How does one manage a team facing real data analysis? In this one-week course, we contrast the ideal with what happens in real life. In this way, you will learn key concepts that will help you manage real-life analysis.

Learn about data science and how you can use it to strengthen your organisation. This course will teach you about the skills you need on your data team, and how you can structure that team to meet your organisation's needs. It will also provide you with an understanding of data sources your company can use and how to store that data.

This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

This course will cover the basic ways that data can be obtained: from the web, from APIs, from databases and from colleagues in various formats. It will look at the basics of data cleaning and how to make data tidy. The course will also cover the components of a complete data set, including raw data, processing instructions, codebooks and processed data, and the basics needed for collecting, cleaning and sharing data.

In this course, you will learn the many ways to import data into Python: from flat files such as .txt and .csv; files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Practical data cleaning is a thorough introduction to the basics of data cleaning. It is perfect for beginners and takes you through data collection, data cleaning, data classification and data integrity.

This course covers the essential exploratory techniques for summarising data. These techniques are typically applied before formal modelling starts and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.

Build data analysis and business modelling skills. Gain the ability to apply statistics and data analysis tools to various business applications.

Statistics you need in the office: descriptive and inferential statistics, hypothesis testing and regression analysis.

Master statistics with R. Statistical mastery of data analysis including inference, modelling and Bayesian approaches.

Acquire new skills fast in courses that combine short expert videos with immediate hands-on-keyboard exercises. A collection of courses using Python, from an introduction, to modules and data importing, data cleaning, and data visualisation all using Python.

This course will give you a full overview of the data science journey. Upon completion, you will know: how to clean and prepare your data for analysis; how to perform basic visualisation of your data; how to model your data; how to curve-fit your data; and how to present your findings.

Within months, COVID-19 went from an epidemic to a pandemic. From the first identified case in December 2019, how did the virus spread so fast and widely? In this free R project, you will visualise data from the early months of the coronavirus outbreak to see how this virus grew to be a global pandemic.

In this course, you will learn how to communicate analytics results to stakeholders who do not understand the details of analytics but want evidence of analysis and data. You will be able to choose the right vehicles to present quantitative information, including those based on principles of data visualisation. You will also learn how to develop and deliver data analytics stories that provide context, insight and interpretation.

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery.

This course is intended as an introduction to the R programming language for individuals working in the financial sector. Through the use of taught material and practical examples, which all have a financial twist, participants will build up the skills needed to perform data analysis using R.

This course is intended as an introduction to the Python programming language for individuals working in the financial sector. Through the use of taught material and practical examples, which all have a financial twist, participants will build up the skills needed to perform data analysis using Python.

Most influential business decisions are made through combining convincing data with an influential and persuasive background story. In this practical course you’ll learn how to transform your numbers into compelling narratives that inspire and motivate action.

This one-day course introduces delegates to statistical forecasting. It is designed for anyone involved in business planning and performance analysis. The course uses a mixture of stimulating discussions and real-life case studies. Each concept will be carefully explained, its relevance to forecasting highlighted and show how the relevant calculations can be done in Excel.