Millie’s Guide to Data Science Careers
Four data scientists joined our Millie’s Guide to Data Science Careers panel to give us more insight into the data science industry.
Our panelists Were:
- Kunaal Naik: Computer Science (BTech) at Institute of Aeronautical Engineering (‘10), Senior Data Scientist at Dell Technologies & Guest Faculty at Jigsaw Academy
- Siddhartha Mamidanna: Data Analytics (MSc) at University of Houston-Downtown (‘19), Data Scientist at Control Risks
- C M Nafi: Data Science, Computer Science (BA) at Luther College, Data Science Intern at Skybridge Associates
- Ahmed Sanda: Data Analytics (MSPPM) at Carnegie Mellon University (‘20), Data Scientist at New York Power Authority
If you are intrigued by the idea of data science but aren’t entirely sure of what careers in data science entail, read on to learn more about the fascinating world of data science.
What exactly is data science?
Data science, simply put, is an improved decision-making practice. It is an area of study that allows faster and more accurate decisions to be made using mathematics and programming. It is a broad field that also relates to predictive analytics and machine learning.
Our panelists also described data science in relation to how it involves the usage of various forms of data and the production of more accessible and beneficial results for people. This process often requires the use of scientific methods linked to computer science and has an immense effect on our day-to-day life, especially during the contemporary digital era.
“Data science is an improved decision-making process using mathematics and programming."
Do I need a data science degree to be a data scientist?
Although a data science undergraduate degree will be ideal, the panelists mentioned that this is a rare option due to data science being a rather new field. Some data science-related undergraduate degrees that are more accessible are computer science, statistics, or operational research. Studying data science at the Master’s level is much more common; Northwestern, Columbia, UCLA, Imperial, and UCL are some popular destinations. Regardless of the major, the panelists stressed the importance of coding and programming; it is greatly advised for students to familiarize themselves with these during their undergraduate years.
Students should also try to get more data science-specific experiences in university. Siddhartha described how he was involved in data science projects during his undergraduate and how these helped him during his graduate school. Nafi worked on dashboarding and using CRMs to further understand data analytics in university.
“Try to get more data science-specific experiences in university.”
Despite thinking being able to code is a beneficial addition for a data scientist and not a must, Ahmed believes that proficient and distinguished data scientists will be those that understand the computer systems.
Kunaal believes that data scientists are problem-solvers, and that coding is a tool in their arsenal. Kunaal emphasized how the knowledge in data science tools does not necessarily have to come from your university. If you have the self-driven approach to learning and push yourself to participate in boot camps or take courses outside of your formal institutions, you will be able to become a proficient data scientist.
“If you have a self-driven approach to learning, you will be able to become a proficient data scientist.”
Do I need to graduate from Stanford or MIT to get a good job?
NO! The name of the institution is of minor importance when it comes to securing a job in the field of data science.
What you should work on is your portfolio. In your portfolio, you can discuss the self-driven approaches you’ve taken to explore deeper into data science, such as the projects you’ve run. Your portfolio, not the school you graduated from, is proof of your skills in data science.
“Your portfolio, not the school you graduated from, is proof of your skills in data science.”
Also, try and get professional work experience as this will offer you a chance to learn the language used in the field of data science, as well as a first-hand experience in the workplace you might be a part of one day. These are lessons that even your graduate school cannot offer, so actively seek them!
What are the pros and cons of being a data scientist?
Working as a data scientist never gets boring as you need to constantly identify the caveats and limitations of models and search for solutions to improve them. This process gives freedom to data scientists to be curious and ceaselessly explore.
Of course, the breadth of data often makes it difficult for data scientists to prioritize their tasks and set a focus. Still, this is what makes data science even more intriguing; there is never a limit to the amount of knowledge you can acquire!
“There is never a limit to the amount of knowledge you can acquire in data science!”
How do I begin?
These are some resources that our panelists recommended to aspiring data scientists:
- Coding academies (General Assembly, Data Incubator)
- Stack Overflow
- An Introduction to Statistical Learning
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
- The Master Algorithm
When using public communities like Google or Stack Overflow, make sure you only take what you see as references; codes on the internet are often not specifically applicable to your case or inefficient. A piece of advice from Kunaal was to search for domain-specific textbooks for you to attain expertise in a specialized sector within data science.
If you read up till here, you’re much ahead of many other aspiring data scientists! Watch the full panel for the full-length insight, and book a free consultation right now to continue your journey toward becoming a data scientist!