|Introduction to R and Statistics in the Life Sciences||16.11.2023 09:30 - 13:30 (Do)||Online||Dr. Sapna Sharma|
|Introduction to R and Statistics in the Life Sciences||17.11.2023 09:30 - 13:30 (Fr)||Online||Dr. Sapna Sharma|
In this hands-on R course students with multidisciplinary backgrounds will learn about different data structures, data cleaning techniques, data visualization and data analysis using statistical methods. The course is designed for two half a day workshops and following a few Q&A sessions.
On Day 1:
Introduction to R and Rstudio
· General introduction to R and its integrated development environment, RStudio.
· Basic R Syntax: includes data types, variables, and basic operations.
· Students will learn how to load data into R, import data from various file formats (e.g., CSV, Excel), and perform simple data manipulations.
Introduction to R Packages and Documentation:
· Participants will learn about different sources such as CRAN or Bioconductor s, how to install and load packages.
· Emphasize the importance of R documentation. By this students will learn how to access package documentation and learn more functionalities that a package can offer apart from the default ones.
Data Visualization Packages and Techniques:
· Hands-on how to install these packages from CRAN or Bioconductor sources, for data visualization.
· Starting with very basic plotting functions in R to create simple visualizations like scatter plots, bar charts, and histograms.
· More advanced techniques with ggplot2, which offers customization options for creating high quality publication-quality plots.
· Introducing summary statistics, hypothesis testing methods.
· Simple and multiple linear regression to understand the relationship between dependent and independent variables.
· Analysis of Variance (ANOVA) statistical technique for comparing means across multiple groups of experiments.
· Clustering techniques
· Assign data visualization and analysis tasks on a given test dataset that is suitable for Engineering or Life Sciences students.
· Encouraging to explore and solve problems independently under my support and guidance as required.
Collaboration and Sharing under Ethical/GDPR Considerations::
· Interactive nature of course aims to encourage collaboration among students and allow them to learn from each other’s experiences and techniques.
· Discuss ethical considerations when working with sensitive data.
Assessment and Feedback:
· Evaluate students' progress through assignments, quizzes, and projects.
· Provide constructive feedback to help them improve their data visualization skills.
The main learning outcome of this hands-on course is to make non-computational students fearless about handling and dealing with data. By following the above steps and incorporating hands-on practice and real-life data examples, students become proficient in data visualization and analysis using R and its packages. This course empowers students to apply these skills effectively in their Engineering and Life Sciences studies.
The course will be held online please do consider having online tools available and functional such as,
- Software Zoom
- A stable internet connection is absolutely necessary.
- The course will be interactive so please check before hand on that Zoom functions, like microphone, camera and share screen.
- To avoid breaking down IT would anyway ask you to shut down the camera and the microphone.
- In some cases, students will be asked to allow me access to their screens to help them with troubleshooting.