Over the past few years the phrase that has stuck out for me is “data science is the sexiest job of the 21st century”. I do believe that it is true, however, lots of people who want to become a data scientist don’t know how to get started. Hi, my name is Deepak and I am a Data Scientist and the Community Manager here at Pivigo. I finished my PhD in 2016 and the goal of this blog post is to give my step by step guide to how to prepare for a career in data science. Although it is primarily aimed at those who have completed or are going to complete a masters degree or PhD, if you have completed these steps then you are well on your way to a career in data science. I hope you find this useful and if you need any more advice or help, please don’t hesitate to get in touch with me.
Step 1: Programming
The underpinning of all good data science is computer programming. It is how most cleaning, analysis and visualisation is conducted. The two most used languages are R and Python. For me python is the easier of the two to learn. During my PhD, I learned IDL which is not used in many commercial sectors. When I was learning Python, I found several good courses. My top one is the Codecademy python course. It’s a read-code style course and will give you a good understanding of Python. Best of all its free! One I found useful, even though I pay a monthly subscription, is Data Camp. The Python course is very thorough and fun. It’s a video-code style course and although can get a bit repetitive, you will reinforce the syntax and methods used in Python. I followed the Python for data science track and really enjoyed it. My final course recommendation is Udacity’s introduction to computer science. This course will teach you python by building a web crawler. Again a video-code course but really well taught course.
Step 2: Databases
The majority of data used for data science is stored in databases, more specifically relational databases. Normally theses are accessed using Standard Query Language (SQL). There are several good courses online to help you learn SQL. Codecademy has a good, free, course. It will teach you the syntax and best practices when using SQL. Udacity has a good course called “introduction to relational databases” which also gives some background on SQL. Having a good grasp of SQL is vital because you will be using it to bring your data from the data base directly in to Python.
Step 3: Machine Learning
The holy grail for data scientists is machine learning, it is a combination of both science and art. To learn the science, the gold standard is Andrew Ng’s Coursera course. Not only will you learn the theory behind machine learning but you will also put it into practice. For those who would prefer to start at a slightly easier introduction the Udacity course introduction to machine learning is a great way to get started with machine learning. The art comes from practice, practice and practice. One can do this through websites like Kaggle. Not only is this a good way to practice what you are learning but it will also give you multiple different applications of machine learning.
Step 4: Commercial Experience
The hardest part about finding a job after completing your PhD is being told “your background is exactly what we are looking for but you have no commercial experience”. The thing to bear in mind is that job specs describe unicorns. To help you in this Pivigo have a bootcamp specifically designed to give you the commercial experience you need to find your dream data science job. Science to Data Science (S2DS), is a five-week bootcamp where you will work on a real data science project for a company. This will give you hands on experience in data science and the commercial outcome. Moreover, this will give you something tangible to talk about at an interview. Having don’t the bootcamp myself I can say that it opened so many doors for me and I would not be where I am today without have taken part.
I hope this blog post has been useful and informative. If you do want any more information or advice please don’t hesitate to get in touch with me.
