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DSAA

Data Science and Analytics

DSAA 5002
Data Mining and Knowledge Discovery in Data Science
[3-0-0:3]
Background
Knowledge of databases and statistics
Description
With more and more data available, data mining and knowledge discovery has become a major field of research and applications in data science. Aimed at extracting useful and interesting knowledge from large data repositories such as databases, scientific data, social media and the Web, data mining and knowledge discovery integrates techniques from the fields of database, statistics and AI.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Extract, clean and represent data.
- 2.
  Explain clustering of data.
- 3.
  Conduct classification of data.
- 4.
  Apply frequent pattern discovery.
- 5.
  Describe outlier detection.
DSAA 5009
Deep Learning in Data Science
[3-0-0:3]
Description
In this course, theories, models, algorithms of deep learning and their application to data science will be introduced. The basics of machine learning will be reviewed at first, then some classical deep learning models will be discussed, including AlexNet, LeNet, CNN, RNN, LSTM, and Bert. In addition, some advanced deep learning techniques will also be studied, such as reinforcement learning, transfer learning and graph neural networks. Finally, end-to-end solutions to apply these techniques in data science applications will be discussed, including data preparation, data enhancement, data sampling and optimizing training and inference processes.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify basic knowledge of machine learning and deep learning.
- 2.
  Apply deep learning modes.
- 3.
  Generate data for deep learning tasks.
- 4.
  Design optimized training and inference process.
DSAA 5012
Advanced Database Management for Data Science
[3-0-0:3]
Description
In this course, the concepts and implementation schemes in advanced database management systems for data science applications will be introduced, such as disk and memory management, advanced access methods, implementation of relational operators, query processing and optimization, transactions and concurrency control. It also introduces emerging database related techniques for data science.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify basic knowledge of data model and relational algebra.
- 2.
  Identify data storage and indexing structures.
- 3.
  Implement Query processing techniques.
- 4.
  Carry out Physical database design and advanced transaction management.
DSAA 5013
Advanced Machine Learning
[3-0-0:3]
Description
In this course, advanced algorithms for data science will be introduced. It covers most of the classical advanced topics in algorithm design, as well as some recent algorithmic developments, in particular algorithms for data science and analytics.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify basic knowledge of algorithm and its complexity analysis.
- 2.
  Apply advanced algorithms.
- 3.
  Carry out optimization by randomized and sampling algorithms.
- 4.
  Apply parallel and distributed algorithms.
DSAA 5015
Parallel Programming for Data Science and Analytics
[3-0-0:3]
Description
Introduction to parallel computer architectures; principles of parallel algorithm design; shared-memory programming models; message passing programming models used for cluster computing; data-parallel programming models for GPUs; case studies of parallel algorithms, systems, and applications; hands-on experience with writing parallel programs for data science and analytics.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Categorize parallel computer architectures and programming models.
- 2.
  Select suitable parallel programming models for data science and analytics tasks on specific hardware.
- 3.
  Design and implement parallel programs for specific data science and analytic tasks.
- 4.
  Evaluate and analyze parallel program performance for data science and analytic tasks.
DSAA 5020
Foundation of Data Science and Analytics
[3-0-0:3]
Description
This course will introduce fundamentals techniques for data science and analytics. Specifically, it will teach students how to clean the data, how to integrate data and how to store the data. On top of these, it will also teach students knowledge to conduct data analysis, such as Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction. In addition, it will also cover advanced data analytics topics including data governance, data explanation, data privacy and data fairness.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Clean, integrate and mine data.
- 2.
  Manage the data and process data.
- 3.
  Acquire the knowledge of data fairness, data privacy and data governance.
DSAA 5021
Data Science Computing
[3-0-0:3]
Description
This course will teach students data science computing techniques. Topics cover: (1) Basic concepts of Data Science Computing and Cloud; (2) MapReduce - the de facto datacenter-scale programming abstraction - and its open source implementation of Hadoop; and (3) Apache Spark - a new generation parallel processing framework - and its infrastructure, programming model, cluster deployment, tuning and debugging, as well as a number of specialized data processing systems built on top of Spark.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Build a Hadoop Ecosystems.
- 2.
  Manage the data over Spark systems.
- 3.
  Apply the knowledge of Spark and Hadoop Ecosystems for maintaining and monitoring Big Data Computing Systems.
DSAA 5022
Data Analysis and Privacy Protection in Blockchain
[3-0-0:3]
Description
This course introduces basic concepts and technologies of blockchain, such as the hash function and digital signature, as well as data analysis and privacy protection over blockchain applications. The students will learn the consensus protocols and algorithms, the incentives and politics of the block chain community, the mechanics of Bitcoin and Bitcoin mining, data analysis techniques over blockchain and user/transaction privacy protection.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Build a blockchain system.
- 2.
  Manage the data over blockchain.
- 3.
  Acquire the knowledge of blockchain and data analytics techniques.
- 4.
  Develop privacy protection techniques over blockchain.
DSAA 5024
Data Exploration and Visualization
[3-0-0:3]
Background
Basic knowledge of computer programming
Description
This course covers essential techniques for data exploration and visualization. Students will learn the iterative process of data preprocessing techniques for getting data into a usable format, exploratory data analysis (EDA) techniques for formulating suitable hypotheses and validating them, and specific techniques for domain-related data exploration and visualization such as high-dimensional, hierarchical, and geospatial data. The course uses programing languages such as python and tools like Tableau.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Understand basic concepts of exploratory data analysis and its relation to descriptive and inferential statistics, data mining, and data visualization
- 2.
  Describe the iterative process of data wrangling, analysis, and visualization
- 3.
  Understand principles for effective visualization design and select suitable visualization techniques
- 4.
  Implement visualizations for diverse types of datasets using python and Tableau
- 5.
  Demonstrate how data exploration and visualization can be applied to real-world problems
DSAA 5027
Spatio-Temporal Data Analysis
[3-0-0:3]
Description
In this course, we will introduce spatial and multimedia database management concepts, theories and technologies, from data representation, indexing, fundamental operations to advanced query processing. Challenges and solution for high dimensional data will also be introduced.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Master basic knowledge of Relational Data Indexing and Query Processing.
- 2.
  Master knowledge of Spatial Databases.
- 3.
  Apply Spatial Data Organisation and Indexing; Spatial Query Processing.
- 4.
  Managing Spatiotemporal Data, Multimedia Databases and High Dimensional Data.
DSAA 6000
Special Topics
[3-0-0:3]
Description
The special topics course is designed for faculty to offer a course about popular research topics. The research topics in data science and analytics change and evolve very fast. The special topics will help students to know the research trend in this area and master the state-of-the-art solutions. Students will not only learn from the lectures, but also investigate the techniques by reading papers, giving presentations and working on projects.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Gain the knowledge of current research trend in Data Science and Analytics area.
- 2.
  Master the state-of–the-art techniques of the chosen topic.
- 3.
  Apply the learned techniques to the real problem on the chosen topic.
DSAA 6018
Independent Study
[1-3 credit(s)]
Description
In this course, an independent research project will be carried out under the supervision of a faculty member.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify knowledge related to the proposed topic.
DSAA 6101
Data Science and Analytics Program Seminar I
[0 credit]
Description
In this course, students are required to attend at least 6 seminars offered by the program. The program will offer at least 10 seminars related to the state of the art research on data science and analytics in each term. These seminars will help students to broaden the horizons of their knowledge on data science and analytics. Graded P or F.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify knowledge of state-of-the-art research topics and techniques on data science and analytics.
DSAA 6102
Data Science and Analytics Program Seminar II
[1-0-0:1]
Description
In this course, students are required to attend at least 6 seminars offered by the program. The program will offer at least 10 seminars related to the state-of-the-art research on data science and analytics in each term. These seminars will help students to broaden the horizons of their knowledge on data science and analytics. Graded P or F.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify knowledge of state-of-the-art research topics and techniques on data science and analytics.
DSAA 6990
MPhil Thesis Research
Description
Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Design, develop and conduct cross-disciplinary research in Data Science and Analytics.
- 2.
  Communicate research findings effectively in written and oral presentations.
DSAA 7990
Doctoral Thesis Research
Description
Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Design, develop and conduct cross-disciplinary research in Data Science and Analytics.
- 2.
  Communicate research findings effectively in written and oral presentations.

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