4 Weeks

"Fandom, Social Media, and Authenticity in the Digital Age" focuses your attention on the interconnected dynamics of identity, representation, interpretation, and self-reflection. It asks the question: how, where, and with whom do we inhabit the worlds of fandom and popular culture in the digital age? We will explore [...]

Digital Marketing Institute is the global standard in Digital Marketing Certification. Our mission is to give our students the confidence and knowledge they need to advance in their careers through this specialization. Facebook is the oldest of the social networks, and the biggest, with approximately 2.7 billion monthly active [...]

This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for. Weekly topics include an overview of the [...]

Are you ready to dive into the world of data engineering? You’ll need a solid understanding of how data is stored, processed, and accessed. You’ll need to identify the different types of database that are appropriate for the kind of data you are working with and what processing the data requires.

History of Ancient Egypt. Before and after the Pharaohs: From the birth of the State (c. 3000 BCE) to the early Christian communities (4th century CE).

This course covers basics of algorithm design and analysis, as well as algorithms for sorting arrays, data structures such as priority queues, hash functions, and applications such as Bloom filters. Algorithms for Searching, Sorting, and Indexing can be taken for academic credit as part of CU Boulder’s Master of [...]

Basic algorithms on tree data structures, binary search trees, self-balancing trees, graph data structures and basic traversal algorithms on graphs. This course also covers advanced topics such as kd-trees for spatial data and algorithms for spatial data. Trees and Graphs: Basics can be taken for academic credit as part [...]

This course introduces the key steps involved in the data mining pipeline, including data understanding, data preprocessing, data warehousing, data modeling, interpretation and evaluation, and real-world applications. Data Mining Pipeline can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree [...]

This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis [...]

In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; [...]