Hands-On Workshop: Competency-Based Experience Featuring Python & Predictive Modeling

Abstract: 

Are you looking for new and effective ways to learn data science? Join our workshop on competency-based education (CBE) in data science and explore cutting-edge approaches to learning in the fast-paced world of technology. Our workshop offers a unique perspective on virtual, CBE education and explores how the latest online learning resources and mentoring models can be used to support those looking to pick up a new skill. Whether you're a beginner or an advanced learner, our workshop has something for everyone! Choose to engage in a foundational Python course or a more advanced predictive modeling course and dive into data analysis topics at different levels of complexity. Our in-room instructors will guide you every step of the way, and you'll have the opportunity to gain practical skills & knowledge in your chosen skill domain. Don't miss this opportunity to enhance your data science skills and take your career to the next level in an unforgettable learning experience!

Workshop Agenda

1. Introductions of workshop leaders
2. Introduction to competency-based online learning and comparison to other models for teaching
data analytics and data science
3. Learning resource navigation tutorial
4. Work time – participants navigate learning resources and learn content in either foundations of
Python or predictive modeling in either Python or R.
5. Work product submission – workshop participants submit code they have written based on
provided task instructions
6. Work products are reviewed by instructors after the workshop and feedback is provided to
advance learning.
Learners will also receive handouts on the key components of competency-based learning and criteria
for selecting strong learning resources for online education. Instructors will be available in the room
during the workshop to address content questions in either course, and workshop leaders will also be
available to address questions about online competency-based learning for data science and analytics.

Workshop Content

“Introduction to Python” includes content on:
1. Introduction to Python language
2. Variables and Expressions
3. Data types
4. Control structures
5. Functions
6. Strings
7. Lists and Dictionaries
8. Exceptions
9. Modules and import statements
10. Files
“Predictive Modeling” includes content in both Python and R on :
1. Introduction to regression (single-variable linear and logistic regression)
2. Intermediate regression (multiple-variable linear and logistic regression)
3. Linear modeling
4. Predictive Analytics

Learning Outcomes

Attendee differentiates competency-based model of learning from other models
Learning Objectives:
Attendee differentiates a competency-based learning model from a “seat-time” model
Attendee identifies advantages of a mentoring model compared to other online learning models

Attendee accesses & navigates the workshop’s available online learning resources
Learning Objectives:
Attendee accesses online learning resources for either “Introduction to Python” or the “Predictive Modeling”
Attendee completes formative assessments

Attendee completes summative assessment activities in either “Introduction to Python” or “Predictive Modeling” learning resources.
Learning Objectives:
Attendee successfully navigates the learning resources to locate content related to the assessment
Attendee utilizes real-time (in-person) instructor support as needed to complete assessment activities
Attendee submits summative assessment by the end of the workshop

Workshop participants are strongly recommended to bring their own laptops with headphones or earbuds. A limited number of laptops and headphones will be made available if workshop participants do not have these. While the learning resources are mobile-friendly, completing the activities will be much more efficient using a laptop.

Bio: 

Daniel J. Smith, PhD, MBA has worked at WGU for 3 years. He has experience in several industries in analytics through the director level in insurance, health care administration, and higher education. His experience is in AI and machine learning applications in industry using R, Tableau, SAS and Python. He enjoys working with students to improve their analytical, programming, and communication skills.

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

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