Online Computer Science Degree Programs with Data Science Concentration
Last Reviewed/Updated: October 1, 2020
Data scientists that are looking for an overview of information systems, and computer hardware and software theory and advanced skills, should consider a Master of Science in Computer Science. Gaining an emphasis in data science and/or analysis will give professionals additional skills in interpreting raw data and building algorithms needed to quickly manage input.
What is Computer Science with Data Science?
Individuals with an interest in studying both theory and gaining the technical skills in computer hardware and software should consider the computer science discipline. Technology has continued to advance and is adopted by many communities, making it an important priority for many on a daily basis. Computer science professionals are tasked with the ability to continue enhancing and solving various problems that inevitably shows up in technology.
At the Master's level, this education prepares students for more advanced skills to use in their computer science profession. Subjects will vary based on program selected, but course topics will often explore programming, different research methods, and the adaptation of machine learning and artificial intelligence. A data science emphasis includes the ability to build databases that hold raw data and extract important information from these sources to improve an organization.
In addition, soft skills can be obtained, such as being able to communicate efficiently with team members and being able to convert specific data in understandable context to other departments. Executives that have an ability to make important changes will need informed positions and will not have the expertise or specific knowledge that specialized professionals will have.
Online Master's Degrees in Computer Science
There are many opportunities for students to gain an online education for their Master's degree in computer science. Graduate-level education is increasingly moving toward a virtual environment as it gives working professionals the convenience to gain this degree without committing to a physical location. Asynchronous formats allow students to study at their own pace when they have no other obligations.
When a student pursues a program with an asynchronous format, they typically have an ability to select how many courses they want to take at one time and can opt for full-time or part-time enrollment. In other class structures, students may take one course at a time in an accelerated format, lasting around seven to eight weeks. Lectures can be viewed at any time, but course assignments and exams will frequently have set deadlines as students still follow a cohort.
Students should review a program's technical requirements when deciding what program will fit best for their needs. Many institutions will require a modern computer or laptop with high-speed internet access in order to stream large lecture videos or access various assignments that may require more processing power. Various applications will be needed and is based on program, but these will usually be provided in the academic cost or they are initially free to install and access.
Computer Science Degrees with Data Science Concentration
Featured Online Data Science Programs
Johns Hopkins AAP
|MS in Data Science and Policy||Website|
George Mason University
|Master of Science in Data Analytics Engineering||Website|
Northern Illinois University
|Master of Science in Data Analytics||Website|
University of California Berkeley
|Master of Information and Data Science||Website|
|BS in Data Analytics||Website|
Colorado Technical University
One of the most convenient programs to gain the education necessary in big data is pursuing a Master of Science in Computer Science with a concentration of Data Science at CTU. Students will gain competency in programming language such as Python and R, progressive analytics, and data mining. In addition to Data Science, students can consider concentrations in Cybersecurity Engineering and Software Engineering.
48 total credit hours are needed to fulfill curriculum requirements. This is divided up into 28 credit hours worth of core courses and 20 credit hours toward the Data Science concentration. Examples of programs that must be taken in the concentration include Data Warehouse, Data Analytics with R, Algorithms for Data Science, and an additional elective within the 600-level of courses.
This program is available to pursue online with added convenience of CTU Mobile. Students are able to access all course material through the application on computers or Android and iOS devices, and participate in class discussions and live chat. For mobile users, they can set push notifications to keep up with assignment due dates and discussion board updates.
A Bachelor's degree with a background in computer science is recommended. Prospective students that do not have proper prior education will need to complete foundational courses in computer science or provide other ways to waive this requirement. New admissions take place every few months, and courses are at an accelerated pace of five and a half weeks to complete.
The College of Computing and Digital Media offers a Master of Science in Computer Science for students that are looking at an on-campus or online program with an emphasis in Data Science. Online courses are available to all students across the country with no on-campus requirements. Graduates from this particular program have gone on to work at Facebook, Microsoft, Allstate, and Disney Interactive.
Both formats are completely identical to each other with the same faculty and regardless of path, student will have access to recorded class lectures and professor notes. For students that live within the Chicago, Illinois, campus have the ability to pursue a hybrid option for a blend of online and on-campus courses when convenient. The college utilizes the Desire2Learn learning management system.
To be administered into the program, prospective students should have a 2.5 cumulative GPA with their Bachelor's degree from an accredited institution. Official transcripts should demonstrate completion of this, and there is an optional opportunity to submit letters of recommendation or professional work resume. Students are enrolled into the program in all four quarters of the calendar year.
While a Bachelor's degree is needed, having a background is computer science is not. This makes the program at DePaul ideal for those that are looking to make a career change into the computer science and data science field. Studio CHI is another student benefit that offers free seminars and training workshops to enhance education within the program.
Austin Peay State University
The Department of Computer Science and Information Technology, in conjunction with the mathematics department, offers a Master of Science in Computer Science and Quantitative Methods. There is further emphasis with a Data Management and Analysis concentration available for students to pursue. This can be completed either online or at the Clarksville campus in Tennessee.
Students will be exploring traditional computer science courses with a mixture of data science concepts and application, including data mining, modeling, and programming languages. There are a few leadership and statistical subjects that will be covered early in the curriculum. No opportunities exist for students to choose their own electives.
33 credit hours are needed to fulfill curriculum requirements for this concentration, and this is usually spread out over two years. Students can opt to complete an additional course during the summer to help with course workload and meet course prerequisites within the program. For example, Database Management Concepts must be completed before taking Data Management Applications and Data Mining Applications.
An alternative to the traditional Master's degree is the Professional Science Masters in the same discipline and concentration. Many courses will have similar topics, such as leadership skills, database management, and data mining. Differences include SAS programming instead of Python programming and an internship is needed in the second year, which is generally taken in the summer.
University of Louisville
The JB Speed School of Engineering has a 100 percent online curriculum for the Master of Science in Computer Science. 30 credit hours are needed to complete the program, and as an alternative, there are two graduate certifications that students can consider online. These can bolster a student's opportunity in the job market, or they can use credit earned in the certificate toward a full degree later.
One of the certifications is available in Data Science, and six courses (18 credit hours) are needed over a span of one year. Three initial courses are required for all students and covers the popular topics of data management, mining, and linear modeling. The other three courses are elective opportunities, such as Artificial Intelligence, Design and Analysis of Computer Algorithms, and Introduction to Bioinformatics.
For the Master's degree, students will choose between the thesis-based or project-based version of the program to complete. Their pathway will culminate with that decision and they will need to defend their thesis or present their written or verbal project. The curriculum is divided up into foundational topics on automata theory and algorithms, computer software, and analytics.
Prospective students can enter the program in the fall, spring, and summer semesters, and are admitted on a rolling basis. Applicants will not need to submit their Graduate Record Exam (GRE) or Graduate Management Admission Test (GMAT) scores in the process, but should have a Bachelor's degree with a 2.75 minimum GPA. Up to six credit hours can be transferred from regionally accredit programs and if they pass review from program directors.
University of Southern California
The USC Viterbi School of Engineering offers a Master of Science in Computer Science with a specialization in Data Science. The format for the program is identical regardless of pursuing the online or on-campus version. Students must complete the program with a 3.0 cumulative GPA or higher, and a maximum of four credit hours can be taken at the 400 course level with the remainder at the 500-level or higher.
32 total credit hours are needed to fulfill curriculum requirements, and this is divided up into 12 credit hours for the core subjects, at least nine credit hours for the specialization, and at least eight credit hours worth of electives. Changes in requirements for each section depends on the amount of credits taken - less concentration credits will require additional elective credits.
For the online program, students will choose the specialization of group of Data Systems or Data Analysis. Examples of courses in Data Analysis includes Machine Learning, Foundations and Applications of Data Mining,a nd Applied Matrix Analysis. Additional electives at the 500 and 600-level include Information Visualization and Optimization Theory and Techniques.
Holding a Bachelor's degree with a background in computer science is recommended. Applicants in a different discipline will need at least three computer science and one CS-related mathematics course as prerequisites. Application requirements include transcripts showing these accomplishments, personal statement with career goals, resume, and GRE scores.
Sample Courses in the Data Science Concentration
Python and R Programming
Two of the most popular programming languages found in data science careers is Python and R. These open-source, object-oriented software options are used to build algorithms and thoroughly analyze collected data that can be used in a variety of ways. Python is a general purpose language that has been adopted by the field, while R caters more toward statistical analysis.
Part of the process in turning raw data into information that can be utilized by an organization is gathering that data in structured and unstructured form. Students will discover the differences between them and gain the ability to find patterns and trends in this information. They will present these findings to the company they work with in order to get ahead of the competition, enhance their relationship with various clients, and offer better goods and services.
Qualitative and Quantitative Research
These are two different research methods that are generally taught in different courses and have different concepts behind them. Quantitative methods are the investigation of numerical data and is more structured. This can be getting specific numbers on a survey or poll that has been distributed throughout the community.
Qualitative research is unstructured and not based around numbers. This is where students look to find the definition or reasoning that the information is provided as is. For example, why are there more poor people in certain urban communities within a big city, or what impacts an amount of traffic accidents that suddenly rise or decrease over time?
Leadership and Communication
One of the most important soft skills to have as a data analyst is being able to explain findings to other team members or those working at an executive level. As others may not be as fluent as finding patters, data visualization is a good way to specifically showcase information found in raw data. Master’s programs will also emphasize leadership skills, such as forming and guiding teams on various projects, as they are more geared for executive positions.
Artificial Intelligence and Machine Learning
A booming aspect of data science is improving the efficiency of AI and machine learning. This is automatic information input and how to create algorithms that sort this data for an organization. There are many ways to gain both structured and unstructured data based on daily human activity, and as this information grows, it is impossible to organize this information manually.
These courses will review theory and discuss the positives and negatives of how both have been implemented in our technology throughout history. Typically, there will also be discussion on how to improve these concepts, such as a device understanding what topics the user wants in an aggregated news feed, and how this can be accomplished in the programming.
Career Opportunities in Computer Science and Data Science
Graduates with a Master’s degree in computer science have gone on to take lucrative positions within information technology. According to PayScale, the average salary for professionals that hold a Master’s degree in computer science is $102,541. When looking at specific positions, software developers see approximately $82,000 on average, data scientists are estimated at $98,000, and senior positions in engineering and development are above the degree average at $108,000 annually.
While a computer science degree will focus on technical aspects of the career, students still learn analytical concepts in the data science concentration. This will be valuable in a statistician role or business analyst position that looks through company data and interprets this information to other departments in the organization. By doing this, companies can gain an advantage against competition by cutting costs or they can improve their supply chain with enhanced manufacturing.
These are technical roles that develops, maintains, and processes data within an information system. For example, an application that records purchase transactions or may send push notifications on deals for prior goods or services requested runs through a system that a data engineer created. This is a broad role that is divided into many different positions at larger organizations, so responsibilities will vary.
Machine Learning Scientist
Computer science degrees with an emphasis on data analysis can prepare students to get into artificial intelligence and machine learning opportunities. An ML scientist’s role (also known as research analyst) will analyze algorithms that take raw data and how efficient is it able to produce valuable information. Frequently, regression methods and quantitative analysis will be used to create reports and determine if there are any changes that can be made to improve the algorithm.
Organizations and Associations in Computer Science and Data Science
Professionals in computer science have an abundance of resources and associations to connect with to improve their skill set and network with others. The International Association of Computer Science and Information Technology (IACSIT) provides a number of conferences and skill workshops for professionals to accomplish these needs. Academic papers published by the association and keynote speakers at these events will share expertise and innovative ideas to enhance this profession across the world.
Many professional information technology fields are covered by this association, such as business, environment, bioinformatics, and education. Having optimal information systems in any organization will improve their functions and bring them better information to work with. Members have the ability to gain continuous education with workshops or submit an academic paper.
Other benefits include gaining entry into conferences at a discounted rate and to be part of a conference committee. Holders of a Master’s degree are eligible to become a traditional member, but a limited number of new applicants are accepted each year. Senior members will generally require a Doctor of Philosophy in computer science or a related field.