Course summary

During the “Applied Data Science with R” open-to-public instructor-led training course you will learn how to apply the R programming language to carry out essential data management, wrangling and statistical analysis operations. The mixture of online pre-recorded instruction videos, weekly live group webinars, additional 1-2-1 check-in (either via email or on Microsoft Teams) and several homework exercises throughout the duration of the course will ensure you will be able to apply R language to your own data and research questions in a matter of weeks.

This course will introduce you to all fundamental concepts of data processing and analysis in the R environment. More specifically, you will learn to understand different types of data and common data structures available in R language, prepare, transform and manage datasets and their variables, export/import data from various file formats (Excel spreadsheets, csv, tab, txt etc.), create simple graphical representations of the data (bar plots, histograms, box plots etc.), obtain summaries, data aggregations, cross-tabulations, frequency and pivot tables, and run and explain results of common statistical tests e.g. both parametric and non-parametric inferential hypothesis testing through correlations, t-tests etc. The course will also provide an introduction to modelling using multiple linear regression methods and will introduce you to data visualisation techniques available in R for data reporting and research communication.

The course will cover modern approaches in applied data science using R language and its rich ecosystem of external libraries including tidyverse family of packages e.g. dplyr, ggplot2, tidyr, readr, tibble and other essential R libraries for data wrangling and statistics.

Who is this course for?

This course is recommended to anyone interested in data science and R language who is looking for a thorough, structured and tutor-led online training provided by a recognised and experienced organisation.

The course will be particularly of interest to the following groups and categories of attendees:

• social and operational researchers, social scientists with psychology, social sciences, medical science, biomedical science and similar background,
• statisticians and data scientists who would like to add R to their data science toolkit,
• undergraduate and postgraduate students, post-docs and more senior academic researchers willing to learn R for their projects,
• business and risk analysts, marketing analysts and data analysts requiring a thorough training in applied data science in R.

What is included in the course fee?

At Mind Project, we pride ourselves for delivering great quality, goal-directed, interactive and cost-effective training. Our remote, hybrid, and on-premises tutor-led courses are designed to maximise your opportunity to learn complex topics in shortest possible time while being able to discuss and interact with our tutor and other fellow attendees. Therefore, the registration fee for this course includes a number of benefits that many other training providers don’t offer:

• Weekly (90-minute long) remotely delivered live group webinars with our expert tutor over the period of 6 weeks – you will be able to ask questions, interact and discuss the topic with other attendees from the comfort of your home/office,
• you will have access to course online materials for 1 year via our Mind Project Learning Platform e.g. tutorial videos, exercises and quizzes, R code scripts used during tutorials and live sessions, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
• 2-week post-course follow-up period during which you will be able to work on homework exercises and submit them for our feedback – the feedback will be provided either via email or Microsoft Teams (i.e. as a 15-minute 1-2-1 session with our tutor),
• exercises and tasks completed by you between the live webinar sessions will help you in better material retention and will enhance your learning progress,
• email supervision and weekly check-ins during the course period will maximise your learning outcomes and improve your learning experience; we will provide you with support throughout the course and the 2-week post-course follow-up period – feel free to ask us questions about the material presented at the training during this period,
• small group size allows easy interaction and stimulating environment for successful learning.

Programme outline

During this course, you will learn a variety of data science approaches for data wrangling, exploratory analysis, visualisations and statistics with the R programming language. The course will be run according to the following schedule:

Week 1: First steps with R language

• Introduction to R language, RStudio and the ecosystem of packages in R,
• Generating random data; logical and mathematical operations in R,
• Built-in R types and data structures,
• Data import/export to/from various file formats,
• Working with data frames, matrices, arrays and lists in R,
• Converting data between different types and classes; factors and ordered factors.

Week 2: Data wrangling and describing data with R

• Essential data wrangling operations: e.g. subsetting, filtering, renaming variables, recoding values and creating new data,
• Introduction to working with strings, dates and time stamps,
• Measures of central tendency, dispersion/variability and other basic descriptive and summary statistics,
• Value counts, cross-tabulations and data aggregations with tidyverse.

Week 3: Data visualisations with R

• From data summaries, exploratory data analysis to visualisations,
• Plotting descriptives with ggplot2: examples of bar plots, line graphs and boxplots,
• Introduction to faceting – grouped and aggregated plots, and additional graphical settings, grid layouts and themes with ggplot2 and associated R packages.

Week 4: Inferential statistics and hypothesis testing with R – Part 1

• Understanding hypothesis testing and traditional test assumptions e.g. normality and homogeneity of variances,
• Parametric and non-parametric tests of differences,
• Power and effect size calculation for inferential tests.

Week 5: Inferential statistics and hypothesis testing with R – Part 2

• Parametric and non-parametric tests of relationships,
• Introduction to linear and non-linear models,
• Analysis of Variance (ANOVA),
• Main effects, random effects and interactions.

Week 6: Linear and non-linear models with R

• Understanding multiple linear regression,
• Regression metrics and evaluation of multiple linear regression models,
• Non-linearity in regression models,
• Comparing regression models.