Bachelor of Data Sciences at Pennsylvania State University

Data Sciences is a field that explores the methods, systems, and processes used to extract knowledge from data and turn these insights into discoveries, decisions, and actions. The emergence of massive amounts of data – also known as "big data" – found in our world through healthcare records, human sensors, digital media, and a number of other sources has increased the need for individuals who can obtain useful knowledge from big data and apply it to address major societal challenges across a variety of fields. Students pursuing this degree will develop the knowledge and skills needed to manage and analyze large-scale, unstructured data to address an expanding range of problems in industry, government, and academia.

MORE INFORMATION ABOUT DATA SCIENCES

You Might Like This Program If...

• You are curious about analyzing information to discover new insights.
• You want to apply data analytics to make strategic decisions.
• You want to understand how data can be used to visualize phenomena and predict different outcomes.
• You are interested in statistics, mathematics, and the social sciences, and want to combine these disciplines to understand what data is really telling us.

MORE INFORMATION ABOUT WHY STUDENTS CHOOSE TO STUDY DATA SCIENCES

Data Sciences is an interdisciplinary field concerned with the integration of methods, processes, systems, and tools from Computer Science, Informatics, and Statistics, to discover, validate, and apply knowledge and actionable insights from data, across a broad range of application domains. The curriculum for the major is designed to equip students with the knowledge and the skills needed to elicit, formulate, and solve data sciences problems using modern computer science, informatics, and statistics tools for data management, machine learning, information integration, and predictive modeling, and effectively communicate their findings to a broad range of stakeholders. The students will gain the critical analytical skills needed to assess the feasibility, benefits, limitations, risks, and ethical implications of applying data sciences methods in different settings. Through experiences such as the capstone project, students should be prepared to function effectively as members of interdisciplinary data science teams to harness the potential of data to enable discovery, optimize products and processes, and inform public policy. The students in the major will specialize in one of the following options: applied, computational, or statistical modeling data sciences, as described below.

Applied Data Sciences (DATSC_BS)

Only available through the College of Information Sciences and Technology

This option focuses on the principles, methods, and tools for assembly, validation, organization, analysis, visualization, and interpretation of large and heterogeneous data, to support data-driven discovery and decision making, with emphasis on addressing pressing scientific, organizational, and societal challenges. A combination of required and elective courses provides students with the training and skills needed to develop advanced tools and domain-specific analyses that yield actionable knowledge from data. This option also provides critical analytical skills needed to assess the benefits and limitations of data analytics across a broad range of applications involving Big Data.

Computational Data Sciences (DTSCE_BS)

Only available through the College of Engineering

This option focuses on the computational foundations of the data sciences, including the design, implementation and analysis of software that manages the volume, heterogeneity and dynamic characteristics of large data sets and that leverages the computational power of multicore hardware. Students in this option will take upper-level courses in computer science and related fields to develop the skills necessary to construct efficient solutions to computational problems involving Big Data.

Statistical Modeling Data Sciences (DTSCS_BS)

Only available through the Eberly College of Science

This option focuses on statistical models and methods that are needed to discover and validate patterns in Big Data. Students in this option will take upper-level statistics and mathematics courses, learning to apply the theoretical machinery of quantitative models to the solution of real-world problems involving Big Data.

1. Knowledge: Understand the technical fundamentals of data sciences with a focus on developing the knowledge and skills needed to manage and analyze data to solve problems in our world.
   1. Integrate statistical concepts/methods and computational/machine learning methods to discover the structure of data and build predictive models.
   2. Apply the principles of data management to organize and use different types of data, both structured and unstructured.
2. Problem-Solving and Evaluation: Identify, formulate and solve data science problems that arise in various applications.
   1. Identify and incorporate relevant abstraction and domain knowledge to formulate data science problems in different application contexts.
   2. Design or adapt appropriate statistical, machine learning, and other data science methods for solving specific problems.
   3. Compare, contrast, and evaluate competing data science methods appropriate to the context of the problem.
   4. Employ modern computing infrastructure to scale up data science methods for massive and complex data.
   5. Integrate data from multiple sources while considering the best practices, challenges, and pitfalls of using heterogeneous data to solve problems.
3. Communication: Articulate the benefits, risks, formulation, solution, and results of data science projects to diverse stakeholders, including fellow data scientists, collaborators with subject matter expertise, and the general public, using written, verbal, and visual forms.
4. Teamwork: Participate effectively on teams in order to accomplish the goals of a project containing data science components.
5. Data Ethics: Critically evaluate and conscientiously respond to the ethical and societal implications of data science practice.
   1. Analyze the potential human impacts of data-driven technologies, especially for marginalized communities.
   2. Develop strategies to solve data science problems that reflect shared social and ethical values, such as privacy, security, fairness, and accountability.
   3. Interpret and apply the ethical responsibilities of computing professionals.
   4. Ensure reproducibility of data science analyses.  
6. Lifelong Learning: Recognize the importance of continued learning beyond graduation.
   1. Demonstrate readiness to join an evolving professional community by participating in professional development, such as reading trade journals and engaging with appropriate professional organizations.
   2. Demonstrate readiness for independent learning by performing literature reviews and staying abreast of current trends within the field of data science.
7. Option Objectives:
   1. Applied Data Sciences Option: Gain in-depth knowledge in a chosen application focus area and demonstrate skills to formulate and solve data science problems in the context of applications in that area.
   2. Computational Data Sciences Option: Design, development, and analysis of software (computational solutions) for data science problems.
   3. Statistical Modeling Data Sciences Option: Demonstrate facility with common regression-based inferential modeling techniques including analysis of variance, generalized linear models, multiple regression, and logistic regression, as well as proficiency in basic statistical optimization and simulation techniques.