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Our Courses
Big Data Integration and Processing
At the end of the course, you will be able to: *Retrieve data from example database and big data management systems *Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications *Identify when a big data problem needs data integration *Execute simple big data integration and processing on Hadoop and Spark platforms This course is for those new to data science. Completion of Intro to Big Data is recommended.
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Course by
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Self Paced
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18 hours
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English
Data Visualization
The specialization "Data Visualization" is intended for post-graduate students seeking to develop advanced data visualization skills. Through three comprehensive courses, you will explore foundational and specialized visualization techniques, including data representation, design principles, network visualization, and volume rendering. As organizations increasingly rely on data for decision-making, the ability to effectively visualize and analyze complex datasets is more valuable than ever.
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Course by
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Self Paced
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16 hours
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English
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values.
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Course by
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Self Paced
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37 hours
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English
Measurement Systems Analysis
In this course, you will learn to analyze measurement systems for process stability and capability and why having a stable measurement process is imperative prior to performing any statistical analysis. You will analyze continuous measurement systems and statistically characterize both accuracy and precision using R software. You will perform measurement systems analysis for potential, short-term and long-term statistical control and capability.
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Course by
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17 hours
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English
The Structured Query Language (SQL)
In this course you will learn all about the Structured Query Language ("SQL".) We will review the origins of the language and its conceptual foundations. But primarily, we will focus on learning all the standard SQL commands, their syntax, and how to use these commands to conduct analysis of the data within a relational database.
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Course by
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Self Paced
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55 hours
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English
Algorithms for Searching, Sorting, and Indexing
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 Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others.
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Course by
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Self Paced
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35 hours
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English