This meetup will have area university instructors and data science practitioners provide a series of structured classes in data science to area participants interested in the subject.
Start/end dates: Sign up for our start date this fall!
Days: Consecutive Sundays
Times: 2 PM to 4 PM (45 minutes lecture, 45 minutes hand’s on “project lab”)
6 sessions, 2 hours per session = 12 total class hours.
Session 1 Introduction
Session 2 Data Visualization
Session 3 Data Characterization with Statistics
Session 4 Machine Learning 1
Session 5 Machine Learning 2
Session 6 Project Presentations, Industry Advice
Introduction, Data Science
Foundational topics in data science aiming to focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will allow the student to understand, sample and apply the basic techniques of data science in diverse scenarios.
The art and science of turning data into readable graphics. This class is designed to provide students with the foundations necessary for understanding and extending the current state of the art in data visualization while illustrating the significance, benefits, Do’s and Don’ts of Data visualization.
Data Characterization with Statistics
Why do we need to know Statistics, What is Statistics, Descriptive Versus Inferential Statistics, Types of Variables. Statistics in Business Decisions. How to statistically and visually describe Data.
Machine Learning I
What is Machine Learning, Why do we need Machine Learning, When to use Machine Learning, Types of Machine Learning, Machine Learning Workflow, Tools and Frameworks – FOSS – Python?
Machine Learning II
Build on the concepts introduced in Machine Learning I. Explore Supervised and Un-Supervised Machine Learning and learn how to use one algorithm from each group, and progress throw the ML work flow.
Project Presentations, Industry Advice
The final class will consist of project presentations from students of an application of their Data Science skills. The student shall find a data set and perform data science tools to uncover insights from the data. Also, the class may optionally invite industry speakers, discuss use cases, and assist student with job seeking for Data Scientists.
Two data sets will be introduced and used throughout the course as a basis of instruction, homework, and end of course projects and group presentations.
The course is free with a recommended $4 donation per class to cover course expenses.
If you have any questions, please emai Anthony Klinkert at AKlinkert@Collin.edu