About the course
This comprehensive training course empowers you to transform raw data into actionable insights and tangible value. Going beyond a simple overview, we delve into the essential methodologies for data exploration and equip you with powerful techniques for uncovering meaningful patterns and extracting key information.
At its core, this course leverages R, the leading statistical programming environment renowned for its extensive analytical capabilities and sophisticated graphical tools. You'll gain practical mastery of R as the de facto standard for data analysts across diverse fields.
By the end of this course, you will be equipped with the knowledge and practical skills to confidently explore, analyse, visualise, and extract valuable insights from your data using the power of R and fundamental machine learning techniques. This course is ideal for individuals seeking to leverage data for informed decision-making and gain a competitive edge in today's data-driven world.
Online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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- Mastering Data Exploration
- Visual Data Storytelling
- Data Restructuring for Analysis
- Fundamentals of Machine Learning - Supervised & Unsupervised
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This intensive training course is ideal for individuals and teams looking to leverage the power of R for deeper data understanding and predictive insights. It is particularly beneficial for:
Data Analysts: Professionals seeking to enhance their data exploration, manipulation, and visualisation skills using R to uncover key trends and patterns.
Scientists and Researchers: Individuals across various scientific disciplines who need a robust statistical programming environment for data analysis, graphical representation, and applying fundamental machine learning techniques to their research data.
Business Intelligence Professionals: BI specialists aiming to expand their toolkit with R's advanced analytical and visualisation capabilities to generate more insightful reports and dashboards.
Aspiring Data Scientists: Those looking to build a foundational understanding of data exploration, visualisation, and core machine learning concepts in R as a stepping stone towards a career in data science.
Marketing Analysts: Professionals who want to analyse customer data, identify segments, and potentially build basic predictive models for marketing campaign optimization using R.
Financial Analysts: Individuals in the finance sector who need to perform statistical analysis, create insightful visualisations, and explore basic predictive modeling on financial data.
Anyone working with data: Individuals in roles that require them to understand, interpret, and communicate insights from data, regardless of their specific job title.
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While this course is designed to be accessible to individuals from various backgrounds, a foundational understanding in the following areas will be beneficial for maximizing your learning experience:
Basic Computer Literacy: Familiarity with using a computer, navigating files and folders, and using common software applications is essential.
Elementary Mathematical Concepts: A basic understanding of concepts such as variables, equations, and basic arithmetic will be helpful when discussing statistical and machine learning techniques.
Logical Thinking and Problem-Solving: The ability to think logically and approach problems in a structured manner will aid in understanding programming concepts and analytical processes.
Familiarity with Data Concepts (Beneficial but not mandatory): While the course covers data exploration, prior exposure to basic data concepts (e.g., datasets, variables, different data types) can be advantageous but is not strictly required.
No Prior Programming Experience in R is Assumed: The course will introduce the R language from a foundational level, making it suitable for beginners to statistical programming.
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This R course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Summarising data numerically
Getting averages and other summary statistics
R-commands - mean, median, summary, apply, tapply, aggregate
Gaining some initial insights into your data.
Averages and other summary statistics
Tabulating data
Frequency (contingency) tables. Cross-tabulation
R commands - table, ftable, xtabs, colMeans, prop.table, margin.table, addmargins
How to transform data using contingency tables and cross-tabulation
Graphical summary of data
Summary graphs. Exploratory graphs
R commands - stripchart, dotchart, plot
Visualizing data
Graphs to help summarize data
Graphs to help explore data and view potential patterns
Unsupervised machine learning
Dissimilarity, Hierarchical clustering, K-means, Partitioning around Medoids, Fuzzy analysis
R commands - dist, hclust, cutree
Looking for clusters in your data
Methods of cluster analysis include hierarchical clustering, partitioning methods and agglomerative nesting
R commands - kmeans, pam, diana, agnes, fanny
Supervised machine learning
Regression analysis, Linear models, Curvilinear models, Non-Gaussian models
R commands - lm, coef, resid, plot, abline
Exploring relationships between factors in your data
Regression analysis includes linear and curvilinear models as well as non-Gaussian regression
R commands - glm
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https://www.r-project.org/ - the home of the R Project
https://posit.co/download/rstudio-desktop/ - hugely popular R editor
https://code.visualstudio.com/docs/languages/r - R extension for VS Code
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