We often hear of these 3 primary data careers: data scientist, data analyst, data engineer. But what exactly are these roles?
If you find a job posting for a “data scientist,” it’s likely that the employer is hiring their first data-centric positions. In this case, as a “jack of all trades,” you may be responsible for a wide variety of tasks. However, most postings will specify a clearer focus – usually in one of the four areas outlined below.
On career paths in data science, written in Coursera Blog.
The outlook of the profession is rather favorable:
There are lots of art in becoming a Data Scientist, from writing resume to using Machine Learning Libraries in R, Python, and Julia (or even Go). It seems that the term “Data Scientist” was first coined by Davenport and Patil, then Drew Conway popularised what it meant with a Venn Diagram.
This post aggregated some tips to get started in Data Science.
8 Easy Steps to Learn Data Science
Data Camp has an infographic detailing 8 easy steps to learn data science:
- Get good in Mathematics, Statistics, and Machine Learning.
- Learn to code: end-to-end development, CS fundamentals, Python and R.
- Understand databases: MySQL, MongoDB, PostgreSQL, etc.
- Explore the data science workflow: from data collection to modeling to reporting.
- Level up with Big Data: Hadoop, Spark, etc.
- Grow, connect and learn: Kaggle, Driven Data, Meetup, pet project, etc.
- Immerse yourself completely: internship, bootcamp, and job.
- Engage with the community: R User group, Local Data Science meetup, etc.
Learn from other data scientists
- David Robinson: 1 year at StackOverflow
- Sander Dieleman: From Kaggle to Google (Deepmind)
- Elena Grewal: How I made it (Airbnb)
- Vincent Granville: Salary history and progress (Data Science Central)
Data Science is masterable with some gravitas:
Finally, do lots of readings:
- The Field Guide to Data Science. Booz Allen Hamilton.
- Good Books for All Things Data, by Multithreaded @ Stitchfix
- Books and practices from Andrew Ng (Coursera, Baidu)