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Online Data Analytics Courses

Curriculum Details

180 total credits required

The online Bachelor’s in Data Analytics program prepares students with the mathematical and computer programming skills necessary to collect, analyze and present data to inform decision-making efforts. Our online data analytics courses are grounded in mathematics, computer science and information management.

As a student, you will complete a capstone course with one of our expert instructors and a regional partner, gaining experiential learning in the field and developing solutions to real-world problems. You will also choose from a wide range of electives to round out your skill set. While the program is designed to be completed in four years, students who have previously earned an associate degree can finish in just two years.

Core Mathematics Courses

Differential calculus including functions, limits, continuity, differentiation formulas, implicit differentiation, higher order derivatives, related rates, differentials, optimization problems, how the derivative affects the shape of a graph and an introduction to antiderivatives.
This course is the second part of a two-course sequence. The two-course sequence will be equivalent in content and credit to MATH 251 (Calculus I). Topics include functions, limits, continuity, differentiation formulas, implicit differentiation, higher order derivatives, related rates, differentials, optimization problems, how the derivative affects the shape of a graph, and an introduction to antiderivatives. Any requirement satisfied by MATH 251 will also be satisfied by the pair of courses.
Integral calculus including the definite integral, the fundamental theorem of calculus, area between curves, volumes by slicing, L’Hospital’s Rule, the calculus of the exponential and logarithmic functions, techniques of integration, improper integrals and arc length.
This course provides an introduction to several topics from discrete mathematics, including mathematical induction, Boolean logic and set operations, counting theory (combinatorics), and graph theory.
An introduction to linear algebra including systems of linear equations, vector and matrix algebra, determinants, linear transformations, eigenvalues and eigenvectors, and the concepts of basis and dimension.
This course is the first of a two-course sequence. The two-course sequence will be equivalent in credit and content to STAT 243 “Elementary Statistics”. Topics include experimental design, introduction to histograms, the normal.
This course is the second of a two-course sequence. The two-course sequence will be equivalent in credit and content to STAT 243 “Elementary Statistics”. Topics include sampling error, confidence intervals, and hypothesis testing including z-tests and chi-square tests.
A second term of statistics covering correlation, simple and multiple linear regression, and one and two sample hypothesis testing including t-tests, chi-square tests, analysis of variance, tests related to regression, and non-parametric statistics. Applications utilizing statistical software are used
Principles of experimental design and associated data analysis techniques such as regression, hypothesis testing, analysis of variance, and non-parametric statistics; experience with statistical packages for computers; introduction to exploratory data analysis.  Prerequisite: Student has met math requirement for graduation. This course requires students to apply basic principles of mathematics including algebra.

Core Computer Science Courses

Introduces basic data representation, branching and iteration, memory management, computer architecture, and the analysis and design of problem solutions.
Introduces basic data representation, branching and iteration, memory management, computer architecture, and the analysis and design of problem solutions.
Introduces some common algorithms for searching and sorting, the analysis of algorithm complexity, exception handling, and file output.
An introduction to the basics of programming as used in C and C++, including selection statements, loops, arrays, string handling, pointers, registers and functions. Practical exercises will require the construction, compilation, debugging, and execution of complete programs that implement given algorithms to solve simple problems. The emphasis in this course will be on the common features of C and C++; however, memory allocation and the use of pointers will be discussed.
An introduction to various implementations of commonly used data structures and their applications. Topics include lists, stacks, queues, trees and heaps. Prerequisite:
The analysis of a variety of algorithms that arise frequently in computer applications. Basic principles and techniques for analyzing and improving algorithms in areas such as list searches, sorting, pattern recognition, polynomial and matrix computations.
Analysis, design, and implementation of data systems in relation to information transfer.  

Core Data Analytics Courses

An introduction to the use of Geographic Information Systems (GIS) software with a focus on data acquisition, representation and management
This course introduces methods for acquiring, filtering, and analyzing data sets using statistical software (such as R).  Topics include implementing algorithms and data structures using statistical software with a focus on problem solving.
An applications-based course introducing issues involved in storing and analyzing large data sets.  Topics include database algorithms and programming in non-relational database systems (noSQL models).  Students will apply their knowledge of database programming to pose and answer questions about large, real-world data sets.
This course takes a programming and simulation approach to the review of probability theory including random variables, distributions, and sampling.  Stochastic models such as Poisson processes and Markov processes are also introduced.
This course introduces applications of multivariate statistical techniques including multiple linear regression and the general linear model, model construction and variable selection, logistic regression, and principal components analysis.  The use of statistical software (such as R) to perform appropriate analysis will be emphasized.  This is a writing intensive class.  The emphasis will be on analysis of real-world data sets and accurate descriptions of appropriate analysis. [Prereqs: DAT 315, STAT 352 (or STAT 327), and MATH 341].
This course teaches applications of machine learning techniques.  Topics include regression models, decision trees, neural networks, naive Bayes, nearest neighbor algorithms, and other clustering and probabilistic classification techniques. 
This is a Capstone course for the data analytics major.  Students will work on a data analysis project in coordination with the instructor and a regional partner, culminating in a written report and public presentation of results.

Transfer Information

EOU offers a generous transfer policy for course credits to make it even more affordable and accessible to learn. The online Bachelor’s in Data Analytics program accepts up to 135 credits for transfer to EOU. All EOU majors require a minimum of 20 EOU credits.

Get in Touch

We are here to answer any questions you may have. Contact an enrollment counselor at 855-805-5399 or complete the request for information form and we will be in touch.