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DA 512 Big Data Processing using Hadoop (Elective)DA 512 Syllabus This course will provide the essential background to start to develop programs that will run on Hadoop Distributed File System (HDFS). The course will also show the students the limitations of traditional programming techniques and how Hadoop addresses these problems. After learning the basics of a Hadoop Cluster and Hadoop Ecosystem, students will learn to write programs using Apache Spark framework and run these programs on a Hadoop Cluster. |
DA 513 Time Series Analysis and ForecastingDA 513 Syllabus Time Series Forecasting is one of the most applied data science techniques across many disciplines, most notably in finance, in supply-chain management, and in production planning. This course is intended to provide a comprehensive introduction to forecasting methods used in the analysis and forecasting of Time Series. At the end of the course, students will be able to analyze and explore time series data, build and apply various Time Series models suitable for a wide range of business problems. PYthon programming language and libraries will be used in the analysis and forecasting of Time Series problems throughout the course. |
DA 514 Machine Learning I (Elective) DA 514 Syllabus In this course, we will cover fundamental aspects of Machine Learning. We will start with fundamentals of machine learning, including different learning paradigms, regression and classification problems, evaluation methods, generalization and overfitting. We will then cover some of the fundamental machine learning techniques such as decision trees, Bayesian approaches, Naive Bayes classifier, and logistic regression, k-Nearest neighbor, and online learning algorithms. Besides understanding the basic theory behind the techniques, students are expected to apply them on different platforms like Weka or Matlab. |
DA 518 Exploratory Data Analysis and Visualization (Elective)DA 518 Syllabus Exploratory Data Analysis (EDA) is an approach to data analysis for summarizing and visualizing the important characteristics of a data set. EDA focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, and decide how it can be investigated with more formal statistical methods. EDA is distinct from Data Visualization in that EDA is done towards the beginning of analysis and data visualization is done towards the end to communicate one’s finding. This course particularly pays attention to the applied techniques to data visualization narratives. We will draw on case studies from the business world, industry to news media. |
DA 517 Machine Learning II (Elective) DA 517 Syllabus In the scope of this course we will begin with data extraction, cleaning, and normalization to prepare the data for Data Mining Algorithms. We will then cover Data Mining techniques including association rule mining, sequential patterns, clustering, text mining. Students are expected to understand the fundamental theory behind each technique, as well as implementing them using an environment such as RapidMiner or Weka. |
DA 516 Social Network Analysis (Elective)DA 516 Syllabus Different types of social networks and connectivity are a crucial part of the underlying models of the new generation of applications we use. These connections include people, places, activities, businesses, products, social and integrated business processes happening in personal and business networks or communities. In this course we will study different applications such as Facebook, Twitter, Linkedin and Foursquare, and discover different networks formed by the connectivity. We will introduce tools that will give us insight into how these networks function: We will introduce fundamentals of graph theory and discover how these graphs can be modeled and analyzed (Social Network Analysis). We will also study the interaction dynamics using game theory. Learning objectives are: - Study different social applications and how they can be modeled.
- Understand the basics of graph theory.
- Understand and perform basic social network analysis
- Understand the basics of game theory
- Apply these concepts to model the Web and new social applications.
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DA 515 Practical Case Studies in Data Analytics (Elective)DA 515 Syllabus This course aims at discussing the key principles of knowledge discovery process through various case studies arising from different application areas. The students are expected to learn the main steps to traverse when they face new data analytics problems. With each case study, the tools for cleaning, processing and altering the data shall be visited. A particular attention shall be given to data inspection, feature reduction and model selection. Each case study will be completed by a thorough discussion and interpretation of the results. |
DA 522 Information Law and Data Ethics (Elective)DA 522 Syllabus Given the widespread distribution of data in today’s buısiness world, the legal and ethical issues related to the use of data have been, and will be, of critical importance in establishing a corporate policy. Within the framework of these legal and ethical issues, students will gain an understanding of the following concepts: private, confidential, anonymous and open data; private versus public data; data ownership and proprietary rights; intellectual property; overview of existing legal framework; constraints, rules and legislative procedure in access and use of data. |
DA 525 Project Management and Business Communication (Elective)DA 525 Syllabus This course is intended to provide industry insight into the world of project management and business communication. Upon completion of this course, students are expected to have a clear understanding of the tasks and challenges that are fundamental to project management requirements. The course will also cover issues on team management and other aspects of project management on schedules, risks and resources for a successful project outcome. The second part of this course will concentrate on effective communication with team members, presentation techniques for a wide range of audiences and communicating results and recommendations to upper management and clients. |
DA 592 Term Project (Non-credit)All graduate students pursuing a non-thesis MSc. Program are required to complete a project. The project topic and contents are based on the interest and background of the student and are approved by the faculty member serving as the Project Supervisor. At the completion of the project, the student is required to submit a final report and present the project. The final report is to be approved by the Project Supervisor. |