CIND 719 - Big Data Analytics Tools
Course Description
This course is an introduction to learning big data tools such as Hadoop and advanced SQL techniques. Students will gain a clear understanding of Hadoop concepts and technologies landscape and market trends. They will construct SQL queries of moderate to high complexity to retrieve data from a relational database. Note: Tools taught Hive, Pig, Oozie, LAMBDA, Gigraph and GraphLab.What Will You Learn?
Develop a comprehensive understanding of big data and its industrial and sectoral applications.
Learn how to:
- Engage in big data and AI computing (cloud computing) and their industrial applications.
- Utilize Hadoop ecosystem for big data.
- Employ Linux file systems, bash commands, and regular expressions.
- Write complex queries on big data using Apache Hive to query data stored in various databases and file systems that integrate with Hadoop.
- Write scripts and analyze data using Apache Spark to efficiently execute streaming and machine learning on big data.
- Leverage network analyses and their use cases.
Notes
The deadline to enroll in CIND 719 for Fall term is September 12, 2022.
The deadline to enroll in CIND 719 for Winter term is January 16, 2023.
You must download the X2Go Client in order to access the software needed to complete the requirements for this course. Prior to your first class, you are strongly advised to test the computer you plan to use, as machines operated using a third-party administrator (such as laptops provided by a workplace) may not allow access to the required software/download(s).
International students should use their own virtual private network (VPN) software to connect to University resources.
Requisites
Department Consent Required. Please submit the Request Department Consent form, or contact Ceni Babaoglu, Assistant Program Director, Data Science at cenibabaoglu@ryerson.ca for more information.
Department consent may be provided if the student has specific professional experience.
Relevant Programs
- Practical Data Science and Machine Learning : Required