These tutorials provide an introduction to SAS for individuals working with administrative data.
Topics covered include:
 Introduction to the SAS Windows Environment
 Viewing SAS Data
 Basic Data Manipulation
 Debugging SAS Programs
 Intermediate SAS Statistical Procedures
 Variable Attributes and Display in Output
 Intermediate Data Manipulation
 Combining SAS Data Sets
 Advanced SAS Statistical Procedures
 Getting Data into & out of SAS
These tutorials provide an introduction to Mplus for individuals who wish to use this software program in their analyses.
Topics covered include:
 Introduction to Mplus
 Path Analysis with Mplus
 Factor Analysis with Mplus
 Teacher: PopData BC
These tutorials provide a general introduction to linear regression modeling.
Topics covered include:
 Understanding Simple Linear Regression
 Examining Model Assumptions and fit
 Overview of Multiple Linear Regression
 Multiple Linear Regression: Model Refinement
 Teacher: PopData BC
RStudio is a software application that provides a powerful user interface for the R language which aims to make R easier to use and more productive. It is also free and works on Windows, Mac OS, and UNIX/Linux.
The Introduction to RStudio for SAS users is a best practices document for those who have had prior training in the SAS programming language, but are new to the R language. It does not make onetoone comparisons with SAS commands, statements or syntax, but points out important similarities and differences between SAS and R where appropriate, in an attempt to aid the transition to the R language for users familiar with SAS programming. In such situations, a separate information box ‘For SAS Users’ is provided. However, prior experience with SAS is not required. This document may also serve as a standalone introduction to RStudio.
This webinar series “An Introduction to Data Visualization and Display using R Commander” provides an overview of visualization using the R language’s superb graphics tools. R is a free, opensource language and environment for statistical computing and graphics, with an extensive collection of features for data visualization. We will use both R’s native graphing capabilities and the tools in ggplot2, an R package that is easily installed. Our graphic user interface is a (free) Canadian product from McMaster University called “R Commander.” This is an SPSSlike menudriven GUI that allows access to much of the power of R graphics without the need for programming in R. R Commander’s menu commands create and display the R code which you need. In most cases you will use this code directly to create data visualizations. Occasionally we will make some simple modifications or even write a line of new code. We will run R and R Commander within the highlyregarded web browserlike interface “R Studio.”
This series introduces users to the basic principles of graphing data, visualizing data, and effectively displaying data in documents and dashboards. The webinar series will be divided into four 2hour sessions. Homework activities will be provided for practice between sessions.
 Teacher: Larry Frisch
This four module course will provide you with an introduction to Data Management and Cleaning for Analysis using R Software. Each module includes a PowerPoint slide deck, training data and associated exercises for practice. You many choose to download all documents for use on your computer or practice the exercises within Population Data BC's Remote Training Lab (RTL). The RTL houses all exercises, training data and R software you require.
Module One includes:
 Introduction and Theory of data cleaning and management
 Getting Started with R software
Module Two includes:
 Data Cleaning: Errors and Missing Data
 Subset and Restrict Data
Mo Module Three includes:
 Recoding: Editing Variables and Creating New Variables
Module 4 includes:
 Merging, Joining and Manipulation of Data

Overview
Data Science is concerned with analyzing and reporting on a range of different kinds of data including structured data stored in organizational databases and unstructured data that is often textrich and not collected according to a particular data model. Work in this field requires specialized techniques and tools that draw upon both statistical and computational methods to address complex real world problems and employ multidisciplinary analytics to derive knowledge from large sources of data (Big data).
The following Data Science webinar series will provide an introduction to this rapidly growing field with a particular focus on machine learning methods and analytic techniques that can serve the needs of health and environmental researchers working to understand trends in society, health and human behavior.
The presentations are intended for those who are interested in a broad overview to basic data science analytics. The sessions will benefit health and environmental researchers, analysts and related professionals who want an introduction to data science approaches for data analytics using R software. (Python code will also be provided) The webinar series includes four modules that each include an introductory and practicum session. Each module will focus on the application of specific machine learning methods and analytic techniques with general formulas presented but will not delve into their statistical theory.
Requirements
To benefit from the webinar presentations, registrants should have knowledge of simple and multiple linear regression models and categorical data analysis such as logistic regression.
No prior working knowledge of R or Python is required, but some familiarity with R would be beneficial for following the practicum sessions.
As a supplemental resource for this series, you may wish to review our new free online resource: Data Management and Cleaning for Analysis with R software.
Webinar module resources
All modules include presentation recordings, slide decks, training data, R and Python code, and related references for further reading/study.
Module format
Each module includes two webinar presentations:
 Session 1: A onehour introductory presentation
 Session 2: A twohour practicum session that includes a focus on applied analytics using training data with R code and supplementary Python code.