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Our Courses
Data Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp. Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science.
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Course by
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Self Paced
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English
Data Mining and Knowledge Discovery
Learn how to discover knowledge in data via data mining.
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Course by
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Self Paced
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10
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English
Process Mining: Data science in Action
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.
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Course by
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Self Paced
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22 hours
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English
Business Intelligence Concepts, Tools, and Applications
This is the fourth course in the Data Warehouse for Business Intelligence specialization. Ideally, the courses should be taken in sequence. Effectively and efficiently mining data is the very center of any modern business’s competitive strategy, and a data warehouse is a core component of this data mining. The ability to quickly look back at early trends and have the accurate data – properly formatted – is essential to good decision making. By enabling this historical overview, a data warehouse allows decision makers to learn from past trends and challenges.
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Course by
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Self Paced
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22 hours
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English
Pattern Discovery in Data Mining
Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery.
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Course by
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Self Paced
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17 hours
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English
Advanced Trading Algorithms
This course will provide back test results for all the strategies in developed and emerging markets. The learner will also be taught scientific ways of back testing without succumbing to either look ahead (or) survival bias. You will learn various methods of building a robust back testing system for the strategies discussed in the previous course. You will be taught how to differentiate between mere data mining and results based on solid empirical or theoretical foundation.
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Course by
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Self Paced
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11 hours
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English
Cluster Analysis in Data Mining
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
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Course by
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17 hours
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English
Text Retrieval and Search Engines
Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.
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Course by
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31 hours
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English