Predictive Analytics

Select start date and campus

Menu
Close

Applying as a Canadian applicant

Domestic students should apply online or by phone at 1-888-892-2228.

Apply now: Canadian applicant

Applying as an International applicant

International students should apply online.
Apply now: international applicants
Close

Campus tours

Campus tours are one of the best ways to experience Conestoga. During this time, we are offering online guided tours to show you all Conestoga has to offer.

Book your tour

Virtual tours

If you can't make an on-campus tour or attend one of our events, the virtual tour is a great way to visit us.

View our Virtual tour

Courses - January 2025

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:

Project Management
MGMT8666

Description: Project management has become central to the operations of any organization. This course focuses on the general principles of project management as well as Agile Project Management and its methodologies. This course takes a holistic, integrated approach to managing projects, exploring both technical and managerial challenges. Students will apply skills gathered from examining real-world cases into simulated projects done in the classroom. This course will help prepare students to write the Project Management Institute (PMI) exams to become a Certified Associate in Project Management (CAPM).
  • Hours: 56
  • Credits: 4
  • Pre-Requisites:
  • CoRequisites:

Fundamentals of Programming
PROG8491

Description: Dynamic object-oriented programming languages can be used in many kinds of analytical problem solving. Python is designed with features to facilitate data analysis and visualization. We will design, code, test, visualize, analyze, and debug data solutions based on industry standard Python data analysis packages. We will focus on the problem-solving ability and creativity that companies are increasingly looking for in data analytics talent.
  • Hours: 56
  • Credits: 4
  • Pre-Requisites:
  • CoRequisites:

Data Modelling for Analytics
PROG8501

Description: Before complex data sets can be useful to the stakeholder, a variety of data sets and data models are required for data analysis and interpretation. This course will introduce students to various company challenges/opportunities as well as the concepts to source, prepare, and visualize the data for ease of communication.
  • Hours: 56
  • Credits: 4
  • Pre-Requisites:
  • CoRequisites:

Statistical Applications for Data Analytics I
STAT8021

Description: The field of statistics is the science of learning from data that can offer essential insight to determine which data and conclusions are trustworthy. When statistical principles are correctly applied, statistical analyses tend to produce accurate results. With a variety of mathematical theories needed throughout the predictive analytics field, students will review areas including matrix algebra, hypothesis testing, linear and non-linear regression modelling, estimation models and bootstrapping. Over the duration of the course, we will use industry standard statistical applications for implementation purposes.
  • Hours: 56
  • Credits: 4
  • Pre-Requisites:
  • CoRequisites:

Multivariate Statistics
STAT8031

Description: Through the use of theory and applications students will be introduced to various multivariate statistical analysis methods. An emphasis will be placed on multiple regression, correlation, classification and high-dimensional data. Students will work to develop models for multivariate data and use samples of data to predict behaviour about unknown variables.
  • Hours: 56
  • Credits: 4
  • Pre-Requisites:
  • CoRequisites:

Level 2

Course details

Career Management
CDEV8132

Description: This course focuses on career management skills needed to navigate the evolving workplace. Students will evaluate their skills, attitudes, and expectations within their chosen careers and explore emerging trends in the workplace. Students will refine their networking strategies and create marketing documents to position them for success. Mock interviews will provide the opportunity for practice, feedback, and reflection as students prepare for future interviews. Students will explore communication strategies that support workplace success and advancement. By the end of this course, students will have created a personalized career management plan.
  • Hours: 28
  • Credits: 2
  • Pre-Requisites:
  • CoRequisites:

Management and Leadership Essentials
MGMT8761

Description: In this course, students will enhance their understanding of leadership and management approaches in Canadian organizations. Emphasis on developing effective management strategies including, professional communication, planning, decision making, conflict resolution, effective team building and navigating change. Key concepts include professionalism, adaptability, boundaries and resourcefulness.
  • Hours: 42
  • Credits: 3
  • Pre-Requisites:
  • CoRequisites:

Programming Statistics for Business
PROG8511

Description:

Using analytical software and programming, students will learn to delve into data sets and draw conclusions using exploratory analytics, predictive and statistical techniques. Including confidence intervals, t-tests, and statistical inference. Students will also practice visualizing their findings in a way that will effectively communicate their conclusions to business stakeholders.

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

Data Modelling II – Analytics
PROG8521

Description:

This course will build on the predictive analytics tool kit by utilizing both supervised (dependent variable) and unsupervised (no dependent variable) learning methods to uncover the information from the data and then act on this information. Building models such as Classification and Regression (supervised) as well as Clustering (unsupervised) will be covered. Students will apply data modelling techniques using Microsoft Excel.

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

Statistical Forecasting
STAT8041

Description:

This course will apply the principles of time series analysis to teach students to analyze data where multiple measurements are made over time using common statistical forecasting techniques. Students will study the key concepts, patterns, and relationships found in time series data. Emphasis will be placed on forecasting and predictive analysis.

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

Statistical Applications for Data Analytics II
STAT8051

Description:

This course builds on the skills developed in Statistical Applications for Data Analytics I to further explore data analytics using current industry tools and best practices. Students will learn to use common industry tools and practices to analyze and interpret analytical models and results to facilitate decision making and generate visualizations. Practical applications of descriptive, predictive, and prescriptive data mining and analysis will be explored across a broad range of industries.

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

Program outcomes

  1. Develop high quality software solutions to collect, manipulate and mine data sets that satisfy the business requirements of organizations
  2. Analyze different system architectures and data storage technologies in order to select appropriate solutions that support data analytics requirements.
  3. Design data models that meet the needs of the predictive analysis process.
  4. Develop software solutions that align with the predictive analysis process to produce desired reports.
  5. Analyze existing data visualization methods used in business to recommend customizations that align with the predictive process.
  6. Customize business intelligence tools to support evidence-based decision making based on the predictive process.
  7. Employ environmentally sustainable practices within the field of data analytics.
  8. Predict industry trends and collect insights to expand the organization’s entrepreneurial strategies and generate new opportunities.
  9. Examine relationships among multiple variables simultaneously to predict the effect of proposed changes to business.