This course will introduce participants to all basic concepts of Data Analysis in R environment. More specifically participants will learn how to input different types of data, prepare, transform and manage datasets and their variables, export/import data files, create simple graphical representations of the data (bar plots, histograms, box plots etc.), run basic statistical tests (e.g. correlations, t-tests etc.), obtain descriptive statistics from a dataset and formulate the results. The course will also provide an introduction to hypothesis testing and modelling using multiple linear regression methods and it will introduce the attendees to data visualisation techniques 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 packages e.g. dplyr, ggplot2, tidyr, readr, tibble and other essential R libraries e.g. data.table, lubridate, Hmisc, readxl, haven etc.
Throughout the course the attendees will learn the following concepts:
R environment: what is R?; Starting R environment; Basic settings and functions; Introduction to IDEs e.g. RStudio,
Mathematical functions and control flow operators; R-related help and support; Installing and running third-party packages,
R data structures: creating scalars, vectors, matrices, arrays, lists and other data objects in R; Creating simple data frames,
Data input and export: adding/deleting observations; Sampling; Flagging/identifying specific cases based on conditional search; Sorting cases; Adding/editing value and variable labels; Dealing with missing data; Reshaping data from long/narrow into wide formats,
Exploratory Data Analysis: inspecting the structure of data objects; Cross-tabulations and descriptive statistics (measures of central tendency and dispersion); Vertical/horizontal merging of data frames and other R objects; Basic EDA plot: histograms, density plots, scatterplots, box plots, bar plots, line graphs etc.,
Tests of differences and correlations; Testing for normality assumptions: QQ, density plots and test-specific normality measurements; One-sample, matched-sample and independent t tests; Correlations and simple regressions; Test-specific visualisation functions/packages; Effect size and power estimation,
Data modelling: ANOVA and multiple regressions; Introduction to logistic and Poission regressions,
Introduction to data visualisations: creating informative data visualisations using R core and third-party packages; Using graphical parameters for adding/editing text, titles, lines, fonts, colours, axes, background and other elements of plots; Introduction to ggplot2 syntax and an rCharts example,
Creating a simple data product with R; Data cleaning, EDA, data management, data “crunching” and analysis, data visualisation, model optimisation and debugging.
The course will run for three days (Wednesday to Friday) between 9:30am and ~5:00pm and will consist of alternating lecture-style presentations and practical tutorials. The example datasets used during tutorial sessions will come from social sciences, psychology and business fields, however the contents may vary depending on specific interests of participants (based on the Participant’s Skills Inventory). There will be two 15-minute coffee/tea breaks and one 1-hour lunch break on each day of the course. Please note that the course will be run in English.
What is included?
Apart from the contents of the course, Mind Project will provide the participants with the following:
- a digital (USB memory stick) Course Manual including all presentation slides, R course codes and a list of reference books and online resources,
- additional home exercises and all data sets available to download,
- Wi-Fi access,
- Central Brussels location in the heart of the EU district – only a 7-minute walk to the European Parliament and a 5-minute walk away from 3 metro stations: Trone, Arts-Loi and Maalbeek,
- networking opportunity,
- Mind Project course attendance certificate.
In order to benefit from the contents of the course it is recommended that attendees have the most recent version of R and R Studio software installed on their personal laptops (any operating system). As R is a free environment you can download it directly from www.r-project.org website and R Studio is available at https://www.rstudio.com/products/rstudio/#Desktop. Please contact us should you have any questions or issues with the installation process. No specific R packages are required before the course (the course tutors will explain this during the training).
No prior knowledge of R is required from participants enrolling on this course, however a keen interest in data analysis is assumed.
Attendees are encouraged to complete the online Participant’s Skills Inventory to allow Mind Project and our course tutors to customise the contents of the course depending on the level of participants’ knowledge and their areas of interest. The data obtained through the Participant’s Skills Inventory will be held fully-confidential and will only be used to provide a quality data analysis training.
Deadline for registrations
The deadline for registrations on this training course is Friday, 31st of August 2018 at 20:00 Brussels (Belgium) time. However, Mind Project reserves the right to end the registration process earlier if all places are booked before the deadline.
Prices and discounts
- 750 Euro per person for the whole course (regular fee).
- 525 Euro per person for the whole course for undergraduate and postgraduate students as well as academic researchers based in Belgium and all representatives of charitable organisations (discounted fee).
- For group bookings of 4 and more participants, please contact us directly.
Please mind that the course fee DOES NOT include the following:
- transport to and from the venue,
- accommodation and lunch.
Please contact us should you have any questions about this course. You may also want to visit the Training Courses – Frequently Asked Questions website, which gives further practical details about Mind Project training courses. You can book your place on the course by clicking Book ticket button in the top section of the course page. Please note that we accept all major credit/debit cards (through the PayPal and Stripe systems) and BACS payments. We can only confirm fully-paid bookings. Please contact us for other payment options e.g. if a Purchase Order is required. Please read Training & Events Terms & Conditions before your purchase.
Course feedback and testimonials
We have received the following testimonials for the previous editions of this course:
- “The course provides a beginner enough knowledge to start analysis using R. I appreciated the instructor’s knowledge and the willingness to give as much explanation as possible.” – Prof. Sunil Sahadev, Salford Business School
- “Engaging teaching, very useful content.” – Kirsty Syder, MarketingQED
- “Excellent course for the intro to R, I highly recommend it.” – Dr Edward Barker, Institute of Psychiatry, King’s College London
- “I’ve been meaning to start R for a long time now and this course gave me the perfect platform to develop into a great analyst.” – Thomas Lees, Your Favourite Story, data analyst
- “I enjoyed my time during the course, developing an understanding of R programming. The teaching staff was nice and professional. I definitely recommend it.” – Pierluigi Patruno, University of Westminster
- “A very clear and practical overview of R and how it can be used in real world data sets. Learning from an expert!” – G’s Group Holdings
- “A concise, excellent introduction to the possibilities of the R programming language.” – Al Board, Origo
- “This was a great course – very practical and taught in a relaxed style.” – Sophie Reid, Design Council
- “Informative and very useful, interactive course.” – Kuldeep Sohal, Bradford Institute for Health Research
- “This course offers comprehensive coverage of fundamental R usage. Simon was a very helpful teacher.” – Peter Morgan, JB Medical Ltd
- “This course was a great way to get an introduction to R and how it can be used to perform statistical analysis and run data visualisations.” – Daria Kuznetsova, Director of Strategy and Research, Teach First
- “The course provided a great overview of R and skills required for the first steps in the field of data science.” – Gerasimos Fotiadis, Kindred
- “Applied is the key word here, it isn’t about theory (like at universities), useful starting points to take to work and expand upon.”
- “A great bootcamp-style course. In three days I went from bare basics to wanting to replace my other tools with R.”
- “Great 3 day course which covered the basics of R in an interactive and fun way. Provided lots of examples and great enthusiasm.”
- “Mind Project Ltd style of teaching R as a tool for data science is very clear, precise, practical and concise.”
- “The course met my expectations. Simon is a great tutor and I would be attending future data science courses.”
- “I would highly recommend it. The way this potential complex subject was broken down in simple English with practical examples was amazing.”
- “A very fun course, with excellent support materials to move you well up the R learning curve.”
Based on the anonymous feedback forms we have also received the following comments from our attendees:
- “I liked the opportunity to move from knowing hardly anything to knowing enough to manipulate data in a day.”
- “I liked the pace, topics (because of variety), tasks and explanations.”
- “The breadth of material covered – from basic manipulation of data to high-level visualisations.”
- “The course took me from basic R concepts through to fairly advanced visualisations. It began with background to R, which is always useful information. Very hands-on, and used real datasets.”
- “Good balance between worked examples and practice exercises. Showed some examples of what is possible with R.”
- “Very clear explanation of concepts. Lots of suggestions of packages and processes. Interactive. Good coverage of fundamentals of the language.”
- “Good demonstration, interactive, plenty to learn.”
- “Combination of practical examples with theory. Ability to begin with confidence in R outside of the classroom.”
- “It exceeded my expectations. I think the make-up of delegates helped. Speed was perfect and everyone took it seriously. I now can start using R at work on Monday.”
- “It’s good to be instructed by an excellent practitioner.”
- “Wide variety of material covered, well-suited to many abilities, well-taught, relaxed atmosphere.”
- “Good overview of what is available on R, great presenter – with extensive knowledge. I also liked that we were asked to fill in a pre-assessment questionnaire.”
- “Interactive course packed with information. Good class size to allow for tailored learning.”
The course will be held at Science 14, Rue de la Science 14, 1040 Bruxelles. Please see the map below.