Data science has become an integral part of our day-to-day lives, supporting problem-solving in industries like finance, healthcare, logistics, technical services, insurance companies, manufacturing and the information sector.
According to Statista, global data creation is projected to grow to more than 394 zettabytes through 2028. To put it into perspective, a single zettabyte is equal to one billion terabytes, and one terabyte is 1,024 gigabytes. Until recently, most iPhones had a maximum storage capacity of 528 GB.
Given how much data humanity is producing, it’s no surprise that data scientists are in high demand: The data scientist role is projected to grow 36% through 2033, which equates to about 20,000 job openings each year.
Data science is a complicated field, so to best inform you, we sat down with two data science experts from Pace University to discuss how data science tools, such as Python, R, SQL, and NoSQL, can be used to solve business problems. We also discussed how the Pace online Master of Science in Data Science prepares students to excel in this evolving field.
Our Experts
Matthew Ganis was the previous program manager for the online MS in Data Science program at Pace University. Matthew has worked as a professor in computer science and astronomy for 35 years, while also working fulltime at IBM as a lead architect and owner of the social media analytics platforms.
Krishna Bathula is a clinical professor and the new program manager for the Pace University Data Science online program. Krishna has worked in the data science industry for 20 years, with a focus on enterprise data analytics.
Q&A
How do Python, R, and SQL integrate to manage data sets?
Matthew
Python, R, and SQL are the basic building blocks that data scientists use for their analysis. R is very advanced from a statistics perspective, whereas Python is more of a general purpose language. Python is useful with lots of packages such as TensorFlow and pandas. It tends to be a more general scaffolding approach to data science.
SQL is a database used to store, analyze, and group data. I would also include NoSQL and MongoDB as two basic building blocks.
What can you tell me about NoSQL?
Matthew
NoSQL allows you to put unstructured data into a database and query it any way you want. This is useful in data science, because data doesn’t often come in as neat and formatted as we’d expect. It comes in more messy. That’s where NoSQL databases come into play.
Krishna
NoSQL particularly helps in computations where you have billions of database transactions that you want to pull together for faster computations in memory.
In the online MS in Data Science, we have designed courses where we teach SQL and NoSQL databases along with Python, which means our students learn about a variety of databases for storing, manipulating, and mining details from the data. — Krishna Bathula, Clinical Professor and Program Manager, Pace University online MS in Data Science
How can Python, R and SQL be used for business problem solving?
Matthew
Python and R can be used to find similarities or patterns in the data, so we can begin making decisions about what that data is telling us. It’s a linear relationship: if I have X, then I can figure out what a Y might be. SQL is the database where the data actually resides. The database holds everything, and then R and the Python code will choose which data to analyze.
Krishna
R and Python both have their niches. R is heavily used by scientific related clients like healthcare sector clients. Many other industries use Python because it has extensive libraries, and is applicable to multiple modules like artificial intelligence and data science. There are specific zones where you can apply Python like quantum computing.
What is a real-world problem in business you might solve in a course?
Krishna
We use a number of business problem-solving activities in class. One example is looking at how 911 and 311 calls are handled by their databases. We look at those databases and consider what traffic is in these systems and if they are being used effectively in order to create a problem statement. These are the kind of problem statements that you can see in the real world.
Matthew
Another case study we use is analyzing network traffic. This is relevant to students with a computer science or cybersecurity background. There’s a very specific protocol of how clients talk to servers and back and forth. When you capture that data, you can look for anomalies within the protocol. By using machine learning, R, Python, and databases, you can figure out if someone is trying to circumvent security and break into a system.
Krishna
Another example is examining retail images on an e-commerce website, creating a data science problem statement, and applying data science algorithms to determine the highest sales in a particular time period. We can use R and Python to wrangle and manipulate data to discover the answer.
How does the Pace University online MS in Data Science prepare students to address business challenges?
Matthew
I think that the curriculum prepares students very well to address business challenges. We’re in the New York area, so there are a lot of Fortune 500 companies. The faculty who teach in the program have access to those employers, so our students work on real-world problems and receive feedback from industry professionals.
I also stress with my students that they should socialize their work with the rest of the class, because working in the real world is often collaborative.
Krishna
For the first three or four weeks, we try to understand where students are at in terms of their knowledge and background, and we create our in-class activities based on their familiarity. Every week there are challenges, and the students have to come up with a particular solution. The students are divided into groups to mimic the working conditions they will likely have once they graduate.
What specific skills do students acquire in the MS in Data Science that are applicable to solving challenges in the professional world?
Krishna
I teach Practical Data Science Studies which focuses on preparing students to enter the industry. In addition to acquiring technical skills, students also learn how to present their work, prepare for the interviews, collaborate with their teammates, and communicate with your team and outside stakeholders. This course prepares students to work in the industry outside of the classroom.
What are some capstone projects that students can expect to work on?
We give students the option to choose their capstone projects. Sometimes they need guidance on what to choose, and we are always there to support them. — Krishna Bathula, Clinical Professor and Program Manager, Pace University online MS in Data Science
Krishna
We receive a variety of capstone project proposals. One exceptional project focused on how students can find a potential roommate with common interests and needs. Recommendation systems can recommend people based on specific criteria. This capstone project was well-received by the Pace community and researchers.
Another capstone project focused on using ChatGPT and LLMs to conduct automated interviews. The project explored how to automate the first round of the interview process so that person-to-person interviews are only conducted with qualified candidates.
Matthew
The capstone projects are all incredibly interesting. One student wanted to optimize machine learning to predict climate shifts. Another student created a capstone project that determines the probability of a piece of news being false. Another project focused on the chance of you defaulting on credit. Another project looked at how to properly stock high end fashion inventory.
There’s a wealth of different interesting capstone topics that range in all different areas, from legal analysis to inventory management to climate awareness to misinformation detection, it runs the gamut based on the students’ interest.
What should students know about getting a job in the data science industry?
Krishna
Data science job descriptions are sometimes very broad. Most of the data science industry doesn’t understand the boundaries between data engineering, data science, machine learning, and engineering. These are different roles, but students don’t always know which roles to apply for. Businesses don’t always know the exact parameters for each role either.
In the online master’s in data science program, we help students look at these job descriptions, understand what skills the job is asking for, and determine whether their skills match those requirements.
About the Online MS in Data Science
The Pace University online Master of Science in Data Science was designed to help students take advantage of professional opportunities in the next generation of quantitative solutions. Our STEM-designated curriculum leverages the Seidenberg School’s decades of experience in online education to explore theoretical and practical approaches to data governance, machine learning, predictive analytics, and more. This flexible, 100% online program fits a combination of hands-on experience and asynchronous activities into your schedule, building the expertise you need to guide the future of data-driven organizations. Pace University also offers an on-campus option for the MS in Data Science.
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To learn more about the online Master of Science in Data Science, fill out the fields in this form to download a free brochure. If you have any questions at any time, please contact an enrollment specialist at (914) 758-1080.