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My degree: Data Science


In our previous series, ‘My Job,’ we investigated the lives of four individuals with four unique jobs. We spoke to Ozzie Iyambo about his role as a DevOps Engineer, we talked to Kenneth Seals-Nutt about being a Software Engineer, we discussed Data Science with Vinamra Mathur, and we learned about being a CISO from Jim Mapes. With the success of this series, we wanted to explore ‘My Degree,’ where we meet students studying unique disciplines. We kickstart the series with Advait Ashtikar, a Data Science student studying at Pennsylvania State University. We spoke to Advait about his experience studying Data Science and what it takes to complete a data science degree.

What is Data Science

In Advait’s Ashtikar words, “data science is understanding and evaluating data and using this information to solve societal issues.” This data science student explained that “data is like an atom, which is the most fundamental unit towards any innovation, especially in technology because you must analyze and understand why those issues occur. Then you use that same data with advanced technology to understand and solve those issues.” One of the main words used to describe data science is “imaginative,” says Advait. “You have to be imaginative. Data science is about visualizing basic data, creating graphs pie charts, and making inferences. By being imaginative, you must make something meaningful out of the process so the data isn’t wasted.” Advait also described his data science degree as “a bunch of maths and statistics sprinkled with some computer science. Data science is the process of understanding and analyzing data to glean meaningful insights to solve problems.

High school years

High school is where Advait's love for Data Science blossomed into a full-fledged passion. We spoke to this budding data scientist about how this experience encouraged him to undertake a computer science and information technology degree. “In high school, since I was in traditional Indian education, my senior and junior years were limited to five subjects of my choice. I chose Maths, Physics, Chemistry, Communicative English, and Computer Science. Through these courses, Advait had some experience with computer science and information technology subjects. “I dabbled in probability statistics, and I also learned C++ coding. I started understanding the coding process and what happened behind the scenes. I also participated in a few projects in high school. However, it was not too extensive. Through these projects, you had to investigate the more theoretical statistics behind all these different lines of code we write today.” With a strong basis behind him, Advait decided to undertake a computer science degree but later changed his major.

Finding Data Science

Advait discussed how he came to love data science and the driving forces that spurred his decision to change his major. “When I was in one of the schools in India, I naturally focused on statistics and mathematics. At that time, I wasn’t much of a programming person.” Advait believed he would continue with computer science and specialize in data science, “but after the first semester, I realized that data science was more my thing than computer science.” This student followed his intuition and switched majors to an applied data science major.

Choosing Data Science

So, what made this student choose data science? As said before, he was always interested in the statistics and mathematics side of computer science and information technology. However, he always knew that he wanted to specialize in data science, so why wait until his master’s program to do that? “It kind of came upon me. I don't want to sit behind a computer, just coding stuff. I want to understand how that code works, but I also want to see what impact the code has,” which is why he decided to change his major as data science satisfies Advait’s natural curiosity. Data science is a more imaginative process whereas with "computer science, you have to be specific about what you want to look into with coding and programming. In data science, it's just so imaginative because you're thinking of a hundred possibilities. What possibility suits me, and what could that data do for me? So, that's why I believe data science resonated with me more, as I could see the effects of the discipline and understand the value of data science.”

Dynamic Data Science

Throughout our discussion, Advait told us what he does at university and how his data science program is constructed. “I study data science in great depth, but at the same time, I have a ‘minor’ called an application focus which concentrates on information and cyber security.” So, I have a few classes where you learn how to apply these skills to data science and computer science realms.” Advait told us a little bit more about data science at university, how it works, and what his major entails.

At Penn State, there are three types of data science majors:

  • Computational Data Science - which focuses on programming models. This is more of a coding-heavy course that goes behind the scenes when creating machine-learning models. Generally, this degree is more technological.
  • Statistical Modelling Data Science - this program incorporates statistical modeling data science, which is more theoretical. It focuses on the theories behind everything known in data science today.
  • Applied Data Science - is the combination of computational and theoretical data science as you get the best of both worlds. You will learn theory and take on a lot of the computing/programming aspects of the course. With applied data science, you will use your knowledge to different focuses. You are asked to choose one direction but can go deeper into the industry. For example, data science could be applied to cyber security or used in economics.

Data Science at university

Advait told us what he’s learned while studying data science at Penn State University. “So far, I've had classes where I've learned about privacy and security for data science. Understanding data privacy, how to secure your data, and what general normative practices occur in industries when using and manipulating data. We have also learned how companies work around or work with regulations. We have also looked at trends, how data breaches happen, and other aspects of data science that are important in understanding the entire discipline. I have also been learning about big data, where you have millions of gigabytes that no computer can process. So, we’ve been learning how big companies work with so much data. We have also covered the general area of big data, the best way to approach big data, and understanding the main parts of data science.”

Advait described the main parts of the data science:

  • Exploratory phase - this is where you clean up your data sets and get your initial assumptions about the data ready and made.
  • Training and testing - then you put the data sets into your machine learning neural network models that train and test these data sets.
  • Validating - once you train and test your data sets, you get validation regarding them. This is when we know this is as authentic and accurate as your data sets get.
  • Inference - you have more inferring to do when everything has occurred and test whether your assumptions match the output.
  • Presentation - you will present (if you are in a working environment) your findings to a client in a clear and concise format. You have to translate that technical language into something the stakeholders will understand.

Data Science skills

Advait told Cybernews Academy that, in his experience, you don’t need any “heavy skill sets” to undertake a degree in data science. However, some fundamental skills will help you better contextualize and understand your discipline. “I think having a good basic understanding of programming, specifically Python (as this is one of the most widely used languages I’ve seen throughout my course) is essential. Alongside this, a fundamental understanding of statistics is also crucial. There are many different words you get thrown in in your first initial classes, like mean, mode, median, and regression, which the professors explain. Still, it's always good to know what this terminology means and how the industry determines them.” Advait also suggests that prospective students should have some experience with data and truly understand what meaningful information they wish to glean from it before they start “messing around with it.” Advait mentioned that you have to go into the course with a “clear and concise assumption” regarding what you want to achieve throughout your work with the data model.

Surprising science

Advait told Cybernews Academy what surprised him about his data science degree. “When I started my degree in data science, I was taken aback by the amount of exploration that goes on. I always thought I would have to explore the data set and spend 90% of my time making models. But I’ve found that there is so much less time spent on actual models in the initial exploratory data analysis, cleaning up an extensive messy data set, and getting all these graphs and visualizations right.

Data science is an extremely intricate and interesting field that works to solve complex world issues. So, if you want to undertake a data science degree, here are five things you should consider delving into the world of data science.

  • Curate your courses - choosing mathematics, statistics, and other STEM subjects will give you the appropriate prerequisites for a data science degree.
  • Find the fundamentals - learning fundamental skills like mathematics and statistics will lay the foundation for a degree in data science
  • Gain some practical experience - whether that’s a personal project or a project you are working on in high school. Any experience with data science will give you a strong start. So, participate in your school's hackathons, group projects, and data science events
  • Be imaginative - data science requires creativity to glean meaningful insights from your analyzing data.
  • Self-learning - start learning skills like programming languages early by taking courses or learning through self-study.