What should you expect?
Students in the data science program will be prepared for jobs dealing with data in whatever fields they are interested. With an emphasis on practical skills for the organization, analysis, visualization, and presentation of actionable information gathered from widely varied data sources, data science will work with students on real world data. Students will take a variety of courses in data science, computer science, statistics, and in a cognate area of their choice.
A Sampling of Courses
- Machine Learning
- Big Data
- Intro to Data Science
- Discrete Structures
- Calculus I
- Linear Algebra
- Intro to Probability and Statistics
- Multivariate Stats
- Comp Science I
- Database Management Systems
- Information Visualization
- Data Acquisition
- DS Consulting (writing)
- Statistical Consulting
12 credits in a cognate area:
As part of the POE in data science you can participate in internships at locations such as Mutual Benefit Corporation or Juniata’s Office of Advancement.
What your four years in the Data Science Program at Juniata College might look like:
- Take Introduction to Data Science (DS 110), Discrete Structures (MA 116), Computer Science 1 (CS 110), and Calculus (MA 130).
- Begin exploring other fields such as business, biology, environmental science, psychology, or history as a possible area to apply your data analysis skills, a cognate area.
- Take Data Acquisition (DS 210), Linear Algebra (MA 160), and Introduction to Probability and Statistics (MA 220).
- Start taking courses in chosen cognate area.
- Take upper level courses in data science, computer science, and statistics. Continue taking cognate area courses.
- Consider studying abroad at the Mathematical Sciences Semesters at Guanjuato, Mexico.
- Look into internships
- Participate in DataFest
- Take Data Science Consulting (DS 325) to have capstone in Data Science of a real life data analysis project. Continue taking upper levels and finish your cognate area courses.
- Complete an internship.
- Participate in Data Fest.
Data Science connects multiple disciplines, so courses come from multiple departments.
Data Science Specific Courses
Introduction to Data Science (DS 110, 3 credits, N, fall semesters)- This course introduces the student to the
emerging field of data science through the presentation of basic math and statistics
principles, an introduction to the computer tools and software commonly used to perform
the data analytics, and a general overview of the machine learning techniques commonly
applied to datasets for knowledge discovery. The students will identify a dataset
for a final project that will require them to perform preparation, cleaning, simple
visualization and analysis of the data with such tools as Weka and R. Understanding
the varied nature of data, their acquisition and preliminary analysis provides the
requisite skills to succeed in further study and application of the data science field.
Prerequisite: comfort with pre-calculus topics and use of computers.
Data Acquisition (DS 210, 3 credits, spring semesters) Students will understand how to access various
data types and sources, from flat file formats to databases to big data storage architecture.
Students will perform transformations, cleaning and merging of datasets in preparation
for data mining and analysis.
Prerequisites: CS 110 and DS 110.
Data Science Consulting (DS 325, requires DS210 and an introduction to statistics class, N, CW, fall semesters)-
The participating students will receive training during the semester in consulting
on statistical problems and to assist in collaborative efforts with faculty and/or
staff on client-partnered projects that are pre-determined. The semester long project
provides the student with both real work experience in the field of statistics and
a project-based learning experience in partnership with the client. May be taken multiple
times for credit. This course meets the CW requirement and is a community-engaged
learning (CEL) course.
Prerequisites: DS 210 and MA220 or BI305 or PY214 or EB211 or permission of the instructor. Offered concurrently with Statistical Consulting (MA 325)
Machine Learning (DS 352, 3 credits, fall every other year, next offering Fall 2020)
This course considers the use of machine learning (ML) and data mining (DM) algorithms
for the data scientist to discover information embedded in datasets from the simple
tables through complex and big data sets. Topics include ML and DM techniques such
as classification, clustering, predictive and statistical modeling using tools such
as R, Matlab, Weka and others. Simple visualization and data exploration will be covered
in support of the DM. Software techniques implemented the emerging storage and hardware
structures are introduced for handling big data.
Prerequisite: CS 110, DS 110, and an approved statistics course: MA 220, BI 305, PY 214 or EB 211.
Big Data (DS 375, 3 credits, spring every other year, next offering Spring 2021) - Management and processing of large data sets, structured, semi-structured and unstructured are addressed, with a focus on Hadoop and NoSQL frameworks.
Internship (DS490/495, Variable)-Internships in data science
Specific Courses from Other Departments
Computer Science I (CS 110, 3 credits, every semester)- An introductory study of computer science software
development concepts. Python is used to introduce a disciplined approach to problem
solving methods, algorithm development, software design, coding, debugging, testing,
and documentation in the object-oriented paradigm. This is the first course in the
study of computer science.
Recommended programming experience or IT110 or IT100, IT111 or IM110 or MA103 but not necessary.
Database Management Systems (CS 370, 3 credits, requires CS 110, fall semesters)- Focuses on concepts and structures necessary to design and implement a database management system. Various modern data models, data security and integrity, and concurrency are discussed. An SQL database system is designed and implemented as a group project. Prerequisites: CS110.
Information Visualization (IM 242, fall every other year, next offering Fall 2019)- This course considers the various aspects of presenting digital information for public consumption visually. Data formats from binary, text, various file types, to relational databases and web sites are covered to understand the framework of information retrieval for use in visualization tools. Visualization and graphical analyses of data are considered in the context of the human visual system for appropriate information presentation. Various open-source and commercial digital tools are considered for development of visualization projects. Prerequisite: IT 110, IT 111, IM 110, DS 110, or CS 110 or permission.
Calculus I (MA 130, 4 credits, every semester)- An introduction to calculus including differentiation and integration of elementary functions of a single variable, limits, tangents, rates of change, maxima and minima, area, volume, and other applications. Integrates the use of computer algebra systems, and graphical, algebraic and numerical thinking.
Discrete Structures (MA/CS 116, 4 credits, fall semesters)- Introduces mathematical structures and concepts such as functions, relations, logic, induction, counting, and graph theory. Their application to Computer Science is emphasized.
Linear Algebra (MA 160, 3 credits, requires MA 130, all springs, every other fall, next offering in Fall 2020)- An introduction to systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues, and applications. Prerequisites: MA130.
Introduction to Probability and Statistics (MA 220, 4 credits, requires MA 130, all springs, every other fall, next offering in Fall 2019)- An introduction to the basic ideas and techniques of probability theory and to selected topics in statistics, such as sampling theory, confidence intervals, and linear regression. Prerequisite: MA130.
Multivariate Statistics (MA 321, 3 credits, requires an introductory statistics class and MA 130 or 160, spring every other year, next offering Spring 2020) -A class in multivariate statistical techniques including non-parametric methods, multiple regression, logistic regression, multiple testing, principle analysis. Prerequisites: An introductory statistics course ( MA220 or BI305 or PY214 or EB211) and linear algebra (MA 160) or Calculus 1 (MA 130)
Statistical Consulting (MA 325, 3 credits, requires an introductory statistics course, falls)- The participating students will receive training during the semester in consulting on statistical problems and to assist in collaborative efforts with faculty and/or staff on client-partnered projects that are pre-determined. The semester long project provides the student with both real work experience in the field of statistics and a project based learning experience in partnership with the client. May be taken multiple times for credit. Prerequisites: MA220 or BI305 or PY214 or EB211 or permission of the instructor. This course meets the CW requirement and is a community-engaged learning (CEL) course. Runs concurrently with Data Science Consulting.