Excel courses


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Introduction to Health Data Analysis

Welcome! In this course, we’ll dive into the fundamentals of understanding what qualifies as data, along with the common methods for data input, processing, and management—all within a healthcare framework. We’ll focus specifically on clinical and scientific terminology, as well as coding practices commonly used in healthcare. This course is ideal for anyone currently working in, or aspiring to work in, healthcare who anticipates engaging with data (spoiler: in today’s digital world, that likely includes you!). Over the two weeks, you’ll participate in three live virtual sessions, supplemented by video lessons, hands-on tasks, and a final project to complete between sessions.

Difficulty: Beginner
Price: £500

Course outline

What You’ll Learn:

  • The fundamentals of health data analysis, including key concepts and terminology
  • How to identify, collect, and manage health data from various sources
  • The basics of clinical coding and scientific terminology used in healthcare
  • Methods for processing and analysing health data to extract meaningful insights
  • How to work with healthcare data using Excel and other basic analysis tools
  • The ethical and privacy considerations when working with health data

Learning Objectives:
By the end of this course, you will be able to:

  • Define key concepts related to health data, including types of health data and common healthcare datasets
  • Understand and apply basic data input and processing methods in healthcare
  • Work with clinical and scientific terminology, including ICD codes and medical classifications
  • Perform simple data analysis tasks using Excel, such as sorting, filtering, and basic calculations
  • Recognise the ethical issues surrounding health data, such as patient privacy and data security
  • Understand the key steps involved in health data analysis, from data collection to generating insights

Teaching and Learning Methods:

  1. Hands-on Exercises: Practical tasks in Excel to apply data analysis concepts to health data.
  2. Video Tutorials: Pre-recorded lessons that cover specific techniques for working with health data.
  3. Group Discussions: Opportunities to discuss concepts and share experiences with fellow students in live sessions.
  4. Final Project: A project that challenges you to analyse a sample health dataset and present findings.

Course Content:

Week 1

  • Introduction to Health Data: What qualifies as health data, and how is it used?
  • Types of Health Data: Clinical data, administrative data, and public health data
  • Clinical Terminology & Coding: An overview of ICD, OPCS, and SNOMED codes and other common medical classifications
  • Data Input and Collection Methods: Common ways health data is gathered from different sources
  • Basic Data Processing in Excel: Introduction to sorting, filtering, and organising health data in Excel

Week 2

  • Basic Data Analysis Techniques: Calculating averages, medians, and basic statistical measures
  • Identifying Patterns in Health Data: Recognising trends, anomalies, and correlations in healthcare datasets
  • Ethical Considerations in Health Data Analysis: Privacy concerns, security, and patient consent
  • Data Visualisations for Healthcare: Creating simple charts and graphs to communicate health data insights
  • Final Project: Analyse a sample healthcare dataset and present your findings through basic analysis and visualisations

Assessment:

  • Hands-on Exercises: Regular tasks to practice health data analysis techniques using Excel.
  • Final Project: A final assignment where students will analyse a sample health dataset, identify trends, and present their findings with a focus on basic analytical methods and data visualisation.

A certificate of competency will be awarded upon successful completion, provided that attendance and the final project meet the required criteria.


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Cleaning and Processing Health Data in Excel

Welcome! In this course, we’ll explore essential techniques for cleaning and pre-processing health data using Excel. You’ll learn how to identify and handle common data quality issues, such as missing values, duplicates, and inconsistencies, all while ensuring the integrity of health-related information. Through practical examples and hands-on exercises, we’ll cover key methods for transforming raw data into usable datasets, tailored specifically for the healthcare context. This course is perfect for anyone working with health data—whether you’re a healthcare professional, data analyst, or someone looking to build their data management skills. Over the two weeks, you’ll engage in live virtual sessions, guided tutorials, and a final project to solidify your knowledge and skills in Excel-based data cleaning.

Difficulty: Beginner/Intermediate
Price: £500

Course outline

What You’ll Learn:

  • How to identify and correct common data quality issues in health datasets
  • Techniques for handling missing data, duplicates, and outliers
  • Methods for data normalisation, transformation, and standardisation
  • Practical Excel tools for cleaning and pre-processing health data
  • Best practices for preparing data for analysis or reporting in healthcare contexts

Learning Objectives:
By the end of this course, you will be able to:

  • Apply Excel functions and features to clean and pre-process raw synthetic health data
  • Identify and address common data quality challenges in health datasets
  • Prepare clean, reliable data for further analysis or visualisation
  • Understand the importance of data integrity and how it impacts healthcare decision-making
  • Work confidently with health-related data in Excel, using key tools such as filters, text functions, and data validation

Teaching and Learning Methods:

  1. Live Virtual Sessions: Interactive, instructor-led sessions to discuss key concepts and answer questions.
  2. Hands-on Exercises: Practical tasks designed to apply data cleaning techniques directly in Excel.
  3. Video Tutorials: Pre-recorded lessons that cover specific techniques for cleaning and transforming health data.
  4. Group Discussions: Opportunities to collaborate and share insights with peers during live sessions.
  5. Final Project: A comprehensive assignment that requires you to clean and pre-process a sample health dataset.

Course Content:

Week 1:

  • Introduction to Health Data: Understanding common challenges and considerations
  • Data Quality Issues: Missing values, duplicates, and inconsistencies
  • Excel Tools for Cleaning: Functions like IFERROR, VLOOKUP, and CONCATENATE
  • Techniques for Handling Outliers and Data Normalisation
  • Data Validation and Error Prevention in Excel

Week 2:

  • Standardising Health Data: Using Excel features for consistent formatting
  • Handling Categorical Data and Dates
  • Pre-processing Health Data for Analysis: Best practices for transforming raw data into usable formats
  • Real-world Case Studies: Working through healthcare-related examples
  • Final Project: A hands-on task to clean and pre-process a health dataset in Excel

Assessment:

  • Hands-on Exercises: Short exercises after each session to practice newly learned techniques.
  • Final Project: A capstone project where students clean and pre-process a health dataset, demonstrating their understanding and skills.

A certificate of competency will be awarded upon successful completion, provided that attendance and the final project meet the required criteria.


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EXCEL–lent Visualisations

Welcome! In this course, we’ll focus on creating compelling visualisations in Excel to help you effectively communicate health data insights. You’ll learn how to design and implement a variety of charts and graphs—ranging from basic bar and line charts to more advanced visualisations like heat maps and pivot charts. Special attention will be given to choosing the right visualisation for different types of health data, ensuring clarity and ease of understanding for your audience. This course is ideal for healthcare professionals, data analysts, or anyone looking to enhance their ability to present data in a visually engaging way. Over the two weeks, you’ll participate in live virtual sessions, video tutorials, hands-on tasks, and a final project to reinforce your skills in Excel-based data visualisation.

Difficulty: Intermediate
Price: £500

Course outline

What You’ll Learn:

  • How to create a variety of effective charts and visualisations in Excel
  • How to select the right chart type for different types of health data
  • How to design visuals that communicate data insights clearly and effectively
  • Techniques for customising and improving chart aesthetics for professional presentations
  • How to use advanced Excel features to create dynamic and interactive visualisations

Learning Objectives:
By the end of this course, you will be able to:

  • Create a range of professional charts and visuals in Excel, including bar charts, line graphs, heatmaps, and more
  • Choose the most appropriate chart type for your dataset and intended audience
  • Customise and enhance visualisations to improve clarity and readability
  • Use advanced Excel tools like PivotTables, slicers, and conditional formatting for interactive visuals
  • Design impactful health data visuals that effectively communicate trends, patterns, and key insights

Teaching and Learning Methods:

  1. Live Virtual Sessions: Instructor-led sessions covering the theory behind effective data visualisation, followed by practical demonstration in Excel.
  2. Hands-on Exercises: Interactive tasks to practice creating and customising visuals in Excel.
  3. Video Tutorials: Step-by-step guides for creating specific types of visuals and applying advanced Excel features.
  4. Peer Feedback: Opportunities to share and critique visualisations with fellow students to improve skills.
  5. Final Project: A project where students will create a set of visuals based on a health data scenario and provide an accompanying analysis.

Course Content:

Week 1:

  • Introduction to Data Visualisation: Principles of effective visual storytelling
  • Creating Basic Visualisations: Column, bar, and line charts for health data
  • Customising Charts: Labels, axes, and colour schemes to enhance clarity
  • Working with PivotTables and PivotCharts to analyse and visualise complex health data

Week 2:

  • Advanced Visualisation Techniques: Heatmaps, bubble charts, and sparklines
  • Interactive Visuals: Using slicers, drop-down lists, and dynamic charts in Excel
  • Data Representation in Healthcare: Designing visuals for clinical and scientific data
  • Best Practices for Presentation: Making your visuals presentation-ready for healthcare professionals
  • Final Project: A comprehensive task where students will create a dashboard or set of visualisations based on a health data case study

Assessment:

  • Hands-on Exercises: Regular tasks to create and customise different types of charts and visuals.
  • Final Project: A final visualisation project where students apply their learning to create a series of health data visualisations, demonstrating their ability to communicate data effectively.

A certificate of competency will be awarded upon successful completion, provided that attendance and the final project meet the required criteria.


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