Program Overview
Mount Union's Bachelor of Science in Data Science and Analytics equips students with the skills to analyze, visualize, and model data. The program emphasizes experiential learning, ethics, and a flexible curriculum that allows for customization. Graduates are highly sought after in various industries, including technology, finance, and healthcare.
Program Outline
Degree Overview:
Overview:
The Bachelor of Science in Data Science and Analytics degree at Mount Union provides students with the skills and knowledge necessary to succeed in the rapidly growing field of data science. The program's curriculum is designed to provide students with a strong foundation in data analysis, visualization, and modeling, as well as the ability to communicate their findings effectively.
Objectives:
- To provide students with a comprehensive understanding of the principles and practices of data science.
- To develop students' skills in data analysis, visualization, and modeling.
- To prepare students for careers in data science and analytics.
Program Description:
The Data Science and Analytics major at Mount Union offers a flexible educational experience that allows students to add a minor or second major from a variety of fields, leading to a customized journey to meet their career goals. Combined with the university's dynamic Integrative Core, students will become well-rounded professionals able to interpret the results of data to technical and non-technical audiences. Impactful internship and faculty-led research experiences are available to all students with a passion for data.
Outline:
Content:
The Data Science and Analytics program at Mount Union covers a wide range of topics, including:
- Data analysis
- Data visualization
- Data mining
- Machine learning
- Big data analytics
- Data ethics
Structure:
The program consists of 46 credit hours of coursework in data science and analytics, as well as 32 credit hours in the university's Integrative Core. Students will also complete a capstone project in their final year.
Course Schedule:
The following is a sample course schedule for the Data Science and Analytics program:
- First Year:
- DSC 140 Data Science Fundamentals
- MTH 141 Calculus I
- BUS 100 Introduction to Business
- Second Year:
- DSC 240 Data Analysis and Visualization
- DSC 250 Scientific Modeling and Data Analysis
- MTH 142 Calculus II
- MTH 223 Intermediate Statistics
- Third Year:
- DSC 330 Data Acquisition and Analysis
- DSC 340 Machine Learning and Neural Networks
- DSC 350 Data Mining
- Elective
- Fourth Year:
- DSC 360 Big Data Analytics
- DSC 440 Data Science Practicum
- Elective
- Elective
Individual Modules:
The following is a brief description of each module in the Data Science and Analytics program:
- DSC 140 Data Science Fundamentals: This course introduces students to the fundamental concepts of data science, including data collection, cleaning, and analysis.
- DSC 240 Data Analysis and Visualization: This course teaches students how to analyze and visualize data using a variety of techniques.
- DSC 250 Scientific Modeling and Data Analysis: This course introduces students to the principles of scientific modeling and data analysis.
- DSC 330 Data Acquisition and Analysis: This course teaches students how to acquire and analyze data from a variety of sources.
- DSC 340 Machine Learning and Neural Networks: This course introduces students to the principles of machine learning and neural networks.
- DSC 350 Data Mining: This course teaches students how to mine data for patterns and insights.
- DSC 360 Big Data Analytics: This course introduces students to the principles of big data analytics.
- DSC 440 Data Science Practicum: This course provides students with the opportunity to apply their skills in data science to a real-world problem.
Assessment:
Assessment Methods:
Students in the Data Science and Analytics program are assessed through a variety of methods, including:
- Exams
- Quizzes
- Projects
- Presentations
- Papers
Assessment Criteria:
Students are assessed on their:
- Knowledge of data science concepts
- Ability to analyze and visualize data
- Ability to communicate their findings effectively
- Ability to apply their skills in data science to real-world problems
Teaching:
Teaching Methods:
The Data Science and Analytics program at Mount Union uses a variety of teaching methods, including:
- Lectures
- Discussions
- Hands-on exercises
- Projects
- Guest speakers
Faculty:
The Data Science and Analytics program is taught by a team of experienced faculty members who are experts in their field. The faculty are committed to providing students with a high-quality education and are always available to help students succeed.
Unique Approaches:
The Data Science and Analytics program at Mount Union offers a number of unique approaches to teaching and learning, including:
- A focus on experiential learning: Students in the program have the opportunity to apply their skills in data science to real-world problems through internships, research projects, and capstone projects.
- A commitment to diversity and inclusion: The program is committed to creating a diverse and inclusive learning environment for all students.
- A focus on ethics: The program emphasizes the ethical implications of data science and prepares students to use their skills responsibly.
Careers:
Potential Career Paths:
Graduates of the Data Science and Analytics program at Mount Union are prepared for a variety of careers in data science and analytics, including:
- Data analyst
- Data scientist
- Data engineer
- Machine learning engineer
- Business intelligence analyst
- Statistician
Opportunities:
Graduates of the program are in high demand by employers in a variety of industries, including:
- Technology
- Finance
- Healthcare
- Manufacturing
- Retail
Outcomes:
Graduates of the Data Science and Analytics program at Mount Union have a high success rate in finding employment in their field. The program's graduates are also well-prepared for graduate
