Data science is one of the hottest growing fields at the moment, along with machine learning and artificial intelligence. However, for anyone who wants to be a good data scientist, the path is confusing.
Some many online courses and resources can help you get started, but still, an important aspect remains, which is the hands-on experience.
Welcome to our first interview of Expert Advice from Data Scientists Series. We will connect with expert data science and AI professionals and share their journey and experience with you.
From their valuable advice and knowledge-sharing, you can decide the course of your career and set on a realistic learning path to become a better data scientist and grab a good job opportunity.
As a data science aspirant, you will get to know what the industry expects from you and how you can develop your skills.
Today, we will share expert advice from Rajiv Shah, who is working as a data scientist and AI researcher at DataRobot. He is also a data scientist professor at the University of Illinois and have worked on various real-world projects and published many whitepapers.
Without further ado, let’s chat with Mr Rajiv and get to know about his journey towards becoming a successful data scientist.
1. Please share some insights from your professional role as a data scientist.
I am currently a data scientist at DataRobot. I spend most of my time working with customers on figuring out how best to model their problems and then get the machine learning solutions implemented.
It’s a lot of fun because I get to see a variety of use cases from different industries. Before DataRobot, I was a data scientist at State Farm (insurance company) and Caterpillar. I think it’s always great to be active an trying new techniques, I occasionally post at twitter (@rajcs4), and a lot of my older projects can be found at http://projects.rajivshah.com/blog/ and http://www.rajivshah.com.
Also Read: 12 Cloud Data Security Best Practices For 2020.
2. Which tools you use for Data Science and which one do you recommend for beginners?
If you don’t come from programming, I recommend using R. The learning curve is much more comfortable, and it’s much easier to come back after a few months away (which is going to happen).
While getting models into production often requires using Python, I find lots of new users quickly get frustrated learning Python. I also encourage people to not just focus on algorithms but think about the whole process of data science from working with data, visualizing data, and building apps to let people use the results of the machine learning models.
3. Myths related to data science that you would like to bust.
Data science is seen as a hot field because of high salaries. Don’t come to data science because of the high salaries. There is already a flood on entry-level data scientists that end up taking far lower-paying jobs. Do data science if you have a passion about solving problems using data. The passion makes a huge difference for succeeding in the field.
4. Tell us about your journey towards becoming a successful data scientist?
Like a lot of early data scientists, my journey to becoming a data scientist was far from linear. I have an undergraduate degree in Electrical Engineering and graduate degrees in Communications and the Law. The thread that tied that together was a passion for understanding the interaction of technology with data science.
Before the data science boom, I actually spent several years as a system administrator, because I was good with computers. Once I stumbled upon data science, I found I could bring together my past skills into a new career. I started by spending time building machine learning models and apps.
5. Some advice for data science aspirants to get a good job opportunity?
Work on projects! Just watching and taking courses is not enough. Actually, building and coding are much more valuable. In my experience, you learn more by doing rather than watching.
6. These days machine learning, big data & AI is getting a lot of attention. How do you think things will change in the next five years?
Specialization and automation are coming. The era of a data scientist that does everything is ending. While it’s still useful to learn those skills, most data science work will be accomplished by teams with specialized skills using software that automates the rote part.
In a nutshell
We thank Rajiv for taking time out of his busy schedule and sharing his experience with CodeItBro. The conversation will help you to decide better the course of your career in the field of data science. In case you have any further doubts or would like to chat, then connect with us at email@example.com.