Science.
Among every FAQ list of jobseekers and first-jobbers, there lies a question mirroring the famous “which comes first: the egg or the chicken” tale. How could one get their first job, when every vacancy seems to require past experience?
The truth is, experience isn’t the only thing companies look for when scouting for new additions, especially to their Data team. Make sure you enhance and expand your skills to showcase your capabilities and ace the technical tests!
Shortcuts to your dream job
Not sure how to make your break into the Data field? Frank Andrade, an avid Data Scientist and Author, has compiled ways to do it even when you have 0 experience. From his own and his colleagues’ experiences, here are some of Andrade’s tips which you should give a go:
Learn SQL/Python, Solve a Project, and Practice Your Communication Skills
Put your skills to the test by solving a project from scratch. Make sure to pick a topic you’re passionate about, and accompany the outcome with top-notch communication skills when presenting it.
Write Data Science Articles Online
Get in companies’ radar by building a stand-out online presence. Write an article describing every step to complete a project you’ve solved. Include things like data sources, the methodology you use, the takeaways, and the conclusion.
Networking
If you’re taking the traditional education route, every teacher and classmate you meet is a door to your first data science job. If you know someone who’s already working as a data scientist, they can also introduce you to key industry people.
Intern / Volunteer
Working as an intern, or pro-bono as a volunteer, will give you the experience you need to grow in your career. After all, working with real-world datasets and working with ones downloaded from Kaggle is a world of difference.
Learn more about each step, and find examples on how to write Data Science articles in Andrade’s Medium piece here.
The modern Data Scientist
Not only is the way to kickstart a career in Data has changed in the course of the past 3 ‘web versions’, the fundamentals of what makes a Data Scientist great has also evolved with it.
Based on the observations of Rassul Fazelat, President & CEO of Data Advisors, the main attributes you need to make it in today’s Data world include: 1) analytical foundation, 2) experience with tools, 3) analysis & presentation.
If you’re looking to build these attributes strongly, and prefer to go the formal education way, Fazelat has formulated a list of college majors that are great starting points for a Data Science career, created by reviewing curriculum for many universities across the United States:
Mathematics, Applied Mathematics or Statistics
Engineering (any engineering with heavy mathematics/calculus-based physics focus)
Meteorology, Astrophysics, or Physics
Chemistry (especially Physical Chemistry) or Biostatistics
Computer Science
Honorable Mention: Economics
Read more on which skills and tools you need to master in order to become the modern data scientist here.
Data Scientist Intern’s day-to-day
If you’re thinking of starting an internship in the Data field but still has a lot of questions in mind, Mekari KlikPajak’s Data Scientist Intern, Rio Audino, has laid it all out via his Instagram Story takeover, in which he shared all about:
Routine activities as a data scientist intern, including daily standup, sprint review/planning, and monthly meeting.
Recruitment process he went through via Mekari’s Next Level Internship + tips on how to pass each level of the program.
Tools he uses daily, such as Tableau, Dbeaver, and VSCode.
Benefits he gets as a data scientist intern in Mekari.
Watch Rio’s full explanation + more questions he answered regarding his experience, in the IG Story Highlight here.
Are you, are anyone you know, currently looking to jump into the Data field?
This week’s Monday Mavens edition should be the perfect all-in-one place for you to gain insights on how to kickstart your career!
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We’ll see you again next week!