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Part Time Data Analyst Jobs in Ontario (NOW HIRING)

PRINCIPLES OF DATA ANALYTICS (On line) Course Code: MEM5300 Section: A Course Description ... Professeur a temps-partiel regulier / Regular Part-Time Professor Date Posted (YYYY/MM/DD): 2026/07 ...

Part-time The Data Collection Associate is responsible for collecting and transmitting data ... These tools analyze information you provide (for example, your resume or answers to application ...

Part-time The Data Collection Associate is responsible for collecting and transmitting data ... These tools analyze information you provide (for example, your resume or answers to application ...

May compile data, statistics and other information to support research activities * May organize ... applications, analyzing resumes, or assessing candidate responses. These tools assist our ...

If you would like more information about how your data is processed, please contact us. Time Type ... applications, analyzing resumes, or assessing candidate responses. These tools assist our ...

If you would like more information about how your data is processed, please contact us. Time Type ... applications, analyzing resumes, or assessing candidate responses. These tools assist our ...

If you would like more information about how your data is processed, please contact us. Time Type ... applications, analyzing resumes, or assessing candidate responses. These tools assist our ...

If you would like more information about how your data is processed, please contact us. Time Type ... applications, analyzing resumes, or assessing candidate responses. These tools assist our ...

Showing results 41-60

Part Time Data Analyst information

What is a part time data analyst?

Part time data analysts are professionals who work fewer hours than a standard full-time schedule, typically analyzing and interpreting data to help organizations make informed decisions. They use statistical tools and software to process data, identify trends, and generate reports. Part time positions are ideal for those seeking flexible work arrangements, such as students, parents, or individuals with other commitments. Despite working fewer hours, part time data analysts are expected to have strong analytical skills and proficiency in data management tools. Their contributions are valuable to businesses seeking data-driven insights without the need for a full-time role.

What does a part time data analyst do?

A part-time data analyst collects, organizes, assesses, and reviews information. In this career, you review data to ensure that it is accurate and to identify trends, provide analysis of a market or a company’s operations and processes, or meet other needs of a company or client. Data analysts typically create reports that explain their analysis. Your responsibilities also include helping to develop and deploy systems and tools to collect, extract, and categorize data so that you can analyze it more efficiently. As a part-time employee, you perform your duties for less than 40 hours per week.

What are the key skills and qualifications needed to thrive as a part time data analyst, and why are they important?

To excel as a Part Time Data Analyst, you need proficiency in data analysis, statistical methods, and a relevant degree such as mathematics, statistics, or computer science. Familiarity with technical tools like Microsoft Excel, SQL, and data visualization platforms such as Tableau or Power BI is typically required. Attention to detail, problem-solving ability, and effective communication are crucial soft skills for interpreting and presenting data insights. These competencies enable accurate, actionable analyses that support decision-making, even within limited working hours.

How does a part time data analyst typically collaborate with full time team members to ensure project continuity?

As a part-time data analyst, you’ll often work closely with full-time analysts, project managers, and business stakeholders to maintain seamless project progress. Regular check-ins, clear documentation of your analyses, and using collaborative tools like shared dashboards or project management software are key practices. Flexibility and strong communication skills are essential, as you may need to align your schedule with team meetings or coordinate handoffs to ensure your work integrates smoothly with ongoing projects.

What is the difference between Part Time Data Analyst vs Data Scientist?

AspectPart Time Data AnalystData Scientist
Required CredentialsBachelor's degree in data-related field; some roles may require certifications like Microsoft Excel or SQLBachelor's or master's degree in data science, statistics, or related fields; often requires programming skills and certifications
Work EnvironmentTypically in office settings, supporting specific projects or departments, with flexible or part-time hoursUsually in tech or research environments, working on complex models and large datasets, often full-time
Employer & Industry UsageUsed across industries like finance, marketing, healthcare for data reporting and analysisCommon in tech, finance, and research sectors for developing predictive models and advanced analytics

While both roles involve data analysis, Part Time Data Analysts focus on supporting business decisions with basic data tasks, often on a flexible schedule. Data Scientists handle complex modeling and predictive analytics, typically in full-time roles. The choice depends on your skills, experience, and career goals.

Are part time data analyst jobs still in demand?

Part-time data analyst jobs remain in demand as organizations seek flexible ways to analyze data for decision-making. Skills in Excel, SQL, and data visualization tools like Tableau are valuable, and remote or flexible schedules are increasingly available in this field.

What are the most commonly searched types of Data Analyst jobs in Ontario?

The most popular types of Data Analyst jobs in Ontario are:

What job categories do people searching Part Time Data Analyst jobs in Ontario look for?

The top searched job categories for Part Time Data Analyst jobs in Ontario are:

What cities in Ontario are hiring for Part Time Data Analyst jobs?

Cities in Ontario with the most Part Time Data Analyst job openings:

Infographic showing various Part Time Data Analyst job openings in Ontario as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 19% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

APTPUO-Winter 2027-MEM5300 A (online)

Uottawa

Ottawa, ON • On-site

CA$239.47/hr

Part-time

PTO

This job post has expired today. Applications are no longer accepted.


Job description

Posting Reason:

New Position

Location:

Main Campus

Academic Period:

2027 Winter Semester

Faculty:

Faculte de genie / Faculty of Engineering

Academic Unit:

Ecole de conception et d'innovation pedagogique en genie \\ School of Engineering Design and Teaching Innovation

Course Title:

PRINCIPLES OF DATA ANALYTICS (On line)

Course Code:

MEM5300

Section:

A

Course Description:

Description: This course focuses on the application of data mining techniques and predictive analytics to business problem-solving. It covers key algorithms and techniques for extracting meaningful insights from business data, including data preprocessing, decision trees, neural networks, k-nearest neighbors, clustering, and association rules. Students will gain hands-on experience with data mining tools and software, applying these techniques in managerial contexts such as customer relationship management, marketing, sales, credit scoring, and churn analysis.

Posting limited to:

Professeur a temps-partiel regulier / Regular Part-Time Professor

Date Posted (YYYY/MM/DD):

2026/07/14

Applications must be received BEFORE (YYYY/MM/DD):

2026/08/15

Expected Enrolment:

40

Approval date:

2026/07/14

Number of credits:

3

Work Hours:

39

Hourly Rate:

Enseignement / Teaching: $239.47 (2024-2025)

The academic year starts on September 1 and ends on August 31.

These rates do not included vacation pay nor statutory pay.

These rates will be applied until a new collective agreement is ratified. Retro will be paid after the ratification.

Course type:

B

Posting type:

Regulier / Regular

Language of instruction:

Anglais | English

Competence in second language:

Active

Course Schedule:

Lundi | Monday 19:00-22:00 - -

Requirements:

  • Education: Bachelor's degree in Business, Computer Science, Engineering, or related field is required; Master's in Management or Engineering preferred. A Ph.D. is considered an asset.
  • Industry Experience: Demonstrated track record in professional or managerial roles involving data analytics, data mining, or technology-driven decision-making. Experience as a CTO or equivalent leadership role in a data-intensive or tech-focused organization is highly desirable.
  • Teaching Experience: Prior experience in post-secondary teaching or professional development instruction is preferred.

Technical and Analytical Skills

  • Proficient in data mining and predictive analytics, with the ability to teach both supervised and unsupervised learning techniques, including decision trees, neural networks, k-nearest neighbors, clustering, and association rules; familiarity with tools such as RapidMiner, WEKA, and others is a plus.
  • Extensive experience with IBM SPSS Modeler, including stream creation, model building and evaluation, and applying CRISP-DM within the visual interface.
  • Ability to apply analytical techniques to managerial contexts such as CRM, marketing, sales, credit scoring, and churn analysis.
  • Solid understanding of data preprocessing, including data cleaning, transformation, and partitioning.

Desirable Additional Skills

  • Familiarity with tools such as RapidMiner, WEKA, and other data mining platforms.
  • Knowledge of scripting or programming languages (e.g., Python, R, SQL)
  • Experience with integrating SPSS Modeler with business systems or databases.
  • Knowledge of modern data analytics trends and use of visual programming tools in business intelligence.

Additional Information and/or Comments:

An acceptable level of education and/or experience could be viewed as being equivalent to the educational required and/or demonstrated experience. If you are invited to continue the selection process, please notify us of any adaptive measures you might require. Information you send us will be handled respectfully and in complete confidence. Employees are required under provincial law to successfully complete all mandatory legislated training. The list of training may be modified by provincial law.

The hiring process will be governed by the current APTPUO collective agreements; you can click here for the main unit, here for the OLBI unit, or here for the Toronto/Windsor unit to find out more.

The University of Ottawa embraces diversity and inclusion in the workplace. We are passionate about our people and committed to employment equity. We foster a culture of respect, teamwork and inclusion, where collaboration, innovation, and creativity fuel our quest for research and teaching excellence. While all qualified persons are invited to apply, we welcome applications from qualified Indigenous persons, racialized persons, persons with disabilities, women and LGBTQIA2S+ persons. The University is committed to creating and maintaining an accessible, barrier-free work environment. The University is also committed to working with applicants with disabilities requesting accommodation during the recruitment, assessment and selection processes. Applicants with disabilities may contact vra.affairesprofessorales@uottawa.ca to communicate the accommodation need. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.

Prior to May 1, 2022, the University required all students, faculty, staff, and visitors (including contractors) to be fully vaccinated against Covid-19 as defined in Policy 129 - Covid-19 Vaccination. This policy was suspended effective May 1, 2022 but may be reinstated at any point in the future depending on public health guidelines and the recommendations of experts.