Computational Thinking and Big Data at The University of Adelaide, Adelaide
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Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
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English | Self Study | Video and Text Based |
Courses and Certificate Fees
The Syllabus
- The pre-course survey
- Course outline
- CompX requirements
- Introduction to RStudio
- Dataframes
- Subjects and variables
- Categorical data
- Quantitative data
- Case Study: Jane Austen novels
- Scatter plots
- Visulising relationships using GGplot
- Describing relationships between different types of variables
- Interpreting plots
- Case Study: Visually exploring relationships between Project Gutenburg Books
- Filter
- Select
- Mutate
- Group by
- Summarise
- Join
- Case Study: Gapminder dataset
- Why Transform Data?
- Principal Component Analysis (PCA)
- Summarising the Advancement of Physics using TF-IDF
- Population, parameters, samples, and statistics
- Point estimates
- Interval estimates
- Counting k-mers genomes
- Section 5 Assignment: k-mers in a genome
- The coding environment
- Object-oriented programming
- Activity 10: Coding a zoo
- Case Study: Class.java.lang.Math
- Social networks as graphs,
- Graph basics,
- Graph algorithms,
- Graphs in Java
- Probability space,
- Random variables and expectations,
- Natural logarithm,
- Tail inequalities
- Hashing basics,
- Advanced hashing,
- Hashing in practice
- Major assignment 1,
- Major assignment 2,
- Course summary
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