DATA SCIENCE TRAINING
New to data science or looking to pick up a few new skills? Don’t miss these free webinars, guided practical tutorials and online resources featuring CANUE data.
Developed in partnership with Population Data BC
|Module 1: Introduction to Machine Learning
|Module 2: Regression and Regularization Algorithms
|Module 3: Advanced Supervised Learning
|Module 4: Advanced Unsupervised Learning
Dr. Aman Verma is a Data Engineer with a PhD in Epidemiology from McGill University, and an undergraduate degree in Computer Science. He has experience in developing machine learning systems with large databases, particularly for scientific data in healthcare. While he’s comfortable learning any programming language, he’s recently become particularly interested in R. Aman is currently involved in a number of projects, including measuring how following opioid prescription guidelines can decrease the risk of opioid overdose, modelling trajectories of chronic obstructive pulmonary disease, and assessing how to best prioritize ambulance calls using secondary healthcare data.
AN INTRODUCTION TO DATA MANAGEMENT AND CLEANING FOR ANLAYSIS IN ‘R’
This self paced free online course will provide you with an introduction to Data Management and Cleaning for Analysis using R Software. Each of the four modules includes a Power Point slide deck, CANUE training data, R code and associated exercises for practice.
To access this resource please create a Population Data BC account here: https://my.popdata.bc.ca/accounts/register/
Once your account has been approved you will be able to access the Education and Training site and self enroll in this and other free online courses.
Topics covered include:
- Introduction and theory of data cleaning and management
- Getting started with R software
- Subsetting variables and data cleaning
- Creating variables, subset observations and data cleaning
- Merging, joining and reshaping data
Megan Striha currently works as a Data Analyst. She has a Masters of Public Health degree and three years of experience in health data analysis, including working with survey, administrative and census data.