Applied Data Science with R - London - 3-Day Training Course

Description:

1. Course description.

During the “Applied Data Science with R” training course you will learn how to apply the R programming language to carry out essential data management, wrangling and processing activities. The course is suitable for data and insights analysts/scientists, data engineers and data product developers who are responsible for pre-processing of data, analytics and reporting of findings.

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 e.g. data.table, lubridate, Hmisc, readxl, haven etc.

2. Programme.

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, business and finance 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.

The programme for this course covers the following concepts and topics:

• R environment: what is R?; Introduction to IDEs e.g. RStudio; Starting R environment; Basic settings and functions,
• 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 and manipulating simple data frames,
• Data import and export: reading/writing data from/to various file formats (Excel spreadsheets, standard file formats e.g. csv, tab, txt etc.),
• Essential data processing: 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; working with dates and timestamps,
• Exploratory data analysis: inspecting the structure of data objects; cross-tabulations, data summaries, aggregations, frequency testing and descriptive statistics (measures of central tendency and dispersion); vertical/horizontal merging of data frames and other R objects,
• Introduction to data visualisations: creating informative data visualisations using R core and third-party packages; essential exploratory plots e.g. histograms, density plots, scatterplots, box plots, bar plots, line graphs etc.; Using graphical parameters for adding/editing text, titles, lines, fonts, colours, axes, background and other elements of plots; Introduction to the Grammar of Graphics with the ggplot2 syntax,
• Tests of differences and correlations; Testing for normality assumptions: QQ, density plots and test-specific normality measurements; One-sample, matched-samples and independent t tests; Correlations and simple regressions; Test-specific visualisation functions/packages; Effect size and power estimation,
• Data modelling: ANOVA and multiple linear regressions – understanding multivariate inferential tests and statistical outputs; Using regressions for predictions on test data,
• Creating a simple data product with R; From data cleaning, exploratory data analysis, data management, data wrangling to analysis, data visualisation, model optimisation and debugging.

3. What is included?

Apart from the contents of the course, Mind Project will provide you with the following:

• printed course pack with all presentation slides, cheatsheets and other essential course information,

• 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,

• stimulating, friendly and inclusive learning environment in a small group (typically 10-14 attendees) led by experienced and energetic tutors and course leaders,

• modern and comfortable training venue located in the heart of City of London – at the London Institute of Banking & Finance, next to the Monument underground station,

• refreshments and a light, energising lunch on each day of the course,

• Wi-Fi access,

• networking opportunity,

• Mind Project course attendance certificate.

4. Further instructions.

• In order to benefit from the course, we recommended that all 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 and open-source 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 delegates enrolling on this course, however a keen interest in data analysis and some experience with data processing is assumed.

• The deadline for registrations on this training course is Friday, 24th of May 2019 at 16:00 London (UK) time. Mind Project reserves the right to end the registration process earlier if all places are booked before the deadline.

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-london-may-2019/.

Cost:

£825 per person for the whole course (regular fee).
£675 per person for the whole course for UK registered undergraduate and postgraduate students, and representatives of registered charitable organisations (discounted fee).
For group bookings of 4 and more delegates, please contact us directly.

Website and registration:

https://www.mindproject.io/product/applied-data-science-with-r-london-may-2019/

Region:

Greater London

Keywords:

Quality in Quantitative Research, Descriptive Statistics, Correlation, Effect size , Statistical Theory and Methods of Inference, Regression Methods, Ordinary least squares (OLS), ANOVA, Linear regression, Quantitative Software, R, Data Visualisation, Creating graphs and charts

Related publications and presentations:

Quality in Quantitative Research
Descriptive Statistics
Correlation
Effect size
Statistical Theory and Methods of Inference
Regression Methods
Ordinary least squares (OLS)
ANOVA
Linear regression
Quantitative Software
R
Data Visualisation
Creating graphs and charts

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