How to create a winning career in Data Science and AI

Harvard’s Dr. Pavlos Protopapas, in conversation with Siddharth Das, CEO, Univ.AI, provides insights on how to begin your Data Science and AI learning journey and what to expect as you go along the way. Edited by Mariya Sethjiwala and Rajan Arora.
Cover Image
A lot of people imagine a career in AI to be all about self-driving cars, image recognition, neural networks, and the like. But what does a real career in AI look like? What problems do you solve daily? Dr. Protopapas answers these questions, and more! Read on.

Dr. Pavlos Protopapas is the Scientific Program Director, Institute for Applied Computational Science at Harvard University, and oversees the master’s programs in computational science and engineering and data science at Harvard. He has had a long and distinguished career as a scientist and data science educator. In conversation with Siddharth Das, he answers critical questions which plague a fresh learner’s mind as they embark on their journey.

What is Data Science?

Data science is a multidisciplinary field which focuses on leveraging data to draw insights that can help an organisation make better decisions. It lies at the intersection of computer science, mathematics and statistics, and business expertise.

It includes data modelling, data handling, data cleaning, data manipulation, data management, as well as data visualisation & its presentation and storytelling. At Harvard University where Dr. Pavlos Protopapas is the Scientific Program Director of the Institute for Applied Computational Science (IACS), ethics is also added to the mix, alongwith critical thinking and awareness of algorithmic bias.

 

What is the relationship between Data Science, AI and Machine Learning?
Image Credits: Gmggroup.org

 

Artificial Intelligence is the larger concept of making machines act like humans. Machine Learning & Data Science are subsets of Artificial Intelligence.

According to Dr. Protopapas, Data Science is the practical application & exploration of data sets to solve business challenges that affect various industries, whereas Machine Learning is more academic in nature. Machine Learning involves rule-based learning & modelling and other computational sciences for improving algorithms to make better predictions.

It is not rare for young students with a fascination for Artificial Intelligence to go big and start discovering & learning more about deep neural networks, an advanced subset of Machine Learning, at the very onset of their AI learning journeys.

Rather, Dr. Protopapas adds, that it is important to develop and strengthen your basics of data science – data acquisition, data manipulation,data wrangling, etc. – before you dig deeper.  A strong set of data science basics ensure that you are laying a strong foundation for your career in Artificial Intelligence.

 

What does a career in Data Science or AI look like?

Data science itself is quite a vast field with many functional areas. In addition to functional areas, it also covers a variety of disciplines. A lot of data scientists at Uber, Google & Facebook, for example, work in advertising. While pharmaceutical and medical industries are also increasingly starting to use data science. Other upcoming industries include distribution, construction, real estate, agriculture, manufacturing and even forestry. As far as global progression in data science goes, the study is just entering its phase two, and its adoption & application will only continue to increase across a plethora of industries as more use cases are discovered every day.

 

Is Data Science only for Mathematicians & Computer Science graduates?

Naturally, for those with a mathematical or technical background, the transition is easier. However, a lot of students are those who looking for a career shift and who enter from various other fields like architecture, law, design, and even literature, but the majority of current students are STEM graduates. If you are not from a mathematical or technical background it just means you will need a bit longer to learn. One fascinating example is that of a park ranger of 10 years, who decided to pursue a data science at Harvard, in Dr. Protopapas' classroom, due to his interest in AI. He now works with a pet care start-up in the Silicon Valley.

 

Do you need a Ph.D to succeed in the field?

The answer is - not really. There Are plenty of jobs in the field of data science and most of them do not necessitate a Ph.D. If you are looking to develop your career in pure research and would want to work with a research lab, then it may be good idea.

 

So how should you start?

In learning Artificial Intelligence, a ‘one size fits all’ model does not exist. It depends on the learning techniques best suited for everyone’s individual personality.

A lot of Data Scientists today with successful careers happen to be self-taught, and you, can also begin your learning journey similarly. The Data Science community is indeed very supportive. There a lot of resources including data science tutorials, AI courses and more, available online to learn & grow. A lot of the work is open source as well, and the community is constantly upskilling themselves and in-turn, others.

But formal education from a renowned university also has its own benefits, like working on live projects, learning from your peers and experienced mentorship. Although, this comes at a certain cost and time investment from the student.

The other option, however, is to opt for a hybrid approach which Univ.AI offers. Univ.AI’s program structure is designed by Dr. Protopapas himself, which creates high quality learning opportunities with globally renowned faculty, interactive live lectures, capstone projects and more.  

This is a great option for those who are trying to find a balance between their jobs, families and learning, eliminating the extra layers of effort required to be present for a physical class, yet presenting you an expert led, structured approach of carving out a rewarding future in AI.

The end game will be your mastery of the skill sets which differentiate good data scientists from the great ones, as the field might be very accessible but is indeed very meritocratic. An education from highly acclaimed institutions including Harvard, MIT or IITs will hold you in good stead as you begin progressing for interviews, but at the end of the day, it will always depend on your individual knowledge, skills and capabilities to steer ahead and land that job through a series of rigorous screening rounds.

To succeed in the field, the key is to find an experienced mentorship support system.

A mentor will help you drastically reduce your efforts in discovering the right knowledge and growth paths most suited for you by pointing you in the direction of the right resources, savingyou plenty of time, money & efforts.

 

If you are interested in a career in data science, there is no time like the present to start. Explore Univ.AI's programs here.

Share this post

-