Data science for the public good

1. Introduction to R

This newly redesigned Introduction to R is a 2-day course with a week between the sessions to allow assimilation of learning and time to practice. This course focuses on applying skills throughout, and building confidence, independence, and resilience so that you can continue your learning beyond the classroom. No prior knowledge is needed to take part in this course. 

2. Introduction to Python

This newly redesigned Introduction to Python is a 2-day course with a week between the sessions to allow assimilation of learning and time to practice. This course focuses on applying skills throughout, and building confidence, independence, and resilience so that you can continue your learning beyond the classroom. No prior knowledge is needed to take part in this course. 

3. Introduction to Data Visualisation

This is a theoretical course that covers the best practices in how to present your data in tables and graphs, following the guidelines for official statistics.

4. Data Visualisation in R

This course explains how to take the best practice principles from Introduction to Data Visualisation and practically apply these using the programming language of R. It will take you from first principles through to producing production ready visualisation.

5. Data Visualisation in Python

This course explains how to take the best practice principles from Introduction to Data Visualisation and practically apply these using the programming language of Python. It will take you from first principles through to producing production ready visualisation.

6. Introduction to Natural Language Processing in R

In this introductory course we will cover the basics of Natural Language Processing (NLP) topics including the process of ‘cleaning’ a dataset, exploring it and applying simple feature engineering techniques to transform the data which will help you for expanding your analysis capabilities.

You will be able to:

• Understand the fundamental concepts and techniques of natural language processing
• Learn to use a regular expression to extract patterns from text
• Understand and apply the necessary steps to ‘clean’, explore and transform the dataset in the appropriate order
• Implement text analysis (sentiment analysis) with real datasets.

7. Introduction to Natural Language Processing in Python

Natural Language Processing (NLP) is a sub-field of Artificial Intelligence. It is used for processing and analysing natural language, and can be scaled up to large amounts of text. This is an Introduction to Natural Language Processing, and thus the main concepts are about text cleaning, processing, exploring datasets, and becoming familiar with working with text based data in Python.

8. Natural Language Processing in Python

This course adds further topics, building on content from the Introduction to Natural Language Processing (NLP) in Python course. You will learn important components used for modelling text including numerical feature representation for text and basic forms of modelling language. This course assumes the introduction course has been completed, and some machine learning experience.

9. Introduction to Machine Learning in R

This course builds on the fundamentals of programming and statistical concepts to introduce the domain of machine learning in R. The course employs the state of the art “mlr3” machine learning package for implement of classification, regression and cluster analysis experiments.

10. Introduction to Machine Learning in Python

Machine learning is becoming more and more integrated in modern data analysis. Before performing advanced prediction, basic concepts in machine learning modelling must be understood and applied. In this newly refreshed course you will learn about a range of machine learning topic areas as well as how to actually perform the modelling operations using the Python programming language and the package scikit-learn. The course covers key aspects of machine learning including:

• Data preparation
• Regression
• Classification
• Dimension reduction
• Clustering

11. Statistics in R

This course is for someone interested in learning statistical methods and how to apply those methods in R. It is an even split of theory and coding and covers:

• Exploratory Data Analysis
• Statistical Tests
• Linear Regression
• Model Adequacy and Model Selection
• Generalised Linear Models

12. Statistics in Python

This course introduces the basics of carrying out statistical analysis in Python. It covers exploratory data analysis and constructing and interpreting linear and generalised linear models in Python.

13. Quality Assurance of Predictive Modelling

As models are used more frequently to make predictions and decisions across industries it becomes important that good practice is followed in model design and usage. In this course we will explore important issues that arise in the field of statistical modelling and machine learning for prediction. The course is a series of modules exploring quality issues related to predictive modelling.

14. Modular Programming in Python and R

As programming becomes more crucial to analysis, the need for well-structured code has increased. A key component in well written, reproducible code is the idea of “modular” design. This is the splitting up of scripts into different units which have specified processes and outputs.

In this course you will learn important concepts relating to modular design, how to convert code to functions and modules in either R or Python.

15. Introduction to Pyspark

This course will give you an understanding of Pyspark, the Python interface to the distributed processing tool Spark. With it, you will be able to handle huge datasets effortlessly, and process, query, and manipulate data which is beyond the reach of traditional programming languages.

16. Foundations of SQL

This course introduces the basic syntax of SQL, which is applicable to any flavour of the language used. The course uses an online platform to complete exercises in SQLite.

The main topics covered are:

• Basic SQL queries
• Manipulating tables and their properties
• Joining tables
• Database management

17. How to request Learning Hub access

To access courses from our catalogue, you will need access to a Learning Hub account.

Access is offered to those working within the Government Analysis Function or working within an analytical role with a collaborating organisation. To check eligibility, for new account requests or if you require help accessing a pre-existing Learning Hub account, please email the Data Science Campus providing the following:

• Your name (given and surname)
• Email
• Organisation
• Job title

Please add “Requests for Learning Hub access” as the email subject.

Any information you provide:

• will be strictly confidential
• will only be available to the Data Science Campus Faculty
• will be stored securely on ONS systems.

We collect your data to:

• help administer the creation of your Learning Hub account
• report aggregated, anonymised summary totals of learners to the UK Statistics Authority on a quarterly basis as required by our work programme.

We will not:

• sell or rent your data to third parties.
• share your data with third parties for marketing purposes.

For more information on how the Office for National Statistics processes your data, please refer to the ONS data protection privacy information.