How Tufts’ Data Science Curriculum Aligns with Industry Demand

Data has always been vital to businesses, as it informs decision making and strategic planning. However, 21st-century technology is creating new data types in vastly greater quantities than ever before. For instance, social media platforms Twitter, YouTube, and TikTok generate enormous sums of data from social media posts and videos that cannot fit neatly into spreadsheets to be processed by traditional data analytics software.

So, businesses are seeking data scientists who can reveal the patterns hidden in so-called big data—the massive amount of varied (structured and unstructured) high-volume and high-velocity data that cannot be sorted by older data processing methods. Data scientists use advanced technologies, such as artificial intelligence and machine learning, combined with sophisticated mathematics to analyze otherwise opaque data sets at extraordinarily rapid speeds.

Since the incorporation of data science into business is a relatively new development, the field is still growing—and there aren’t enough data scientists to fill demand. The U.S. Bureau of Labor and Statistics (BLS) forecasts employment in data science jobs to grow 36 percent by 2031. In addition, even among currently working data science professionals, there are troubling gaps in expertise—around 40 percent of technology leaders have experienced skills shortages on their teams related to big data analytics. 

Part of this skills shortage pertains to the advanced education and training required to be successful—it is a high bar to clear (62 percent of data scientists hold a master’s degree or higher). The dearth of skilled data science professionals also reflects how higher education does not always align with industry demand. Anaconda’s 2022 State of Data Science Report showed that 51 percent of respondents thought data scientists lacked tailored learning paths to advance their skills. 

To ensure that their data science students are fully prepared to meet industry needs, Tufts’ Online Master of Science in Data Science (MSDS) program teaches a curriculum designed to fill these skill gaps. Students gain the knowledge and expertise necessary to secure data science jobs right after graduation and remain competitive in the job market throughout their careers.

Tufts’ Online Master of Science in Data Science Curriculum Teaches Skills Employers Seek

DevSkiller’s 2022 Top IT Skills Report identifies data science as the fastest-growing skill area in information technology. Indeed, data science is a unique area of proficiency comprised of many specific skills. Some may be familiar to professionals experienced in data analytics or computer science, while others are brand new. The Tufts Online MS in Data Science program intentionally aligns its curriculum with industry demands, ensuring its graduates are equipped for success in their data science careers.

Statistics

Statistics is the backbone of data science. At its essence, data science uses advanced mathematics to analyze data and make predictions from the patterns revealed. However, new technologies have enhanced the power of statistics to reach specific conclusions. Advances in computer processing utilize statistics to create tools like natural language processing (NLP) and computer vision, enabling computers to interpret the world around them through data science. Marketing and sales teams can collaborate with data scientists to create statistical models related to customer behavior and market trends. In this manner, data scientists use statistics to transform opaque data sets into valuable insights.

For these reasons, statistics is the core of Tufts’ data science curriculum. In Math 166 Statistics, students develop competency in mathematical theory related to statistics. They learn mathematical skills they will later deploy as data scientists, such as parameter estimation, the convergence of random variables, properties of estimators, statistical tests and confidence intervals, and non-parametric statistics. 

Programming

Modern data science exists at the confluence of statistics and computer science. Data scientists can extend the power of their mathematical knowledge by programming statistical models into computers, offloading the computational responsibility to machines that can do it faster and more accurately. According to Anaconda, data scientists frequently use the programming languages Python—to automate aspects of their work—and SQL—to organize and retrieve information from databases. Programming proficiency is among the top skills required in data science job postings, according to the job market analytics firm Lightcast.

The good news is that Python and SQL are high-level programming languages that resemble conversational English more than machine code. Basic Python is relatively simple to learn, and Tufts online MSCS students hone their programming skills in courses such as CS 135 Introduction to Machine Learning, which uses (but does not teach) Python in the context of data science. Students also practice SQL in Comp 115 Database Systems, using the language to organize and interact with databases.

Data Analysis

Data analysis creates insights from large volumes of data, usually in service of business decisions. It bridges the gap between complex mathematics and the real world of business, involving stakeholders in subfields such as strategic planning, finance, and marketing. Analyzing data is the core of data science—it’s what data scientists do daily and what their other skills support. So, it’s no surprise that data analysis was the tenth most popular skill in job postings in 2022. 

Tufts online MSCS students learn the mathematical theory behind data analysis in Math 123 Data Analysis. They gain experience with common algorithms used in data science, practice advanced mathematics, and explore mathematics in application areas like image processing and network analysis.

Machine Learning & AI

Machine learning and artificial intelligence help fill the gap between data science and data analytics. Like data scientists, data analysts use statistics and programming skills to turn raw data into actionable insights. However, data scientists distinguish themselves from data analysts by employing more advanced technologies, such as machine learning and AI. With these skills, they can automate aspects of analysis that previously required human input. For example, they can train machines to clean data, identify typos in data sets, and correct them with minimal human involvement. A human worker might take days or weeks to comb through a list of people and the cities they live in, correcting typos and changing instances of “NYC” and “New York City” to all read the same. A machine trained by a data scientist with machine learning skills can do the same work in seconds.

Increased automation through machine learning and AI allows data scientists to analyze new categories of information. Older data analysis techniques are easily automated for organized data that can fit on a spreadsheet, such as students’ grades or income information for a given area. New forms of data, such as social media posts and videos, are impenetrable to traditional data analysis. However, data scientists can train computer models to analyze Tweets for sentiment, search through thousands of photographs to identify specific plant life, or digitize paper invoices from decades before computers were commonplace in business.

Machine learning and AI are critical skills for data scientists to master, so  Tufts’ online MSCS curriculum focuses heavily on them. Students take CS 135 Machine Learning to survey machine learning methods, such as supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases. They apply those skills in science, engineering, and medicine. Students also take CS 131 Artificial Intelligence, which focuses on the history, theory, and computational methods of artificial intelligence.

Big Data

The ubiquity of big data requires that data scientists be proficient in data extraction and organization. Some of these skills overlap with other areas of data science, such as machine learning, and others are entirely new. In either case, employers are eager to hire data scientists with this specialized expertise. One job market forecast identifies big data analytics as the most in-demand skill for employers in the digital age.

Tufts online MSCS students take Comp 119 Big Data, where they learn new techniques for collecting, processing, and acting on massive data sets. They develop advanced skills in parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. They also apply statistics and machine learning skills to practical big data problems.

Soft Skills

One might assume that data scientists only need to be focused on possessing advanced technical skills. While much of their time focuses on the technical aspect of their work, data scientists also operate in professional settings that prize soft skills. They must be able to work closely with fellow data experts and non-experts. So, skills like communication, empathy, and teamwork are just as essential as machine learning and mathematics. Professional data scientists recognize the value of these non-technical skills. In Anaconda’s State of Data Science survey, 29 percent of respondents noted that communication skills were lacking in their businesses’ data science department.

Tufts’ Online MS in Data Science Prepares Students for Rewarding Careers

Data science is a discipline built on advanced technical skills, such as those covered above, and employers are willing to pay high salaries for professionals who can convert challenging data sets into actionable information that can guide business decisions. Survey data from Kaggle shows that data scientists in the U.S. routinely earn between $150,000 and $200,000. Top firms like Meta and Netflix sometimes offer compensation packages above $250,000 to acquire data science talent with the optimal skill set.

Tufts online MS in Data Science prepares students for challenging careers in the data workforce. In addition to a curriculum focusing on the technical knowledge necessary to succeed in data science, Tufts students complete capstone projects before graduation. Their projects apply data science principles learned in class to real-world problems, culminating in a final report summarizing data gathered, analytic results, lessons learned, and opportunities for future study. 
Students complete their projects and other coursework under the supervision of Tufts’ top data science faculty, who research leading edge topics such as computer vision and machine learning. Tufts data science faculty have been recognized with prestigious awards for teaching and advising, including the Liebner and Seymour Simches awards.