University of Bath

Introduction to Analytics

A powerful new approach to analysing small and big data using open-source R software. Led by prominent thinkers in data science and forecasting.

Participants in discussion in a management training workshop
On our Introduction to Analytics programme, you will learn to identify correlations and patterns in data and use software for statistical analysis and forecasting

Explore the world of analytics

This two-day programme aims to provide an introduction to the world of analytics and how it is possible to analyse and visualise small and big data using the R open source statistical software.

It combines theoretical aspects of descriptive and predictive analytics with a hands-on approach to R statistical software, using real-life data from a range of different industries.

On the first day, we focus on data visualisation, transformation and exploratory analysis, while on the second day we explore patterns between multiple variables as well as prepare predictions using time series forecasting methods.

During this programme, you will be using analytics with R, the preferred statistical software for business. However, the principles can be easily applied across other statistical software.

Who should attend

The programme is mostly suitable for middle-level managers including but not limited to:

  • business analysts
  • data scientists
  • supply chain managers
  • demand planners
  • marketing analysts
  • sales forecasters.

What the programme covers

Day 1: Exploratory analysis

Introduction and managing data

After a short introduction to analytics, we will introduce the main tools used in the programme (R statistical software and RStudio interface) and use these for simple operations and demonstration of useful functions. We will explore commonly used data structures and demonstrate how we can load data from external data files.

Data visualisation

We will focus on one of the most commonly used, versatile and widely supported systems for making graphs: ggplot2. ggplot2 is a coherent system for describing and building graphs. We will introduce aesthetic mapping, faceting and a range of basic geometrical objects that can be used with ggplot2 to represent data.

Data transformation

Visualisation is an important tool for insight generation, but it is rare to get the data in exactly the form you need. We will look at ways to use R to filter, arrange, select and manipulate elements and variables of the data, as well as the basics of using logical operators to transform data.

Exploratory data analysis

We will show how to use transformation and visualisation for exploratory data analysis. We will look at methods to obtain descriptive statistics and to visualise variation and distribution, such as histograms, density plots and boxplots.

Day 2: Exploratory analysis and prediction

Bivariate data and correlation

We will consider displays of bivariate data, which are instrumental in revealing relationships between variables. We will explore the application of visual methods of displaying data, as well as scatterplots and quantile plots and methods to calculate correlation between variables.

Simple regression

Quite often, we wish to model the effect of one variable to another, such as the effect of advertising on sales, so we can use the insights gained to optimise decisions. We will demonstrate how to model such relationships through linear regression modelling using R. We will look at the statistical output of regression and use it for extrapolation and decision-making.

Multiple regression

This session focuses on models where multiple independent variables are affecting (and can potentially be used for predicting) the dependent variable. Through an example, we will explore the effects of different promotions on product sales. We will demonstrate how it is possible to include additional variables. We will also show how we can decide to include and exclude important variables to build a model with maximised predictive power.

Univariate predictive analytics

In many cases, the only data available are past observations of the variable under investigation. This session focuses on univariate models for prediction of the future values of a sequence of observations through capturing fundamental time series patterns, including trends and seasonal cycles. Simple and more complex models will be applied through the R statistical software to automatically produce predictions.

How you will benefit

  • Acquire, clean, visualise, and analyse data
  • Identify correlations and patterns in data
  • Use software for statistical analysis and forecasting
  • Improve awareness of variability in data
  • Understand uncertainty in forecasts

How your organisation will benefit

Your organisation will gain:

  • understanding from visualising and analysing real world data
  • business insights from quantitative data analysis
  • understanding of the benefits of open source software, which could potentially be applied directly

Fees, dates and location

Fees

Three-day programme: £1,200.

Dates

2018 dates to be confirmed.

Location

The programme is run at University of Bath in London, 83 Pall Mall.

Register your interest in this programme

Programme leaders

Dr Fotios Petropoulos

Fotios Petropoulos is an Associate Professor at the School of Management, University of Bath and elected Director at the International Institute of Forecasters. His research programme has addressed behavioural aspects of forecasting and forecast process improvement in business and supply chain. He is also interested in research involving big data as well as how information can be extracted from time series. Fotios is also a frequent contributor to the practitioner-oriented journal Foresight: The International Journal of Applied Forecasting, where he also serves as the editor for forecasting support systems. He is co-founder of the Forecasting Society, which promotes and disseminates judgmental forecasting research and its applications.

Dr Lukasz Piwek

Lukasz Piwek is an Assistant Professor in Data Science at the School of Management, University of Bath. He is a co-founder of interdisciplinary Psychology Sensor Lab and member of an ESRC-funded Centre for Research and Evidence on Security Threats (CREST). His interdisciplinary research work focuses on using Big Data obtained from mobile devices, smart wearables, apps and social networks in user profiling, behavior change and developing new research methodology, as well as novel data visualization techniques - with a primary application for healthcare and security.