Pre-Data Science Courses

Interested in pursuing a graduate or professional degree in one of these areas?

• Applied Statistics
• Data Science

This foundational coursework in core data science areas will provide students with the skills and knowledge necessary to excel in graduate level data-science coursework. Intended for students and working professionals who are looking to change disciplines, these courses are curated to fill in common knowledge gaps from three primary areas: Mathematics, Statistics, and Computer Science Fundamentals.

After successfully completing the courses of your choice, you will have a more competitive application to submit to graduate programs in Statistics, Data Science, Data Analytics, and Business Analytics. CSU boasts a strong history in Math, Statistics, Computer Science, and more recently Data Science, and is great place to start on your path to a new area of expertise. Recommended coursework specific to CSU’s MAS in Statistics or Data Science are provided below. Reach out to admissions at your institution of application to see what courses they recommend.

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Mathematics

• Calculus: Students may take courses in differential calculus, integral calculus, and multivariable calculus to understand mathematical concepts and techniques used in data analysis.
• Linear Algebra: Courses in linear algebra cover topics such as matrix operations, vector spaces, eigenvalues, and eigenvectors, which are essential for understanding machine learning algorithms and statistical methods.
• Probability: Students learn basic probability theory and probability distributions, the foundation of statistical inference.

Statistics

• Statistics: Students learn methods for statistical inference, hypothesis testing, and regression analysis, providing a solid foundation for data analysis and modeling.
• Exploratory Data Analysis (EDA): Students learn techniques for exploring and summarizing datasets, identifying patterns, outliers, and relationships among variables.
• Data Visualization: Courses cover principles of data visualization, visualization tools and libraries (e.g., matplotlib, seaborn, ggplot2), and best practices for creating effective visualizations to communicate insights from data.

Computer Science Fundamentals

• Programming: Introductory courses in programming languages such as Python, R, or Java introduce students to programming concepts, data structures, algorithms, and problem-solving techniques.
• Data Structures and Algorithms: Students learn about fundamental data structures (e.g., arrays, linked lists, stacks, queues) and algorithms (e.g., sorting, searching, graph algorithms) used in data manipulation and analysis.
• Database Systems: Courses cover database design, implementation, querying using SQL, and database management systems (e.g., relational databases, NoSQL databases), which are essential for working with structured data.

Study from wherever you are

Whether you are looking to start a career, change your career, or you need to refresh your understanding of a subject in preparation for further academic study, you will find applicable online pre-data science courses within these offerings. The classes you need depend on the school and program you intend to apply to. All classes are offered online, so you can study on your own schedule, and there is no need to relocate or leave your current employment.

Earn the same credits as students on campus

These courses are taught by the same CSU faculty and cover the same content as on-campus equivalents. You can be sure that the learning outcomes are the same to those completed by students on campus. CSU transcripts do not differentiate between online and on-campus courses, but students may wish to consult with a professional school admission counselor to confirm courses will be accepted.

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