East Carolina University
Department of Computer Science

CSCI 4180
Big Data Analytics
Standard Syllabus

Catalog entry

P: CSCI 3700. Hands-on introduction to very big data and the practical issues surrounding how the data is stored, processed, analyzed, and visualized. Work with cloud-based high performance computing systems, large data collections, and high velocity data streams.

Course summary

Introduces students to technologies, algorithms, and architecture for analyzing very large data sets.

Course topics

• Introduction to the terminology of data analytics
• Technologies used for Handling Big Data
• Natural Language Processing
• Distributed file systems and map-reduce as a tool for creating parallel algorithms that succeed on very large amounts of data.
• Similarity search, including the key techniques of minhashing and localitysensitive hashing.
• Stream data analytics and algorithms for dealing with data in real time.
• The technology of search engines, including Google’s PageRank, link-spam detection, and the hubs-and-authorities approach.
• Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements.
• Algorithms for clustering very large, high-dimensional datasets.
• Two key problems for Web applications: managing advertising and recommendation systems.
• Machine-learning algorithms that can be applied to very large data, such as perceptrons, support-vector machines, and gradient descent.

Student learning outcomes

• Explain the fundamentals of Big Data standards for creating parallel algorithms
• To demonstrate effective use of Map Reduce and No SQL system
• Explain the fundamental techniques and tools used to design and analyze large volumes of data.
• Implement the most important and popular algorithms used in data mining and machine leaning.
• Acquire deep understanding of what the algorithms are supposed to do
• Demonstrate the effectiveness of predictive data analytics project lifecycle
• Demonstrate effective use of machine learning-based predictive tools used in Big Data.

Textbook

Grading

The course is graded using a combination of a group project (20%), Homeworks (10%), weekly quizzes (10%), a midterm exam (30%), and a final exam (30%). Letter grades are as follows: 94 or higher is an A; 90 or higher is an A-; 87 or higher is a B+; 83 or higher is a B; 80 or higher is a B-; 77 or higher is a C+; 73 or higher is a C; 70 or higher is a C-; 67 or higher is a D+; 63 or higher is a D; 60 or higher is a D-; and lower than 60 is an F.

Grade meanings