Applied Data Science with R - 6-week tutor-led online course

Organised by

Mind Project Ltd

Presenter

Simon Walkowiak MSc, MBPsS

Date

04/02/2021 - 11/03/2021

Venue

Online

Map

View in Google Maps  (EC2A 4NE)

Contact

Mind Project Ltd
Simon Walkowiak MBPsS
Phone: 02033223786
Email: info@mindproject.co.uk

Description

1. Course description.

During the “Applied Data Science with R” open-to-public online training course you will learn how to apply the R programming language to carry out essential data management, wrangling and processing activities.

This course will introduce you to all basic concepts of data processing and analysis in 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 basic statistical tests e.g. 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.

 

2. Course programme.

This instructor-led course duration is planned over 6 teaching weeks.

In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills by watching pre-recorded instruction videos via our Mind Project Learning Platform and working through set tasks (e.g. quizzes) as well as homework coding exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment is 40-50 hours over 6 teaching weeks.

Start date: Thursday, 4th of February 2021 @10:00am London (UK) time
Schedule of sessions: Every Thursday at 10:00am London (UK) time for 6 weeks
Deadline for registrations: Monday, 1st of February 2021 @ 17:00 London (UK) time

 

Week 1: First step 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.

 

Week 2: Data wrangling with R

  • Working with data frames, matrices, arrays and lists in R,
  • Converting data between different types and classes; factors and ordered factors,
  • 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.

 

Week 3: Exploratory data analysis with R

  • Measures of central tendency, dispersion/variability and other basic descriptive and summary statistics,
  • Value counts, cross-tabulations and data aggregations with tidyverse,
  • Plotting descriptives with ggplot2: basic examples of bar plots, line graphs and boxplots,
  • Faceting - grouped and aggregated plots; multiplots (multiple plots on the same page); additional graphical settings, grid layouts and themes of plots produced 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 variance,
  • Parametric and non-parametrics tests of differences,
  • Power and effect size calculation for inferential tests.

 

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

  • Parametric and on-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. 

 

3. Course pre-requisites and further instructions

  • We recommend that you have the most recent version of R and R Studio software installed on your PC (any operating system). R is a free and open-source environment and you can download it directly from https://cloud.r-project.org/ website. RStudio Desktop (also free) is available at https://rstudio.com/products/rstudio/download/. 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 delegates enrolling on this course, however a keen interest in data analysis and some experience with data processing is assumed.
  • Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) to access our Mind Project Learning Platform.

 

4. Your course instructor.

Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse etc.), research and academia (GSMA, CERN, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).

 

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/applied-data-science-with-r-tutor-led-online-course-feb21/.

Level

Entry (no or almost no prior knowledge)

Cost

By 10th of January 2021 (Early Bird offer):
£345 (normally £420) per person for the whole course (regular fee).
£210 (normally £270) per person for the whole course applicable to undergraduate and postgraduate students, representatives of registered charitable organisations and NHS employees only (discounted fee).
Additional discounts available for multiple bookings and groups.

Website and registration

Region

Greater London

Keywords

Descriptive Statistics, Correlation, Effect size , Statistical Theory and Methods of Inference, Parametric statistics, Non-parametric statistics, Regression Methods, Ordinary least squares (OLS), ANOVA, ANCOVA, Linear regression, R, Data Visualisation

Related publications and presentations

Descriptive Statistics
Correlation
Effect size
Statistical Theory and Methods of Inference
Parametric statistics
Non-parametric statistics
Regression Methods
Ordinary least squares (OLS)
ANOVA
ANCOVA
Linear regression
R
Data Visualisation

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