Training and Events
Applied Data Science in R - London - 3-Day Training Course (few places remaining)
|Mind Project Ltd|
Simon Walkowiak MBPsS
27/11/2017 - 29/11/2017
CAP House, 1st Floor, 9-12 Long Lane, London, EC1A 9HA
View in Google Maps (EC1A 9HA)
Mind Project Ltd
1. Course description.
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 Regression analyses and ANOVAs. Methods of data visualisation will be presented for each statistical test.
Throughout the course the attendees will learn the following concepts:
The course will run for three days (Monday to Wednesday) 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.
3. What is included?
Apart from the contents of the course, Mind Project will provide the participants with the following:
4. Further instructions.
Should you have any questions please contact Mind Project Ltdat firstname.lastname@example.org or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/applied-data-science-in-r-london-november-2017/.
Entry (no or almost no prior knowledge)
£475 + VAT (£570) per person for the whole course (regular fee).
Website and registration
Quantitative Data Handling and Data Analysis, Descriptive Statistics, Statistical Theory and Methods of Inference, Microdata Methods, Regression Methods, Generalized liner model (GLM), ANOVA, Linear regression, Logistic regression, Data Mining, Quantitative Approaches (other), ICT and Software, Quantitative Software, R, Data Visualisation
Related publications and presentations
Quantitative Data Handling and Data Analysis