Will I Learn About Data Science in an MSCS Program?

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Data science is an evolving discipline that can be considered an amalgamation of computation, statistics, domain expertise and artificial intelligence. Dedicated data science degree programs are a relatively new academic offering, and many data scientists still enter the field without degrees in data science. One 2017 Burtch Works study found that while 90% of working data scientists had graduate degrees, those degrees were seldom awarded by data-focused programs. Instead, respondents studied engineering, statistics or computer science before becoming data scientists.

Today, professionals who want to transition into data science careers can still choose from a multitude of degree programs at the bachelor's degree and master's degree levels. At the graduate level, there are some standalone data-focused degree programs and more programs that offer data science specializations or are dedicated to artificial intelligence and computer science.

Enrolling in a data science master's program might seem like the obvious path forward for aspiring data scientists, but some Master of Science in Computer Science (MSCS) programs—including the online MSCS offered by the Case School of Engineering at Case Western Reserve University—include data science in the standard computer science master's curriculum. Data professionals who choose these graduate school programs can take advantage of a wider range of opportunities, in and out of data science.

What skills do data scientists use?

IBM and data science firm Anaconda released reports in 2017 and 2021, respectively, identifying skills that data scientists regularly use in their work. Some, like programming, are a core part of most computer science master's curricula. Others, including data mining and data privacy, are less common in computer science MS program coursework and only found in select programs.

Computer programming

In the Anaconda report, 19% of respondents said the most prevalent data science myth was that "data scientists don't know how to code." In reality, data scientists typically work with datasets too large to process manually, so they automate data collection, organization and analysis as much as possible.

Data scientists write new computer programs or modify existing algorithms to deliver the insights they need from the data they have. When Anaconda asked data scientists how frequently they used different programming languages, Python was the most popular language, with 85% of respondents saying they used it sometimes, followed by SQL, R, Javascript and C++.

Data analysis

One of the chief responsibilities of data scientists is to draw meaningful conclusions from raw data. Analysis represents a significant part of data scientists' work, and 35% of their time goes toward selecting, training and deploying analysis models. Comprehensive data analysis requires both algorithmic skills, which help data scientists determine the best algorithms to process a given dataset, and computer science knowledge because data scientists use computer systems to deploy algorithms efficiently and effectively on large datasets.



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Database systems management

Imagine a basketball enthusiast who tracks points per game for the players on their favorite team. They could probably do this with a pencil and paper, at least for a game or two. If the enthusiast wanted to track every player on the team for the whole season to determine who scored the most points, they could use a spreadsheet and finish their analysis very quickly.

But what if they wanted to analyze the data for every NBA team and player? What if they wanted to find the average number of three-point field goals for rookie players in games played at arenas 100 miles away or further from their home court? Working with data for that kind of query requires more advanced database skills.

Data scientists and other professionals who work with Big Data use database-management skills to maintain large datasets and extract relevant data as they need it. They understand how databases work, how to use query languages such as SQL to pull information from large databases, and how to store data so it's secure and accessible.

Data scientists in the Anaconda survey said they spent about 39% of their time on data preparation and cleansing. This work involves adjusting data that is incorrectly formatted, duplicated, incomplete or otherwise erroneous—often by automating data cleaning processes.

Data mining

Data mining, also called knowledge discovery, encompasses the processes used to unearth previously unknown insights in massive datasets using advanced statistical methods and technologies such as neural networks.

Imagine the basketball enthusiast once again. Their favorite team isn't scoring well, but it's not apparent why. They have a vast data trove with information about points scored coupled with a lot of other data, such as where the team played, who they played against and even what the players had for dinner the night before.

Faced with this massive dataset, it's difficult to know where to start to find out why the team is not scoring well. Using data mining techniques powered by machine learning, the enthusiast could tackle the problem and find patterns that would've been invisible otherwise.

Data visualization

Data visualization is an umbrella term for two processes in data science: exploratory visualization and declarative visualization. Exploratory data visualization covers the processes data scientists use to represent data graphically so they can understand it better. For example, data displayed in an iconography of correlations or radar chart may reveal more insight than the same data presented in tables. Declarative visualization covers the data display methods data scientists use to share and explain their findings to stakeholders such as C-Suite executives or investors who may not be data-literate.

Machine learning

In 2017, IBM identified machine learning as one of the fastest-growing skills in data science. Today, it is common for data scientists to use artificial intelligence and machine learning methods and tools to automate data analysis and optimize processing models with limited human intervention. Over time, a data model that uses machine learning can track its own accuracy and adjust its algorithms to produce more accurate analyses or predictions.

Does the typical computer science master's curriculum cover those skills?

Some foundational data science skills, particularly those related to programming paradigms, AI and intelligent systems, algorithm analysis, and design and database systems, are also foundational computer science skills. Many Master of Science in Computer Science programs cover these competencies at a very advanced level. Even so, the typical computer science master's curriculum may not adequately prepare technologists to work confidently with data. The online MSCS program at Case Western Reserve University is one of the few to prioritize data science skills.

The 30- to 34.5-credit hour MSCS program includes core courses in artificial intelligence and data mining, advanced concepts in computer programming, database systems management, algorithms, and data privacy. Specific courses that support data science careers include CSDS 435 Data Mining, which covers the process of discovering interesting knowledge from large amounts of data, and CSDS 433 Database Systems, which focuses on issues related to database management.

Case School of Engineering also retains several faculty members who specialize in data science and bring their expertise to the online computer science master's program. Speaking about his colleagues, Case Western Reserve Associate Professor Michael Lewicki says: "We're applying new techniques to new problems in new areas like data science, figuring out how to use developing knowledge. It's really about the novelty; we push boundaries."



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What else does the computer science master's curriculum cover?

In both pure data science careers and computer science careers, having a wide range of interdisciplinary skills is essential for success.

Top MSCS programs typically have core courses and electives that cover concepts and competencies related to networked systems, database systems, system security and privacy and software engineering. Case Western Reserve's flexible online computer science master's program blends technical training with an education in the kind of transferable human skills that have become especially valuable in technology. Consequently, the online MS in Computer Science program at Case School of Engineering can lead to careers in data science, as well as jobs in DevOps, cloud engineering, information security, computational research, database engineering or solutions architecture.

Additionally, as data science has branched into numerous specialty areas (e.g., data engineering and data warehousing), career pathways in the field have become more integrative. The Anaconda survey solicited responses from cloud engineers, artificial intelligence engineers, software developers, project managers, system administrators and others doing what is ostensibly data science work under various job titles. Graduate programs in computer science and data science are changing to stay aligned with the growing implementation of data science, AI and other technologies.

Can I get a data science job with an MS in Computer Science from Case School of Engineering?

Aspiring data scientists now have many options when it comes to graduate courses and degree programs, and the MSCS may not be the first to come to mind. However, pursuing a master's in computer science is worth it for data professionals who want to ensure they enjoy ongoing career versatility as the technology landscape changes. The online Master of Science in Computer Science program at Case School of Engineering prepares students to excel in many data science, information technology and computer science careers.

This flexible online program is ideal for graduate students with backgrounds in software development, computer networks or other technology fields who want to continue working full-time while advancing their knowledge of computer science and learning to drive data strategy. Students complete coursework in a mix of synchronous and asynchronous sessions that support work-life balance without sacrificing student collaboration and networking opportunities.

A computer science master's may be the better degree for future data science job seekers in a field shaped so profoundly by computer technology. Case Western Reserve's online computer science master's program provides a broader knowledge base that helps data scientists stay adaptable.

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