DAT 120 - Practical Machine Learning for Business
Course Description
Machine Learning is changing the way we live every day. Obtain the essential knowledge and skills on how machine learning and data science are applied in real life. Explore specific use cases in several data-driven industries. Learn how to apply machine learning to help businesses build better products, improve decision-making, and reduce costs.
This course is delivered in collaboration with WeCloudData
Course Details
Learning Outcomes
By the completion of this course, successful students will be able to:
- Describe four practical use cases of data science including predictive churn modeling, customer segmentation, propensity modeling, and customer lifetime value
- Describe three common data use cases in the Canadian job market including customer relationship management, risk management, and SaaS
- Evaluate at least one industry use case using a machine learning algorithm
- Implement machine learning models using various techniques such as classification, regression, and unsupervised clustering algorithms and apply them in different real-world scenarios including credit scoring for risk management, acquisition models for sales, and churn modeling for marketing
- Explain and present the machine learning outcome and actional insights in layman’s terms to business stakeholders with visual storytelling to support business decision making
Topics
- Python for Statistics & Data Science
- A refresher of Pandas Dataframe and basic data manipulation
- Using python for statistical testing
- Machine Learning Concepts and Algorithms (classification, regression, unsupervised clustering)
- Writing your first ML algorithm from scratch
- Use Case: AB Testing with Python for product growth
- Machine Learning Techniques (full lifecycle)
- A refresher of machine learning pipelines
- Parameter tunings in Python
- Dealing with an imbalanced dataset
- Common feature engineering techniques
- Industry Use Cases:
- Predictive Churn Modeling in Telecom
- Customer Segmentation and Clustering in Retail
- Time series forecasting with ML in Finance
- Building retail credit risk models in Banking
Who is this course for?
This course is designed for:
- Business associates, project managers
- Professionals from every level or industry who work with analytics or data
- Recent graduates and academics in Computer Science
- Individuals who are aspired to pursue career opportunities like data scientist/data science analyst, predictive modeller, modeling analyst
Notes
Software Requirement:
To complete the labs activities and mini-project, you will require:
- Anaconda Python Distribution
Prerequisites
There are no mandatory prerequisites for this course, however you are required to perform self-assessment to ensure you meet the requirements to enrol.
Self-assessment for enrolment:
A minimum of 6 months experience in python programming
Recommended pre-requisites:
Applies Towards the Following Program(s)
- Machine Learning and Visualization : Required