Training in Statistical Analysis and Data Preparation with R
In 2 days, use R to prepare imperfect data (missing values, imputation, censored data), compare groups, run PCA and regression, test trends and forecast time series.
Real data is rarely clean: missing values, results below a detection limit and irregular measurements can distort an analysis. In 2 days, you learn to prepare this kind of data in R, with several imputation methods and a check of their quality, before applying the statistical tests and models most often used in environmental, scientific and operational data analysis.
You compare groups with ANOVA and non-parametric tests, reduce dimensions with principal component analysis, build regression models, test trends over time and forecast with ARIMA and SARIMA models. Each method is explained, applied in R on example data sets and interpreted.
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The in Statistical Analysis and Data Preparation with R course at a glance
- Length
- 2 days
- Schedule
- 9:00 a.m. to 4:30 p.m.
- Format
- Live virtual class or in person in Montreal and Quebec City
- Level
- Intermediate
- Audience
- Analysts, researchers, scientists, engineers and technicians who already use R and need to prepare imperfect data sets and apply robust statistical methods, including time series analysis.
- Software
- R and RStudio, with common R packages for imputation, statistical tests and time series
- Price
- $1,301 per participant, or $1,105 for public bodies and non-profits (plus taxes)
- Language
- English
Doussou Formation and in Statistical Analysis and Data Preparation with R: key facts
- Since 2016Doussou Formation has been delivering professional training in Quebec
- 300+specialized courses in our catalogue
- 97 %satisfaction according to client evaluations
- RAP · CPMTAccredited supplier and recognized training organization
Upcoming in Statistical Analysis and Data Preparation with R sessions
in Statistical Analysis and Data Preparation with R course outline
Data preparation
- Identifying and visualizing missing data in R
- Simple imputation and multiple imputation
- Random forest imputation
- Values below the limit of detection (LOD): substitution methods
- Handling censored data
- Assessing imputation quality and sensitivity analysis
Quality index analysis and group comparison
- Analysis of variance (ANOVA) in R and its conditions
- Tukey HSD post hoc test
- Kruskal–Wallis non-parametric test
- Interpreting and reporting the results
Factor analysis and multivariate regression
- Principal component analysis (PCA) in R
- Multivariate regression models
- Binary (logistic) regression models
- Checking and interpreting a model
Trend tests and time-based analysis
- Mann–Kendall trend test in R
- Time series analysis: components, seasonality
- Lomb–Scargle periodogram for irregularly spaced data
Time series modelling and forecasting
- ARIMA models: identification, fitting and diagnostics
- SARIMA models for seasonal data
- Forecasting and evaluating forecast quality
- Final exercise: from raw data to forecast
Prices and practical information
Preferred rate
$1,105 / participant
Public bodies and non-profits · plus taxes
Standard price
$1,301 / participant
Businesses and individuals · plus taxes
Practical information
- Length: 2 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
- Funding: Doussou Formation is a training organization accredited by the CPMT. For Quebec employers subject to the Act to promote workforce skills development (payroll over $2M), this course can count toward their 1% training obligation.
- Equipment: A computer with R and RStudio installed. Online: a computer with a good Internet connection, ideally with two screens.
Corporate in Statistical Analysis and Data Preparation with R 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.
Participant reviews
97% satisfaction (end-of-course evaluations, 120 evaluations) · Read all testimonials
Frequently asked questions about the in Statistical Analysis and Data Preparation with R course
Do I need to know R?
Yes, at a basic level: you should be able to import a file, work with a data frame and run a script. The course focuses on statistical methods, not on learning the language.
Is this course very mathematical?
Each method is explained in terms of when to use it, its conditions and how to interpret the results. The emphasis is on practical application in R rather than on mathematical proofs.
What is censored data?
Censored data are values that are only partly known, for example a measurement reported as below the limit of detection (LOD). The course shows how to handle them so they do not bias the analysis.
Can I use my own data?
In a private group, the exercises can be adapted to your own data sets or to a case inspired by your field.
What should I take next?
If you need to strengthen your R skills first, take our R Programming, RStudio and Tidyverse course. To share your results in interactive dashboards, see The Most Comprehensive Power BI Training (2 days).
Why take this in Statistical Analysis and Data Preparation with R course
Objectives: what you will be able to do
- identify missing data in R and choose an appropriate treatment;
- apply simple, multiple and random forest imputation, handle censored data and values below the limit of detection;
- assess the quality of an imputation and run a sensitivity analysis;
- compare groups with ANOVA, the Tukey HSD test and the Kruskal–Wallis test;
- perform a principal component analysis (PCA) and build multivariate and binary regression models;
- detect trends with the Mann–Kendall test and periodicity with the Lomb–Scargle periodogram;
- build and interpret ARIMA and SARIMA time series forecasting models.
Prerequisites
Basic knowledge of R (importing data, data frames, running scripts) and of descriptive statistics. Our R Programming, RStudio and Tidyverse course is a good preparation.
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
Concrete result
You leave with a set of commented R scripts covering each method, from data preparation to forecasting, which you can reuse on your own data, plus the course material and the exercise files.