Big Data Solution Architecture (Optional Co-op)

Description: This self-directed course focuses on introducing new students to the supports, services, and opportunities available at Conestoga College. By the end of this course, students will understand the academic expectations of the Conestoga learning environment, as well as the supports available to ensure their academic success. Students will also be able to identify on-campus services that support their health and wellness, and explore ways to get actively involved in the Conestoga community through co-curricular learning opportunities.

• Hours: 1
• Credits: 0
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs; ​No cost.

Description: Database design must accurately reflect business requirements in order to enable effective data analysis. In this course, students will learn how to describe business rules in Entity-Relationship and Process Diagrams and subsequently develop relations to normalize the data. Students will also focus on the implementation of the database design by using Structured Query Language (SQL) to build, query and maintain large data sets.

• Hours: 84
• Credits: 6
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description: “Not only SQL” databases are heavily used in Big Data applications - particularly those that are web-related. The benefits in scalability and performance make a NoSQL database a compelling choice. In this course, students will design and implement NoSQL databases using systems like CouchDB, MongoDB, Cassandra or Hive. Students will also use the Hadoop framework to demonstrate the implementation of a NoSQL database in a large-scale storage and data processing model.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description: Custom applications are often required to collect, transform and present data. In this course, students will review fundamental concepts like language syntax, data types, flow control and modular programming. Students will also use programming languages (like Python, Java and C#), program libraries and visual components to analyze and present information derived from the connected data sources.

• Hours: 84
• Credits: 6
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description: Reliable, accurate and maintainable software is critical to the success of a company. In this course, the student learns the fundamentals of project management in both traditional and agile approaches. In addition, the student learns the importance of creating good software by applying good design principles and implementing solid testing methodologies.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description: Companies with a Web presence are very interested in analyzing their site usage. Students in this course will examine data mining through social media tracking. At first, students will demonstrate how to gather and analyze the data that most web sites track automatically. Later, students will study Google Analytics, including setting it up and understanding the data that is available. Finally, students will install and configure a commercially available tool to visualize and explore large data sets.

• Hours: 56
• Credits: 4
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description: Data Mining in Business Intelligence and Big Data often involves the analysis of demographic and psychographic data. In this course, students will examine the ethics of using this type of data, as well as topics in applying appropriate security measures in data collection and visualization.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description: In a high-tech oriented field like Big Data analysis, there are constantly new topics to explore. The course is driven by student presentations in areas like computing platforms, data storage, environmentally sustainable practices, analysis tools, programming languages and Open Data.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description: The fundamentals of Big Data Analysis are rooted in traditional statistics and advanced statistical models. Students will begin this course by reviewing ways in which a common tool (like a spreadsheet) provides powerful statistical analysis tools. Then, students will implement some formulae and algorithms used for Big Data analysis in custom computer programs (using, for example, R) and specialized software packages like SPSS, MATLAB and Statistics. Finally, students will examine topics in predictive and prescriptive modeling.

• Hours: 84
• Credits: 6
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description: Many Big Data applications require the acquisition and integration of data from multiple data sources. Often, these data sources are from different databases or data stores. In this course, students will demonstrate approaches to data collection and integration with a common data store. They will also explore, demonstrate, and compare the two general approaches of using a Data Warehouse versus a NoSQL implementation (like Hadoop).

• Hours: 84
• Credits: 6
• Pre-Requisites: PROG8410 OR PROG8411
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.

Description:

Students will focus on group activity and discussion to review case studies in Big Data. Students will examine these case studies from the point of view of architecture, implementation, and maintenance of the solutions. In addition, the students will explore how the entrepreneurial spirit helped drive innovation in this field.

• Hours: 70
• Credits: 5
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs: Not available at this time.