This course is an introductory course to distributed database systems and modern database systems, emphasizing NoSQL datastores. The first aim of this course is to introduce the basic principles of distributed database systems (DDS), different methods of designing DDB, and database integration. The second aim of this course is to teach the students the basics of NoSQL data stores and their philosophy and the differences between the four main types, which are key-value, column, document, and graph datastores. An example of some of these types will be introduced.
Social media analytics is the art and science of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insight- ful decision making. It is a science, as it involves systematically identifying, extracting, and analyzing social media data using sophisticated tools and techniques. It is also an art, interpret- ing and aligning the insights gained with business goals and objectives. In order to get value from analytics, one should master both its art and science. The course offers concepts, tools, tutorials, and case studies that data scientists need to extract and analyze the eight layers of so- cial media data, including text, actions, networks, apps, hyperlinks, multimedia, search engines, and location layers.
Most of data in the word is text, processing it automatically is challenging. In this course, we will examine state-of-the-art methods to solve natural language processing problems (NLP) using existing tools. We will cover pre-processing phases such as tokenization, vectorization, stemming, etc. Will discuss various applications such as clustering, prediction, and sentiments analysis, and current trends in this field.
This course will introduce the concept of data management in an organization through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.
The course mainly provides an introduction to the fundamental methods of machine learning. In this course the theoretical foundations as well as essential algorithms for supervised and
unsupervised learning will be discussed. The topics included in this course are linear regression, naïve Bayes, support vector machines, K-means, PCA, Assoc rule, etc. Some real time scenarios will be discussed to visualize the application of Machine learning techniques. Students will also learn how to implement these concepts in Python.