Master’s in Applied Data Science Degree Programs

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Working in big data requires the application of analytical tools to gather and manipulate information to make the best decisions for an organization. These skills are found within the specialized degree of Applied Data Science, which teaches students the principles and techniques of data analytics and machine learning.

Master's Degrees in Applied Data Science

What is the difference between a traditional data science degree and applied data science?

In many cases, the curriculum for these programs will overlap with many of the same courses, but there may be more of an emphasis in using practical statistical methods.

Many universities provide a full Master's degree within the subject of applied data science, but these courses can also be found in traditional data science degrees, or as Master's degrees within Applied Statistics.

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Review of Online Master's in Applied Data Science Programs

Bay Path University Master of Science in Applied Data Science

This program offers two variations of data science programs - one that is a more general, all-around curriculum that provides education for most data science positions regardless of background, and a specialized track that is mostly geared toward data-driven solutions. The main difference between the two are an emphasis in theory and prior coursework for the specialist track. Students are able to select which path they would like to take after completing the Foundations of Data Science to get more information. Regardless of choice, there are five common courses between both tracks, four specific courses for the chosen path, and three electives selected by the student.

Overall admission requirements to get into the program require a Bachelor's degree with a GPA of 3.0 or higher, official transcripts, an essay that details why the program is necessary for personal and professional goals, an updated resume, and two letters of recommendation. Either pathway can be completed 100 percent online within a span of one to two years and there is no need for GRE or GMAT scores in the admission process. Graduates should be able to handle large data sets to solve real-world situations, determine the best research methods for data-driven decisions, and have a complete understanding of all ethical, legal, and cultural perspectives when it comes to managing information.

Boston University Metropolitan College Master of Science in Applied Data Science

The Metropolitan College has an online program in Applied Data Analytics that is geared for mid-level professionals within the information technology sector. The curriculum features a rigorous schedule in processing data, machine learning, and working with databases. Typical data science courses are available with a number of software programs, such as Python, R, and Amazon and Google cloud services.

In order to get into the program, prospective students must complete an application and submit the fee along with college transcripts, updated resume, three letters of recommendation, and a personal statement. There is no specific degree that is required for entry, but students are expected to have prior coursework in computer science, mathematics, statistics, and software development. An introductory course is offered for these categories, along with Java and Python courses.

Indiana University - Purdue University Indianapolis Master of Science in Applied Data Science

The School of Informatics and Computing also provides a Master's degree within Applied Data Science, giving students the ability to take massive amounts of data and convert it into something useful for the organization they work for. There are 30 credits needed to complete the program with a number of electives available, but not every course is available online. By the end of the curriculum, graduates will be able to demonstrate competency in data analytics (such as test hypothesis and utilize statistical inference), managing data in the cloud, and creating data visualizations.

Admission requirements include a Bachelor's degree with an overall GPA of at least 3.0, submit GRE scores, and have prior coursework in calculus and matrix algebra. Other submissions when applying should include official transcripts, a resume, personal statement, transcripts, and three recommendation letters. Other related programs in the field include a PhD minor within Applied Data Science, a Master of Science in Sports Analytics, and a Master of Science in User Experience Design.

Syracuse University Master of Science in Applied Data Science

The School of Information Studies offers the Applied Data Science program through their Online iSchool, which is very similar to their on-campus program. Courses are distributed live each week with online group projects and homework assignments available online. Graduates that complete the program will be able to identify patterns and develop strategies through predictive models, be able to mine and manipulate data, and have the communication skills necessary to communicate with other members in the organization.

Students have the ability to work with faculty on their projects and have access to research labs. There is an emphasis on having real-world experience in order to jump into the position desired. Admission into the program takes place in the fall and spring semesters. Requirements into the program include filling out an application with fee and submitting a personal statement, official transcripts, GRE scores, and a resume. There is an optional choice to send a video submission that serves as a way to stand out among other candidates.

Sample Courses for Applied Data Science

Especially with degrees under Applied Statistics, there are a number of statistical analytics courses that are part of the core curriculum or under various elective opportunities. Statistical inference is observing data points and forming a hypothesis on properties of a population. Bayesian probability is a form a subjective statistical inference that determines a belief based on the data, like the percentage chances of a team winning a game based on what has taken place up until a certain point. Linear regression is one of the most commonly used forms of predictive analysis, which is determining the best-fitting line within a set of data.

Structured Data

Being able to analyze this information requires the skills and tools necessary to gather it from structured and unstructured data sources. These courses fall under data mining, which is either listed under a data mining course itself or along the following topics: text mining and retrieval, process mining, pattern discovery, and data visualization. All of these resources will give students the ability to find trends and abnormalities within data in order to benefit the organization they work with, and they can also use it for predictive modeling once the data is organized.

Analytics and Programming

Applied data analysis courses, which are often self-titled, is the specific process of applying the skills of data analytics toward real-world situations. Frequent subjects in this course include how use this information toward storytelling to have a major impact when presenting findings, how to analyze data through the lens of legal and ethical practices, and hands-on experience with software programming, such as SAS, R, and Python.

How to Begin a Career in Data Science

Starting a career in data science will typically require at least a Bachelor’s degree in a related field, such as data science itself or statistics, mathematics, and computer science. These undergraduate degrees will take either three to four years to complete. Higher education, such as a Master’s degree, will add one to three years based on availability. These programs can usually be completed without needing to quit a full-time job, meaning coursework is completed during the evening or weekend hours, or online to fit any schedule needs.

Just as important as the education are the skills required to complete big data tasks. An education will help prepare students on what to expect, but they must acquire the ability to analyze data. This includes critical thinking, researching, manipulating data, mining, testing theories, and ensuring the accuracy of the data. Knowing various programming and software tools is very important. Python, R, SQL, Tableau, and Excel are some of the more popular options out there. Knowing one or more of these tools will at least demonstrate hard skills to the employer, even if they use other tool sets.

While technical skills are very important in this profession, and being able to critically analyze information and work with the applications involved in the field, soft skills are also needed, such as communication. Not only do scientists need to present information, but they must be able to work with other members on the team. They may have to work with engineers to help build and maintain the architecture, or to other analysts in order to work with unstructured data and to fill in any potential gaps with their findings. Communication is certainly key if the scientist’s role is more administrative or managerial when guiding a team in the information technology sector.

For those that do not have the time or resources to commit to a full-time higher education program, there is the ability to gain certification. These programs offer a truncated curriculum that frequently have a few core courses based on the subject and electives to choose from. These are typically four to five courses long and can be completed within a year. For example, the University of California-Berkeley offers a fully online Graduate Certificate in Applied Data Science. Prerequisites include having a basic understanding in Python programming and statistics, and the curriculum includes an introductory data science course, one course in applied methods and techniques (such as machine learning), and an elective. All courses need to be completed with a B or higher.

Important Applied Data Science Organizations and Associations

Data Science Association

The Data Science Association has a mission to increase diversity and improve ethics within the professional data science landscape. Generally, there is an annual conference that focuses on a pure experience instead of being filled with vendors and recruiters, giving people an opportunity to learn about any of data science, from data mining and processing to deep analytics and complex machine learning. The home website offers many links to resources such as news, academic papers, coursework, and various upcoming events within big data.

Association for Computing Machinery

The Association for Computing Machinery has a chapter dedicated to data science known as the Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). Their goal is to create the standards of knowledge discovery and data within the market. A membership provides discounts on the annual conference that features workshops and various tutorials over a week-long stretch, other conferences affiliated with KDD, and a subscription to the community newsletter. The ACM is a nonprofit founded back in 1947 and has over 100,000 student members, and there are various special interest, professional, and student chapters across 56 countries around the world.

Applied Data Science Career Options

Applied Data Scientist

Figures out organizational solutions through the use of extracting digital information with analytical tools. Duties will be different depending on the company and how much the traditional data scientist is tasked with. Some of these needs include the ability to mine unstructured and structured data and query this information from databases created by engineers. Data must be cleaned to eliminate errors and unnecessary information before transferring it to analytical modeling purposes. Data scientists may also need to build algorithms and discover trends or make predictions based on the data they have. This information is then presented to higher-level professionals, which includes creating charts that easily depict this information and reports explaining potential solutions to problems or cost-cutting strategies. The average salary for a typical data scientist is around $121,000 per year according to Glassdoor.

Machine Learning Researcher

Part of the research and development segment of machine learning and artificial intelligence within an organization. They will focus on subjects like natural language processing and reinforcement learning, and typical duties will include presenting information for other employees or at conferences. Their goal is to figure out problems and issues that arise in the development process. The biggest skills needed for this position include intermediate mathematics and theory, programming skills in software like Python, the ability to develop machine-learning algorithms, and a vast knowledge of statistics. The average salary for a machine learning researcher is around $111,000 per year according to Glassdoor.

Masters of Data Science Degrees

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