More than a handful of times have I been asked about how to get into the field of data science. This includes SMU’s Master of IT in Business classes, regular DataScience SG meet ups, and requests via email/linkedin. Though the conversations that follow differ depending on the person’s background, a significant portion is applicable to most people.
I’m no data science rockstar. Neither am I an instructor that teaches how to get into data science. Nonetheless, here’s some previously shared advice on “How to get started in Data Science”, documented here so it can be shared in a more scalable manner.
What this post will (not) cover
This post will focus on the tools and skills (I find) essential in data science, and how to practice them. Every organization has different needs, and what’s listed is largely based on Lazada’s data science stack and process. Nonetheless, they should be applicable to most data science positions. These should be viewed as minimum thresholds, and they do not necessarily predict success in data science. They are:
- Tools: SQL, Python and/or R, Spark
- Skills: Probability and Statistics, Machine Learning, Communication
- Practice: Projects, Volunteering, Speaking and Writing
This post will not cover character traits, personalities, habits, etc. While there are some traits I find strongly correlated with success in data science (e.g., curiosity, humility, grit), we will not discuss them here. In some sense, these traits lead to success in all roles/life—not just data science.