The Master of Science in Applied Sports Science Analytics is designed to provide students with the knowledge and skills to work successfully with athletes, coaches, sports medicine professionals, and athletic organizations. Students will learn methods used in performance data collection and analysis to improve health and human performance. Graduates enter the workforce as collaborative problem solvers who are capable of leading and managing within the field of data analytics for sports performance.
The M.S. in Applied Sports Science Analytics offers a dynamic collaboration between exercise science, statistics, and data science. The interdisciplinary nature of the degree provides students with the analytic skills to develop new applications and interfaces for large and complex sport and human performance datasets. When combined with foundational knowledge in exercise science, graduates possess the skill set to aid in the analysis and interpretation of the results to various levels of industry professionals from the athlete to the coaches and doctors, to the general manager and C-level executives.
What will I learn with an M.S in Applied Sports Science Analytics?
- Develop innovative training principles by combining key sports science and data analysis concepts
- Information technology concepts for manipulating, storing, and analyzing big data
- Design, implement and develop machine learning algorithms
- Design and evaluate interactive visualization for data analysis
- Apply statistical learning methods for data mining and inferential and predictive analytics for sports science
- Enhance human performance through evidence-based interventions and applied research and practical skills
- Examine the role that strength and conditioning techniques play in enhancing performance, preventing injuries, and accelerating rehabilitation
- Understand the joint structure, joint function and biomechanical principles underlying the kinetics and kinematics of human motion
Learn from Real-World Environments
In alignment with Lasell University's Connected Learning Philosophy students are encouraged to develop their ideas and test them in real-world environments with practical relevance through the completion of a research-based capstone relevant to a problem of practice.
Advance your career
According to the Bureau of Labor Statistics, the occupational outlook for mathematicians and/or Statisticians is projected to grow 30% from 2018 to 2028. The median salary is $91,160 for statisticians. Graduates from the program pursue careers in:
- Athletic Data Analytics
- Elite Sports Coaching
- Performance Management
- Sports Science
- Strength and Conditioning
- Talent Development
- Director of High Performance
- Sports Statistical Analyst
Why pick Lasell?
Join Lasell's vibrant community of working professionals and gain access to a wide range of resources to develop your expertise and expand your network. Your graduate student journey will be distinguished by a personalized and supportive learning environment. As a Lasell graduate student, you will:
- Online Experts - Lasell has been delivering high-quality, online programs for over 15+ years.
- Career-Focused - Our programs are designed with the expertise of industry leaders and faculty incorporating real-life organizational needs into the curriculum.
- Dedicated Support - Our Enrollment Counselors and Academic Advisors are dedicated to helping you reach your goals. They'll build your plan, cheer you on, and coach you through to your graduation.
- Faculty that know you by name - Lasell's small class size ensures that your faculty will know you and be invested in your success.
- Engage with an active alumni community - You can leverage your peers to network for advice, ideas and jobs.
- Career Services - Lasell's Career Development Team is ready to work with you from Day 1 to create a career plan, launch a new job search, negotiate a promotion and more.
- Resources at your fingertips - Lasell provides a robust array of student services: the Academic Achievement Center, Tutor.com, the Brennan Library, and the Career Development Center.
Created with input from sport science professionals working with professional athletic teams and sport science device manufacturers, the curriculum combines evidence-based research and practical application in sports from a health and human performance perspective.
Below is the curriculum for the Applied Sports Science Analytics program plus two electives. One (1) elective must be an NHP or RSCI course. The remaining elective can be any MSAT, RSCI or NHP course.
Please note: DSCI704 Data Analytics is a prerequisite for DSCI705 Visualization Design, Analysis, and Evaluation and EXSC702 Sport Performance Analysis.
This course covers data science concepts, techniques, and tools to support big data analytics, including cloud computing, SQL, parallel algorithms, nonrelational databases, and high-level language support. The course applies the MapReduce programming model and virtual-machine utility computing environments to data-driven discovery and scalable data processing for scientific applications.
This is an introductory course in the design and evaluation of interactive visualizations for data analysis. Topics include human visual perception, visualization design, interaction techniques, and evaluation methods. Students develop projects to create their own web-based visualizations and develop competence to undertake independent research in visualization and visual analytics.
This course examines the role that science and evidence-based practice play in enhancing human performance in high-performance settings. A distinctive feature of the subject is its focus on developing students' knowledge, decision-making, and applied research skills required to plan and deliver evidence-based interventions for athletes to enhance health and performance. There is a strong focus on preparing graduates with relevant practical skills that high-performance sport practitioners use. Students will be taught to translate advanced concepts to professional practice by applying critical thinking, independent learning, and effective communication.
This course applies statistical learning methods for data mining and inferential and predictive analytics to the field of sports science. The course also introduces techniques for exploring and visualizing data, assessing model accuracy, and weighing the merits of different methods for a given real-world application. Emphasis is placed on the analysis, interpretation, and communication of data essential in creating a toolset for transforming large, complex sports science informatics datasets into actionable knowledge. Pre-requisite: Data Analytics
This subject examines the role of resistance training, aerobic/anaerobic conditioning, and other contemporary techniques that are used to prepare high-performance athletes. Students develop a strong understanding of the fundamental principles that underpin the training response and learn to critically evaluate emerging techniques within the strength and conditioning field. Additionally, this subject equips students with an in-depth understanding of training prescription that can be utilized in any field that prescribes exercise (e.g., exercise physiology, rehabilitation, and injury prevention). The strategies and interventions explored in this course focus on enhancing performance and reducing injury risk for high-performance athletes. Students will engage in problem-solving and critical thinking activities for independent learning and the effective translation of expertise into practice.
The course involves a study of joint structure, joint function, and the biomechanical principles underlying the kinetics and kinematics of human motion, including normal gait and human movement. Emphasis is placed on the interaction between biomechanical and physiological factors in musculoskeletal and neuromuscular function, and the application of kinesiological principles to clinical rehabilitation practice.
This course provides an overview of foundations of research design and the uses and interpretation of results. Content includes: reviewing the literature, developing research problems/questions; hypothesis testing, experimental, quasi-experimental and other research designs; and evaluating research studies as they relate to evidence-based practice in the health professions. The intended outcome is to familiarize students with the evidence-based guidelines associated to clinical outcomes and evidence-based practice.
Through the completion of a research project, on a topic within the field, this course serves as an essential outcome component to augment the professional development and new learning that occurs in didactic course work and demonstrates the ability of the graduate to make significant contributions to their professions. Pre-requisite: must be taken in final semester of program. Prerequisite: RSCI780
An introduction to the ethical and social consequences of collecting, curating, and analyzing data in academia, public and private contexts. A socio-technical stance is taken in unpacking issues of algorithmic biases, fairness, transparency, and accountability. Additionally, students develop a strong understanding of responsibilities and issues associated with the culture of the sports science field. Students will develop an understanding of the importance of effectively and accurately communicate data in sports environments. Students will practice building effective work environments and develop innovative training principles through the use of key sport science concepts.
This course covers the concepts of information technology used in manipulating, storing, and analyzing big data. Students gain an understanding of the tools used for statistical analysis, R, Python, and several machine learning algorithms for application in an industry setting. Emphasis is on designing, implementing, and developing machine learning algorithms. Particular focus is placed on interpretation and visualization of results.
Candidates seeking admission to Lasell University's MS in Applied Sport Science Analytics program must hold a bachelor's degree from an accredited institution and demonstrate through academic background and/or work experience the ability to succeed in graduate studies. GRE/GMAT scores are not required for admission. The TOEFL may be waived for international applicants who have earned a bachelor's degree at an accredited college/university in the United States, United Kingdom, Australia, New Zealand, or Canada. All other applicants must submit a TOEFL/IELTS score.
Admission Requirements Checklist:
- Online application
- Official transcripts of all college-level coursework*
- A one-page personal statement describing your goals, strengths and potential for achievement in graduate school
*A cumulative grade point average of 3.0 is recommended for recent college graduates with fewer than 3 years of professional work experience.
Materials should be emailed to firstname.lastname@example.org.
The Office of Graduate Enrollment
1844 Commonwealth Ave.
Newton, MA 02466