Data Skills Foundation Courses at ESS

The Essex Summer School (ESS) is introducing Data Skills Foundations courses, a professional development programme running from April to June 2025. This hybrid-format training consists of three course clusters, each containing three two-day modules, specifically designed for professionals seeking to enhance their data literacy and analytical capabilities.

The programme begins with Foundations of Data Literacy (April 10-25), focusing on practical Excel-based skills. This is followed by Introduction to R and Basic Data Analysis (May 1-17), where participants transition to the powerful R programming environment. The final cluster, Intermediate Statistics with R (June 5-20), advances into more sophisticated analytical techniques and visualisation methods.

Each module runs for two consecutive days, allowing for intensive learning while maintaining flexibility for working professionals. Participants can enrol in individual modules, complete clusters, or join the entire programme, with sessions available both in-person and online.

Click on the heading of each course for a day-by-day outline.

A. Foundations of Data Literacy (Modules 1-3)

1. Module 1 April 10,11: Data Skills Foundations

This module aims to establish the importance of data literacy within organisations. Participants will learn basic data concepts, data management principles, and ethical considerations, all through the practical application of Microsoft Excel.

  1. Introduction to Data Literacy
  2. Planning Data Collection
  3. Entering, Organizing, and Cleaning Data
  4. Data Validation
  5. Data Security and Ethical Considerations

2. Module 2 April 17,18: Data Analysis and Interpretation

This module aims to equip participants with the skills to analyse and interpret with Excel. By the end of this module, participants will be able to draw insights from data, perform basic descriptive and inferential statistics, and identify trends, patterns, and correlations.

  1. Drawing Insights from Data
  2. Basic Descriptive Statistics
  3. Identifying Trends and Patterns
  4. Correlation and Regression Analysis
  5. Data Interpretation and Decision-Making

3. Module 3 April 24,25: Communicating with Data

This module aims to equip participants with the skills to effectively communicate data insights through visual elements and compelling narratives.

  1. Fundamentals of Data Visualisation
  2. Creating Effective and Professional Data Visualisations
  3. Data Storytelling Principles and Best Practices
  4. Using Data to Support Ideas and Make Recommendations
  5. Presenting Data Insights to Different Audiences

B. Introduction to R and Basic Data Analysis (Modules 4-6)

4. Module 4 May 1,2: Introduction to the R Environment

This module introduces participants to R programming and the RStudio environment, focusing on
basic syntax and data structures.

  1. Introduction to R and RStudio
  2. R syntax, data types, and basic operations
  3. Working with vectors, matrices, and data frames
  4. Control structures
  5. Writing functions in R

5. Module 5 May 8,9: Data Manipulation with R

This module focuses on data manipulation techniques in R, introducing participants to powerful packages for data transformation.

  1. Reading and writing data in various formats
  2. Data cleaning and preprocessing
  3. Data validation
  4. Introduction to data manipulation with Base R and tidyverse
  5. Working with dates and times in R

6. Module 6 May 16,17: Basic Statistical Analysis in R

This module covers fundamental statistical analysis techniques using R, building on the skills learned in previous modules.

  1. Descriptive statistics in R
  2. Probability distributions and random number generation
  3. Hypothesis testing
  4. Correlation analysis
  5. Simple linear regression
  6. Introduction to reporting outputs using R Markdown

C. Intermediate Statistics with R (Modules 7-9)

7. Module 7 June 5,6: Data Visualisation with R

This module focuses on creating effective data visualisations using R, introducing participants to ggplot2, tidyverse, and other packages related to visualisation.

  1. Introduction to ggplot2 and the grammar of graphics
  2. Creating and customizing various plot types
  3. Advanced plotting techniques
  4. Interactive Visualisations with plotly
  5. Creating maps and spatial Visualisations

8. Module 8 June 12,13: Data Analysis and Modelling in R

This module covers more advanced statistical techniques and expands on Module 3 and 6 content within the R environment

1. Advanced Regression Techniques
2. MLE techniques
3. Time Series and Panel Analysis
4. Model Diagnostics
5. Model Selection
6. Hierarchical models

9. Module 9 June 19,20: Advanced Data Types and Reporting Workflows in R

This module has two major focuses: First, it covers advanced data types and text analysis, teaching participants to work with complex data structures, regular expressions, and text analytics. Second, it develops professional reporting skills through R Markdown, teaching participants to create dynamic documents, implement reproducible research practices, and establish automated reporting workflows.

1. Complex Data Structures
2. Text Processing Fundamentals
3. Unstructured Data Analysis
4. Text Analytics Applications
5. Dynamic and Automated Documentation and Reporting

 

Individuals interested in these courses should email essexsummerschoolssda@essex.ac.uk.
Applications will be open by 15 February 2025.