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Event Phone: 819-816-3150

  • January 16, 2019
    9:30 am - 12:30 pm
  • January 23, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • January 30, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • February 6, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • February 13, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • February 20, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • February 27, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • March 6, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • March 13, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • March 20, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • March 27, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • April 3, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • April 10, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • April 17, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • April 24, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • May 1, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • May 7, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • May 23, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • May 29, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)
  • June 5, 2019
    9:30 am - 12:30 pm
    - Datetime Description (optional)

Class size limit has been reached — new registrations will be sent to the waiting list.

Length: 20 x 3 hour sessions, weekly, at Impact Hub Ottawa (123 Slater, 6th floor). Sessions on 23 Jan, 20 Feb, 27 Mar, 3 Apr, 5 Jun will be held at 373 Sussex.
Level: Halfway between a university course and the Applied Data Science and Visualization Training courses.

An introduction to Data Science, data analysis, and data understanding, with topics selected from:

  1. Data Science Prerequisites
    Theory: 8 hours, Practice: 7 hours — universals of data science; basics of R/Python; data cleaning; data reduction; case studies
  2. Data Visualization
    Theory: 5 hours, Practice: 1 hour — simple graphical methods, representations of multi-dimensional data, design suggestions
  3. Introduction to Machine Learning
    Theory: 13 hours, Practice: 5 hours — association rules, decision trees, k-means, issues and challenges, naïve Bayes, hierarchical clustering
  4. Text Mining and Natural Language Processing
    Theory: 8 hours, Practice: 4 hours — basics, classification, clustering, sentiment analysis, named-entity recognition, summarization, topic modeling, etc.
  5. Focus on Supervised and Unsupervised Learning
    Theory: 6 hours, Practice: 3 hours — density-based clustering, spectral clustering, validation metrics, support vector machines, neural networks, etc.

The goal of this workshop is to provide an introduction to various concepts and algorithms used in A.I. and Data Science, as used in common programming environments. The application of these concepts will be illustrated through some examples ranging from simple to elaborate, along with discussions of common challenges and pitfalls.

There will be a combination of traditional lectures with Jupyter notebook exercises.

Audience and Requirements: This workshop is for individuals who wish to understand the functionality and capabilities offered by different data science concepts and methods, even if they won’t be the ones implementing them. This workshop requires very little mathematical or computer programming knowledge. Some experience with quantitative ideas is assumed. Necessary concepts will be introduced in the course.

Participants must provide a laptop with wi-fi connectivity and the ability to run R/Python notebooks from a web browser (typically, Chrome, Firefox, Edge. Some issues have been encountered with Internet Explorer and the WebSockets driver). Exposure to programming (R/Python/SAS/etc.) a plus.

Expectations: 60 hours of additional independent learning outside of class; suggested readings and exercises

Venue:  

Venue Phone: (613) 680-3506

Venue Website:

Address:
123 Slater Street, 6th Floor, Ottawa, Ontario, Canada

Post Author: Patrick Boily

Patrick is interested in the applications of mathematics and statistcs to evidence-based decision support. He has worked on 25+ such projects since 2008. He has extensive experience in data science, machine learning, A.I. and predictive analytics, data cleaning and data visualization.