Diploma in Data Analytics Co-op

You will have 600 hours of guided learning followed by 240 hours of practical experience in an established business.

The work placement will help you apply the theories you have learnt in real business situations. We will help you secure your work placement by sending you for interviews.

The modules are:

1. Data Design

This module is designed to provide you with the skills to enhance the quality and usefulness of data analytics by starting with the intended outcomes.

The module will enable you to consider the information an organization wants to gain from data analytics. This will give you the skills to select the most appropriate data collection method, design deployment approaches, implement data collection techniques and revise instruments and systems to be developed.

This module incorporates automated data collection as well as traditional methods to enable the development of methodologically-sound approaches. Throughout the module you will be given access to the Amazon Web Services (AWS) virtual environment where you will be able to complete additional assignments and earn micro-credentials.

2. Data Handling and Decision Making

This module is designed to introduce the theoretical concepts and practical applications of data auditing, handling and decision-making. It will equip you with the skills needed to identify what the findings from data analysis mean and how they can be applied.

You will learn approaches that can be used to audit existing data within an organization to identify gaps, analyze data and generate recommendations from your findings.

As part of your studies you, will be able to learn how to utilize R as it is rapidly becoming the leading language in data science and statistics. Today, R is the preferred programming language for data science professionals in every industry and field.

3. Working with Data using SAS and SQL

This module gives you an opportunity to gain practical experience in handling data using analysis software. The module will also cover theoretical concepts from data design and handling while teaching techniques to work with Structured Query Language (SQL) and SAS.

The module is designed in two parts; part one focuses on learning fundamentals of SQL) with multiple exercises, which is essential for working with databases. You’ll get hands-on experience accessing and manipulating data in order to gain useful insights. In part two, you will learn how to use SAS software for data handling and analysis.

4. Data Visualization and Interpretation

This module is partnered with the data handling and decision-making module, and enables you to develop your skills in data presentation to facilitate the understanding of findings, so you can make informed decisions. Data visualizations are a powerful method of making data accessible and understandable to non-specialists.

Through this module you will learn the appropriate use of graphs and charts as well as the use of specialist data visualization packages and tools such as Tableau, Qlik Sense and D3 to visualize data and impact the decision-making process.

5. Work Placement

At the conclusion of the program, you are required to complete 240 hours of work placement in a suitable business environment. Appropriate business sectors for placement include marketing, retail, finance and accounting, not-for-profit, customer care and administration.

Activities performed will vary depending on the work placement site, however, key responsibilities include being supervised by a placement host at all times, observing all workplace and school safety and security procedures, dressing appropriately, interacting with other staff respectfully, courteously and enthusiastically, learning about the work environment and participating in the daily routine as required.

• Have an Ontario Secondary School Diploma or equivalent or be at least 18 years of age and pass the Wonderlic test.

For non-native English speakers:

• Successful completion of TSoM EAP Level 4 or
• Have the required IELTS 5.5 score or equivalent or
• Pass the TSoM English Assessment (Written onsite or online with exam proctor)

Computer Use Expectation

In order to successfully progress through studies at Toronto School of Management (TSoM), it is required that you have access to a personal computer or laptop, with minimum configurations:

• CPU: 64-bit x86 Intel or AMD Processor from 2011 or later with full virtualization support with minimum 2GHz or faster core speed. Intel i5 or higher with 4 cores or more is recommended.  Ensure that it fully supports VMware and VirtualBox.
• RAM: 6GB or more is recommended.
• Storage: Minimum 256GB HDD/SSD or higher
• USB3 support
• Wireless Adapter with N or AC standard

Additionally, TSoM offers access to computer labs on campus, but availability cannot be guaranteed and some program software may not be available on all open access computers.

TSoM offers students discounted pricing on laptops and desktop computers through our partner CDW Canada should you be interested in purchasing one upon your arrival to Canada please reach out to Josh Antinolfi at josh.antinolfi@TorontoSoM.ca

The assessment of each module consists of:

• Individual assignments or class tests 40%
• Essay 25%
• Report 35%

• Individual assignments or class tests 40%
• Essay 25%
• Report 35%

• Individual assignments or class tests 20%
• Mid-term examination 30%
• Final examination 50%

• Individual assignments or class tests 40%
• Presentation 35%
• Final assignment 25%

• Co-op evaluation/report Pass/Fail

 

Letter Grade	Percentage	Grade Point
A+	90 - 100	4.3
A	85 - 89	4.0
A-	80 - 84	3.7
B+	77 - 79	3.3
B	73 - 76	3.0
B-	70 - 72	2.7
C+	67 - 69	2.3
C	63 - 66	2.0
C-	60 - 62	1.7
D	50 - 59	1.0
F	0 - 49	0.0

Upon completion of this program, you will be awarded a Diploma in Data Analytics Co-op. If you do not pass, you can re-enroll in the course and re-take at the next available sitting.

Graduates of this diploma can consider career opportunities under NOC 2172 which includes positions such as:

• Data analyst
• Database analyst
• Data mining analyst
• Data warehouse analyst