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Illustration: R and Statistics Training course

R and Statistics Training: Quantitative Methods and Data Analysis

Langage R

  • Formats: live online virtual classroom, or in person in Montréal and Québec City
  • Duration: 3 days (9 a.m. to 4:30 p.m.)
  • Price: $1,472 per participant ($1,251 for public organizations and non-profits), plus applicable taxes
  • Audience: analysts, researchers, professionals and students who need to produce reliable statistical analyses with R
  • Prerequisites: familiarity with the Windows environment and basic knowledge of statistics. Prior experience with R is helpful but not required: the course opens with an overview of R and RStudio.
3 days 9:00 a.m. to 4:30 p.m. Online, Montreal, Quebec City

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The R and Statistics Training course at a glance

Length
3 days
Schedule
9:00 a.m. to 4:30 p.m.
Format
Live virtual class or in person in Montreal and Quebec City
Audience
Tous
Software
R (recent version): free, open-source statistical programming language. RStudio Desktop (Posit): free development environment for R. Quarto: reproducible reports and…
Price
$1,472 per participant, or $1,251 for public bodies and non-profits (plus taxes)
Language
French

Doussou Formation and R and Statistics Training: key facts

Upcoming R and Statistics Training sessions

No public session is scheduled at the moment. Ask for a date or a quote.

R and Statistics Training course outline

Getting Started: Overview of R and RStudio

  • R, RGui and RStudio: main menus and features.
  • Install packages, find help, use AI as a co-pilot.
  • Import data, understand the syntax, run an R script or a Quarto document.
  • Fix syntax (linting) and keep your environment up to date.

Descriptive Statistics

  • Install the tidyverse and packages specialized in quantitative methods.
  • Import, save and explore a dataset.
  • Compute descriptive statistics and frequencies for continuous and categorical variables.
  • Build presentation tables with kable and kableExtra.
  • Chain functions to produce a result, a table or a chart.
  • Visualize variables with base R and ggplot2: histogram, bar chart, density plot.
  • Test normality (Kolmogorov-Smirnov, Shapiro-Wilk, Anderson-Darling) and visualize it (histogram, Q-Q plot).
  • Compare means across groups.
  • Going further: exploring data, descriptive calculations, tables and charts.

Recoding, t-Tests and Cohen's d

  • Repeat the Module 1 workflow on a new dataset.
  • Check the normality of variables.
  • Convert a continuous variable into a categorical variable (factor).
  • Handle missing values.
  • Compare two means with a t-test.
  • Measure the size of the difference with Cohen's d.
  • Going further:
    • t-test variants (one sample, two samples, paired data);
    • equality of variances;
    • non-parametric tests and ordinal data;
    • the proportion test;
    • significance versus power.

Correlation and Simple Regression

  • Label variables to identify them more easily.
  • Visualize and model the relationship between two variables.
  • Compute, present, visualize and test correlations.
  • Build, analyze and visualize a simple linear regression.
  • Recode and handle missing values and outliers.
  • Compare the visualization options of different packages.
  • Going further:
    • quick exploration with base R, polished presentation with ggplot2;
    • regression with or without an intercept.

Day 3

Multiple Linear Regression

  • Recode unusual values: missing, outliers or errors.
  • Move from a two-variable relationship to a multivariable relationship.
  • Build, analyze and visualize a multiple regression.
  • Going further:
    • forms of linear models;
    • validating assumptions: linearity, homogeneity of variance, autocorrelation of residuals;
    • possible corrections;
    • mediating and moderating variables (interactions).

ANOVA and Dummy Variables

  • Review Modules 1 to 4.
  • Make predictions and scenarios from a multiple regression.
  • Encode variables: from categories to numbers, from a factor to dummy variables.
  • Build frequency tables and cross-tabulations.
  • Run, visualize and interpret one-way ANOVA, two-way ANOVA and ANOVA with dummy variables.
  • Going further:
    • hierarchical, generalized, robust and mixed-effects models;
    • non-linear models (logit, Poisson);
    • survival analysis and time series;
    • ANCOVA and MANCOVA.

Appendix

  • Chart types.
  • Review labs.
  • Cheat sheet: base R and tidyverse syntax.

Your R and Statistics Training trainer

Scientifique de données

Hugues S.

A data scientist, Hugues explores data collected within organizations and develops analyses and forecasts, or collaborates on projects aimed at extracting value from collected data. His projects include, among others, customer segmentation, map-based data visualization, transaction matching, and subscription lifetime…

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Prices and practical information

Preferred rate

$1,251 / participant

Public bodies and non-profits · plus taxes

Standard price

$1,472 / participant

Businesses and individuals · plus taxes

Practical information

  • Length: 3 days
  • Schedule: 9:00 a.m. to 4:30 p.m.
  • Format: Live virtual class or in person in Montreal and Quebec City
  • Payment: online (PayPal or card) when registering, or by invoice for organizations
  • Equipment: A computer running Windows 10 or 11, or a recent version of macOS, with at least 8 GB of RAM. Installation rights on the computer, to install R, RStudio and…
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Corporate R and Statistics Training training

Train your whole team as a private group, on your own documents, at your premises or in a virtual class, on the dates that suit you.

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Participant reviews

97% satisfaction (end-of-course evaluations, 120 evaluations) · Read all testimonials

Why take this R and Statistics Training course

Objectives: what you will be able to do

  • explore a dataset and derive descriptive statistics, tables and charts;
  • check the normality of variables and handle missing values and outliers;
  • compare means with a t-test and measure effect size with Cohen's d;
  • analyze correlations and build simple and multiple linear regressions;
  • use dummy variables, make predictions and run one-way or two-way ANOVA;
  • automate your analyses in a reproducible pipeline, with the help of AI.

Prerequisites

  • Connaissance de l’environnement Windows

What is included

  • Course materials and exercise files for each participant
  • Certificate of attendance
  • Optional personalized coaching after the course
  • In public in-person sessions:
    • Tea and coffee
    • Lunch at a nearby restaurant
    • Parking (in some cities)
    • Wi-Fi access

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