In this introductory course, you will learn the basic tools and techniques for data science applied to business practices—also known as business analytics. You will gain insight into the use of machine learning and data science for business applications and will have the opportunity to bring your own data for hands-on mentoring. No prior programming, statistical or database skills are required, but all are a plus.
If you want to start discovering data science and machine learning concepts without a mathematical or programming background, this course is for you!
What you will learn
- How to use Microsoft Azure Machine Learning, a cloud-based ML and data insights platform
- Basic machine learning techniques that can be applied for predictive modeling, such as price prediction from product features
- Basic descriptive techniques, such as clustering, to derive insights from your business data
- Automated text analytics (for user reviews, requests for product features, actionable customer feedback, etc.)
- How to design data science experiments to determine the effectiveness of your models
Meetings and format
The course is delivered via Zoom videoconference with live instruction. To attend, you need a computer with webcam, microphone and high-speed internet.
Each Zoom session allows for live interaction with the instructor and other students via chat, web conferencing or phone, all in real time. Assignments and other materials will be available online through a web-based learning management system.
Who should attend
Working professionals, engineers, managers, anyone involved in the areas of product/process development and design, sales, operations, supply chain management, e-commerce/product listing or similar areas of work.
Working professionals who want to gain a deeper understanding of their company’s data, and how to use it to generate insights and react quickly to customer and market feedback.
The instructional fee is $745, including a nonrefundable administrative fee of $75. See Registration for details.
The registration fee is the same regardless of residence.
Grant Williams earned a Doctorate in Computer Science from Louisiana State University, where his research focused on empirical software engineering. He worked as a data scientist at Microsoft Corporation, where he implemented machine-learning solutions for automatically identifying bugs in Windows updates using social media. He is scholarly assistant professor of computer science at WSU Vancouver.
A typical course program involves the following:
- Intro session
- Cohort introductions and icebreakers
- Introduction to data science and business analytics
- Provisioning a cloud machine-learning environment (Azure ML)
- Creating a basic Azure machine-learning pipeline
- Choosing your capstone project
- Prediction and classification
- Introduction to supervised machine learning
- Sample datasets (automobile price data and U.S. income)
- Setting up linear regression analysis
- Prediction and classification on your own data using Azure ML
- Decision trees, forests and how to know which is better
- Introduction to various Azure machine-learning classifiers
- How do we know how good a classifier is?
- Introduction to cross-validation
- NYC yellow cab locations and fares dataset for non-linear modeling
- Evaluating classification on your own data
- Introduction to descriptive techniques with K-Means clustering
- Sample datasets (Seattle safety and NYC Yellow Cabs)
- Hands-on creating a clustering pipeline with Azure
- Visualizing clusters
- Clustering your data
- Introduction to natural language processing
- Turning text into numbers
- Text classification
- Movie review score prediction from text and S&P 500 (stock market) sector prediction from text description datasets
- Capstone project presentations
- Capstone projects are chosen by you at the beginning of the program (in alignment with the needs of your employer) and provide the “living lab” to use the concepts as taught in course modules
Applied learning model
- Sessions will be a mix of short traditional lectures and hands-on interactive analytics projects based on business-relevant sample data
- Bring your own data to use in class (or data will be provided). Suggestions regarding appropriate data will be made ahead of each meeting. You may substitute our sample data with your own and transform it to develop analysis pipelines
- Present your project to a group of peers during the final class
- The combination of the in-class training and the application of course concepts on real-world capstone projects will increase your depth of knowledge
- An Azure subscription and the payment for compute time that your project requires. A free Azure subscription can be created here: https://azure.microsoft.com/en-us/free/. It is recommended to wait until right before class to get 30 days of free Azure compute, which will take you through the course. Once the free period ends, if you decide to continue using it after the course ends, Azure is billed “as you go” to an attached credit card.
- Course materials will be provided as PDFs
Minimum one year of college courses or relevant industry experience, associate degree preferred and English proficiency.
- Attend at least five of six classes
- Completion and presentation of capstone project