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Arizona State University

Big Data

Arizona State University via Coursera MasterTrack

Overview

With zettabytes of data being collected annually, governments, companies, and people have more access to data than ever before. With so much data, it can be hard to know where to start looking for important insights or trends to drive business decisions.

Data mining techniques provide the first level of abstraction to raw data by extracting patterns, making big data analytics tools increasingly critical for providing meaningful information to inform better business decisions, and applying statistical learning theory to find a predictive function based on data.

You’ll learn to apply mathematical theory and decision making techniques that are vital to big data analysis, classification, clustering, and association rule mining through real-world projects designed by faculty from Arizona State University.

By committing to online study for 6-9 months, you can earn the Big Data MasterTrack Certificate that will be a pathway to the online Master of Computer Science degree at Arizona State University.

Syllabus

Course 1: Data Processing at Scale
- This course delves into new frameworks for processing and generating large-scale datasets with parallel and distributed algorithms, covering the design, deployment and use of state-of-the-art data processing systems, which provide scalable access to data.

Course 2: Data Mining
- This course will introduce you to the fundamentals of data mining and pattern recognition. You will gain a deeper understanding of data through hands-on experience in the topic areas of big data analysis, classification, clustering, and association rule mining. Advanced topics such as reinforcement learning, deep learning, transfer learning and Deep Mind for Google will also be covered. By the end of the course, you will be able to apply state of the art data mining technology to real world applications, analyze and compare competing techniques, and design optimal solutions for a given set of application driven constraints.

Course 3: Statistical Machine Learning
- This course investigates the data mining and statistical pattern recognition that support artificial intelligence. Main topics covered include supervised learning; unsupervised learning; and deep learning, including major components of machine learning and the data analytics that enable it.

Course 4: Data Visualization
- This course covers techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science to enhance the understanding of complex data.

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