Timeplan 2023 Vår - lærer Daniel Kumazawa Morais -
Daniel Kumazawa Morais
L1: Course introduction
1
Content
- Structure of the course
- Why/What is data science and bioinformatics?
- Invited speaker: Alf-Martin Søllund, BarentsWatch
L2: Data wrangling
2
Content
- What is the R language and Rstudio and its main components
- Most used packages for Data Sciences
- Data structures and classes in R
P1: Data wrangling
1
L3: Data visualisation
3
Contents
- Origins and rationale behind the Grammar of Graphics (ggplot)
- Suitable visualization for each type of data
- Best practices for data visualization
L4: Statistics for big data
4
Content
- Descriptive vs Inferential statistics
- Frequentist approach for hypothesis testing vs the Bayesian paradigm
- Linear Models in R
P2: Data visualisation
2
P3: Statistics for Big data
3
L5: Data modelling and interpretation
5
Contents
- Algorithms for Supervised and Unsupervised Learning
- Key concepts to evaluate the performance of a Machine Learning approach
P4: Data modelling and interpretation
4
L6:Turning data into actionable insights. Knowledge-base management
6
Content
- Using R language for business intelligence
- Invited speaker: Tara Zeynep Baris (HUB Ocean/Ocean Data Platform)
L7:Introduction to bioinformatics
7
Contents
- The role of bioinformatics in fisheries and aquaculture
- Bioinformatics and the future of aquaculture and fisheries management
- Sources of data for bioinformatics
P5:Introduction to bioinformatics
5
L8:Genetics data and database
8
Contents
- Examples of biological data
- What are the main biological databases
- How to use the main biological databases
P6: Genetics data and database
6
L11:The role of genetics data in fisheries, aquaculture, and conservation
11
Contents
- Genomics for aquaculture
- Metagenomics for aquaculture
- Integration of omics tools