DATA Initiative

Aldo, KCS '22, is working towards his master's in data science at Northeastern University's Khoury College of Computer Sciences. Outside of the classroom, he is the expert in residence for the DATA Initiative and serves as a resource for students working on analytics projects in the Analytics Lab.

Q. How did you end up having a passion for data science and analytics and what was your motivation behind it?

A. While I was pursuing my undergraduate degree in computer engineering back in Chennai, India, I read the book by Ray Kurzweil, “How to Create a mind,” and I was fascinated on the working of the human brain and his ideas on how to simulate the neocortex artificially. I became interested in computational neuroscience and started studying it, which eventually lead me into machine learning and data science. I see data science as the most intelligible solution to solve real life problems. 

Q. What co-ops or job experiences do you have in this field?

A. Before pursuing my graduate studies at Northeastern, I worked as a software engineer for almost two years. I worked at two different startups: Mad Street Den, a top start-up in the field of AI-based fashion retail, and Intain AI, an AI-powered blockchain startup assuming roles such as Platform Engineer (DevOps, Infra), Product Developer and Computer Vision Engineer. I also had my own second-hand retail business and was a part of the business division of my family's medical distribution company.

Q. What is one of your favorite projects you have worked on prior to joining the DATA Initiative?

A. I have worked on multiple interesting projects in engineering, computer vision, and OCR before moving to Boston. A very notable one was VueModel, which used generative adversarial networks (GAN) to generate real-life model images for any given fashion product, eliminating the need for a photoshoot and the model itself.

Q. What are some of the key challenges you faced during any work projects or working in the industry?

A. Any cool project can flop when you do not build the right infrastructure to sustain its “coolness.” We always face the need to improve our data pipelines and other parts of our engineering infrastructure such as a need for a faster database, a better algorithmic complexity, etc. to keep improving our product and handle the millions of API requests we had coming in every day. Keeping a product stale without any update is a sign of its depreciation.

Q. How important do you see the offset between business understanding vs software engineering in the field of data science?

A. Business understanding is essential. When you really need data science to solve a business problem, software engineering comes in handy to build the right algorithms that are efficient and use the right tools to cater to the needs for an accurate solution, especially when the data science solution is an actual software or an API which needs to serve the results quickly and efficiently.

Q. Would you share some thoughts on how important “explaining things” is in data science?

A. Data science is all about how you deliver the data. No matter how fancy an algorithm can be, if the audience cannot make a sense of it, it is useless. So, we data scientists/analysts should always be careful to keep the strategy, the model, and our visualizations correlated and relevant because the structure can entirely fall apart even when one of these three fails.

Q. What is the one advice that you followed in the past or your biggest learning lesson that is helping you in your data science journey which you will also give to your juniors?

A. Keep looking for answers even if you think know the right one. There is always a better solution and never be scared to say that you do not know something. Being humble in learning is an asset to be carried throughout the career. Always be open to suggestions and be wise n choosing the right advice that can help you progress.

Q. Anything else you would like to share.

A. In any job, you'll need to understand people and focus on working on tasks collectively as a team. Be versatile and take up any task you are given because you never know where that will lead you.

For more information about the DATA Initiative or to be featured in the next informational interview, please email contact@datanu.org.

Kasia, DMSB '20, is working towards her bachelor's in business administration at Northeastern University's D'Amore-McKim School of Business. She is a student in the inaugural class of the DATA Initiative Analytics Lab.

Q. What led you to your passion for data science and analytics?

A. I am a fourth-year marketing and finance concentration with a minor in business analytics. When I got my co-op at TJX, I did a lot of entry-level work, after which I realized I need exposure to more decision-making work environments. That was also the reason for me to complete my second co-op at Amazon Web Services where I got exposed to data analytics for the first time and produced tangible results.

Q. What co-ops or job experience do you have in this field?

A. My work experience at AWS was within the marketing automation team where I tracked leads for the sales department. They gave me the independence to explore and try new methods and hence it involved a lot of self-learning for me. Using data, I found some interesting facts and provided recommendations directly to VP and managers. 

Q. What kind of role do you prefer as a data scientist/or analyst in the future? Which industry do you want to move into?

A. Right now, I am more interested in working for smaller teams as well as businesses that are starting to grow. Preferred roles would be like a business analyst, marketing analyst or product analyst. The IT industry would be my first preference, partially because of the challenging environment it possesses which will allow more room for learning and growth. 

Q. What is one of your favorite school projects you have worked on?

A. One of my favorite projects so far is at the DATA Initiative laboratory where I am getting the chance to work on solving problems in understanding online gaming.

Q. What are some of the key challenges you faced during any project or working in the industry?

A. I am currently taking a lot of technical courses that I have found challenging but rewarding at the same time. I remember making my website in HTML where mistakenly having just an extra bracket can take a toll on you.

Q. How do you see applied knowledge vs theoretical knowledge within this field?

A. Theory helps you to make insights, otherwise, the technology can only give you numbers and dots. The theory is what helps you make sense of it. That is what the DATA initiative is doing, and that is why I have taken a business analytics minor.

Q. How important do you see the offset between business understanding vs software engineering in the field of data science?

A. 10 years ago, that connection barely existed. But now, you can't have one without the other. How would you run a campaign? What platform are customers using? How much cost are you incurring? The company wants marketers to have a good understanding of data analytics.

Q. One biggest learning lesson (or mistake) you made during this journey and now that you feel fortunate that you have realized it. 

A. The biggest mistake I made is being a perfectionist and being stubborn about details. Working with data has taught me that you can only win by trial and error.

Q. How do you see the future of data science and analytics 3-4 years from now?

A. It is not going to take over the world. At the end of the day, a human has to make decisions based on data. Earlier, people only learned about basic things, but now the overlap between business and data science is getting bigger.

Q. Anything else you would like to share. 

A. I really liked the documentary on Netflix called The Great Hack. It showed how the power of analytics can allow candidates to win elections.

For more information about the DATA Initiative or to be featured in the next informational interview, please email contact@datanu.org.

Melissa, DMSB '21, is working towards her bachelor's in business administration at Northeastern University's D'Amore-McKim School of Business with a concentration in marketing analytics. She is the project manager for the DATA Initiative and works closely with the Analytics Lab.

Q. Why did you decide to pursue a marketing analytics concentration?

A. When I first got to Northeastern, I knew that learning marketing principles and how to build consumer relationships was my priority. During my first co-op, I got a glimpse into data analytics and how numbers could explain results. It was exciting to track the impact of marketing campaigns and learn how to visualize data. After taking my first marketing analytics course with Professor Kwong Chan, I realized that merging my previous interests in understanding consumer markets with my newfound excitement for data was the perfect combination.

Q. What co-ops or job experience do you have in this field?

A. I did my first co-op at New Balance as its digital consumer experience co-op. I worked on analyzing its wholesale brand pages such as Amazon, Zappos, and DSW.  Playing with data visualization and numbers prepared me for my second co-op, which I'm on now, where I work with data analytics more. At Genentech, I partner with the Department of Informatics to look at how analytics powers internal scientific decision making and how cloud data repositories help with data management. Since this role oversees internal communications and strategies, there are a lot of opportunities to use tools like Tableau and R to create visuals and trends to share with the rest of the team.

Q. What are some organizations on campus that have helped you learn more about data analytics?

A. I started working with the DATA Initiative, a cross-disciplinary hub that shares new knowledge in data analytics. Over the past year, I have worked with the DATA Club in promotion of analytics on campus, helped establish the first student Analytics Lab, and organized the inaugural DATA Forum last September, which brought in guest speakers from leaders in the industry. These initiatives have given both undergraduate and graduate students the opportunities to learn more about analytics and connect with projects or companies to promote learning.

Q. What is one of your favorite school projects you have worked on?

A. I took the Introductory data science course this past semester and created a mock predictive model for Yelp. The goal was to help recommend cuisines to potential business owners by looking at trending successful businesses in a given state. What I liked about this project was the ability to connect real-world problems with programming and my new technical skills in Python! This dataset gave my partner and me a lot of interesting information, and we became really invested in it.

Q. What are some of the key challenges you faced during any project or working in the industry?

A. I think there are times you can come to a road block where your project might not be working, but I think these moments make me work harder. What I love about analytics is that there are multiple ways you can solve a problem, and that these stories can be told differently depending on who is looking at the data.

Q. What kind of role would you prefer as a data scientist/or analyst in the future? Which industry would you want to move into?

A. I would say in the future, I would be interested in working as a product analyst over data scientist. Like I've said before I enjoy working with the consumer-focused processes and would love to incorporate data analytics into my work and decision making. However, there is a lot to learn from my second co-op and my mentors in the industry so I am pretty flexible!

Q. What is your biggest piece of advice for someone who is considering a similar path with marketing analytics?

A. The biggest piece of advice I have is to just try it. I was scared that this field would be too difficult or technical for me. Most of my work with marketing prior was along the lines of basic design and social media advertising. However, after taking the marketing analytics course, I realized that this was the perfect concentration for my old interests and newly budding interest for analytics. In my opinion, there is a big difference with having just “marketing” versus “marketing analytics” on your resume. I think the latter provides lots of opportunities, especially since the future of data science and analytics will continue to grow.

For more information about the DATA Initiative or to be featured in the next informational interview, please email contact@datanu.org.