- From www.edx.org
Computational Thinking and Big Data
- Self-paced
- Free Access
- Fee-based Certificate
- 10 Sequences
- Introductive Level
- Subtitles in English
Course details
Syllabus
Section 1: Data in R
Identify the components of RStudio; Identify the subjects and types of variables in R; Summarise and visualise univariate data, including histograms and box plots.
Section 2: Visualising relationships
Produce plots in ggplot2 in R to illustrate the relationship between pairs of variables; Understand which type of plot to use for different variables; Identify methods to deal with large datasets.
Section 3: Manipulating and joining data
Organise different data types, including strings, dates and times; Filter subjects in a data frame, select individual variables, group data by variables and calculate summary statistics; Join separate dataframes into a single dataframe; Learn how to implement these methods in mapReduce.
Section 4: Transforming data and dimension reduction
Transform data so that it is more appropriate for modelling; Use various methods to transform variables, including q-q plots and Box-Cox transformation, so that they are distributed normally Reduce the number of variables using PCA; Learn how to implement these techniques into modelling data with linear models.
Section 5: Summarising data
Estimate model parameters, both point and interval estimates; Differentiate between the statistical concepts or parameters and statistics; Use statistical summaries to infer population characteristics; Utilise strings; Learn about k-mers in genomics and their relationship to perfect hash functions as an example of text manipulation.
Section 6: Introduction to Java
Use complex data structures; Implement your own data structures to organise data; Explain the differences between classes and objects; Motivate object-orientation.
Section 7: Graphs
Encode directed and undirected graphs in different data structures, such as matrices and adjacency lists; Execute basic algorithms, such as depth-first search and breadth-first search.
Section 8: Probability
Determine the probability of events occurring when the probability distribution is discrete; How to approximate.
Section 9: Hashing
Apply hash functions on basic data structures in Java; Implement your own hash functions and execute, these as well as built-in ones; Differentiate good from bad hash functions based on the concept of collisions.
Section 10: Bringing it all together
Understand the context of big data in programming.
Prerequisite
Instructors
Lewis Mitchell
Lecturer in Applied Mathematics
University of Adelaide
Markus Wagner
Senior Lecturer, School of Computer Science
University of Adelaide
Simon Tuke
Lecturer in Statistics
University of Adelaide
Gavin Meredith
Research Associate, School of Computer Science
University of Adelaide
Ian Knight
Lecturer, School of Computer Science
University of Adelaide
Editor
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