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

Date:

26/10/2020 - 30/11/2020

Organised by:

Mind Project Ltd

Presenter:

Simon Walkowiak MSc, MBPsS

Level:

Entry (no or almost no prior knowledge)

Contact:

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

video conference logo

Venue: Online

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 (to qualify for the Course Attendance Certificate) plus an additional 1 calendar month for the completion of the data science project (to obtain the graded Course Completion Certificate).

In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills working through set tasks and homework exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment for the Course Attendance Certificate is 40-50 hours over 6 teaching weeks, and for the Course Completion Certificate it will equate to 70-80 hours (over 2.5-month period) including the project report writing time.

Start date: Monday, 26th of October 2020 @14:30 London (UK) time
Schedule of sessions: Every Monday at 14:30 London (UK) time for 6 weeks
Deadline for registrations: Friday, 23rd of October 2020 @ 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; introduction to probability distributions,
  • Parametric tests of differences,
  • Parametric tests of relationships,
  • Power and effect size calculation for inferential tests.

 

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

  • Testing nominal variables,
  • Non-parametric tests of differences,
  • Non-parametric tests of relationships,
  • Introduction to linear and non-linear models.

 

Week 6: Linear and non-linear models with R

  • Analysis of Variance (ANOVA),
  • Main effects, random effects and interactions,
  • Understanding multiple linear regression,
  • Non-linearity in regression models.

Additionally, in order to receive the full Course Completion Certificate, you will have to submit a short data analysis report (up to 2,000 words) along with R data processing and analysis scripts within one calendar month from the last day of Week 6. The project will be assessed and graded. You will also receive a formal written feedback about your project.

 

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). As R is a free and open-source environment you can download it directly from https://cloud.r-project.org/ website and RStudio Desktop 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) during the tutor-led video sessions.
  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) in order to attend the video-streamed tutorials. You may also use your mobile phone (Android or iOS) to connect to our tutor-led video sessions.
  • The primary spoken and written language of the course is English.

 

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-oct20/.

Cost:

By 17th of August 2020 (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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