Big Data Solution Architecture (Optional Co-op)

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Courses - January 2023

Level 1

Course Details

Conestoga 101
CON0101

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:

Relational Database Design
PROG8400

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:

NoSQL Database Implementation
PROG8410

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: 56
  • Credits: 4
  • Pre-Requisites:
  • CoRequisites:

Programming for Big Data
PROG8420

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:

Data Analysis Mathematics, Algorithms and Modeling
PROG8430

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: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Software Quality
PROG8440

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:

Level 2

Course Details

Co-op and Career Preparation
CEPR8200

Description:

This series of modules will prepare graduate certificate students for job searching for their co-op work terms with the guidance of a Co-op Advisor. Students will familiarize themselves with the co-operative education policies and procedures and will learn the expectations, rules, and regulations that apply in the workplace regarding social, organizational, ethical, and safety issues while deepening their awareness of self- reflective practices. Students will critically reflect on their skills, attitudes, and expectations and evaluate available opportunities in the workplace. Successful completion of these modules is a requirement for co-op eligibility.

  • Hours: 14
  • Credits: 1
  • Pre-Requisites:
  • CoRequisites:

Ethics and Security
ETHS8010

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:

Emerging Trends in Big Data
INFO8120

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:

Big Data Integration and Storage
PROG8450

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
  • CoRequisites:

Web Analytics and Business Intelligence Tools
PROG8460

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: 84
  • Credits: 6
  • Pre-Requisites: PROG8420 AND PROG8430
  • CoRequisites:

Case Studies
SENG8080

Description:

The popularity of the Internet has provided numerous examples of best (and worst) practices in the field of Big Data analysis. In this course, students will focus on group activity and discussion to review some of the more compelling 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 discuss how the entrepreneurial spirit helped drive innovation in this field.

  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Level 3

Course Details

Co-op Work Term (Big Data Analytics - Solutions Development)
COOP8150

Description:

This co-op work term will provide students with college-approved work experience in an authentic, professionally relevant work environment. Students will be provided the opportunity to connect theory and practice by leveraging their academic knowledge to develop specialized vocational skills. The practical applications of this work term will promote students’ awareness of key concepts and terminology in their field, improve their competencies in problem-solving and decision-making, further their application of professional judgement, hone their leadership skills (independently or as part of team), and enhance their capacity to critically analyze and reflect on their demonstrated abilities in the workplace.

  • Hours: 420
  • Credits: 14
  • Pre-Requisites: CEPR8200
  • CoRequisites:

Program outcomes

  1. Develop high quality software solutions to collect, manipulate and mine data sets to satisfy the business requirements of organizations
  2. Analyze different system architectures and data storage technologies in order to select appropriate solution to support data analytics requirements
  3. Design data models that satisfy the requirements of a particular business process
  4. Design and present data visualizations to communicate information to business stakeholders
  5. Apply business analytics and business intelligence tools to support evidence-based decision making
  6. Apply appropriate privacy and security measures in collecting, processing, and visualizing data
  7. Employ environmentally sustainable practices within the field of data analytics.
  8. Apply basic entrepreneurial strategies to identify and respond to new opportunities