Data Skills Courses at ESS

The Essex Summer School (ESS) is introducing new Data Skills courses for professional development 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 from 10 am to 5 pm, 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 for both in-person and online participation.

 

Courses

A. Foundations of Data Literacy (April 10-25)Build essential skills for working with data, including concepts, basic analysis, and effective reporting. These modules are based on instruction in Microsoft Excel. No previous background needed.

1. Module 1 (April 10, 11): Data Skills Foundations – Covering essential concepts for working with data in Excel.

2. Module 2 (April 16, 17): Data Analysis and Interpretation – A first introduction to data analysis, including Excel fundamentals and basic statistics.

3. Module 3 (April 24, 25): Communicating with Data – Develop skills in creating effective visualisations and presenting data-driven stories.

B. Introduction to R and Basic Data Analysis (May 1-16)

Learn the basics of the powerful R statistical programming environment, along with the fundamentals of data manipulation and analysis. The ‘Foundations’ course (A) is not required, but recommended for those with no previous experience in data analysis.

4. Module 4 (May 1, 2): Introduction to the R Environment – Get started with R programming through hands-on practice and basic data operations.

5. Module 5 (May 8, 9): Data Manipulation with R – Learn data transformation and validation techniques to prepare data for analysis.

6. Module 6 (May 15, 16): Basic Statistical Analysis in R – Apply statistical methods and create professional visualisations.

C. Intermediate Statistics with R (June 5-20)

Intended for those who have taken the ‘Introduction’ course (B) above, these modules take your data skills to the next level with intermediate statistics using the R environment and more advanced visualisation, modelling, text processing, and automated reporting techniques.

7. Module 7 (June 5, 6): Data Visualisation with R – Create advanced visualisations and interactive graphics using specialised R packages.

8. Module 8 (June 12, 13): Data Analysis and Modelling in R – Explore regression analysis, time series, and predictive modelling techniques.

9. Module 9 (June 19, 20): Advanced Data Types and Reporting Workflows in R – Master automated reporting and interactive dashboards using R Markdown.

 

Course Fees

First module taken: £500
Additional modules: £350
Cluster of three modules taken together: £1200

Applications are open through 5 March.

Apply here

If you are interested in these modules, contact us.

A. Foundations of Data Literacy (April 10-25)

Module 1: Data Skills Foundations (April 10,11)

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.

  • Introduction to Data Literacy
  • Planning Data Collection
  • Entering, Organizing, and Cleaning Data
  • Data Validation
  • Data Security and Ethical Considerations

Module 2: Data Analysis and Interpretation (April 16,17)

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.

  • Drawing Insights from Data
  • Basic Descriptive Statistics
  • Identifying Trends and Patterns
  • Correlation and Regression Analysis
  • Data Interpretation and Decision-Making

Module 3: Communicating with Data (April 24,25)

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

  • Fundamentals of Data Visualisation
  • Creating Effective and Professional Data Visualisations
  • Data Storytelling Principles and Best Practices
  • Using Data to Support Ideas and Make Recommendations
  • Presenting Data Insights to Different Audiences

 

B. Introduction to R and Basic Data Analysis (May 1-16)

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

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

  • Introduction to R and RStudio
  • R syntax, data types, and basic operations
  • Working with vectors, matrices, and data frames
  • Control structures
  • Writing functions in R

Module 5: Data Manipulation with R (May 8,9)

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

  • Reading and writing data in various formats
  • Data cleaning and preprocessing
  • Data validation
  • Introduction to data manipulation with Base R and tidyverse
  • Working with dates and times in R

Module 6: Basic Statistical Analysis in R (May 15,16)

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

  • Descriptive statistics in R
  • Probability distributions and random number generation
  • Hypothesis testing
  • Correlation analysis
  • Simple linear regression
  • Introduction to reporting outputs using R Markdown

 

C. Intermediate Statistics with R (June 5-20)

Module 7: Data Visualisation with R (June 5,6)

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

  • Introduction to ggplot2 and the grammar of graphics
  • Creating and customizing various plot types
  • Advanced plotting techniques
  • Interactive Visualisations with plotly
  • Creating maps and spatial Visualisations

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

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

  • Advanced Regression Techniques
  • MLE techniques
  • Time Series and Panel Analysis
  • Model Diagnostics
  • Model Selection
  • Hierarchical models

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

This module covers advanced data types and text analysis, teaching participants to work with complex data structures, regular expressions, and text analytics.

  • Complex Data Structures
  • Text Processing Fundamentals
  • Unstructured Data Analysis
  • Text Analytics Applications
  • Dynamic and Automated Documentation and Reporting

Interested in these courses?

Applications are open through 5 March.

Apply here

Contact Us with questions