Computational Thinking and Big Data
offered by The University of Adelaide

Public University
1 Campus
Estd. 1874

Computational Thinking and Big Data
at
The University of Adelaide
Overview

Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets.

Duration	10 weeks
Total fee	Free
Mode of learning	Online
Difficulty level	Intermediate
Official Website	Explore Free Course
Course Level	UG Certificate

Computational Thinking and Big Data
at
The University of Adelaide
Highlights

Earn a Paid Certification after completion
Doubt support sessions available
Access to course materials

Computational Thinking and Big Data
at
The University of Adelaide
Course details

Skills you will learn

R Java Data Science Data structures Statistics Big Data

What are the course deliverables?

Understand and apply advanced core computational thinking concepts to large-scale data sets
Use industry-level tools for data preparation and visualisation, such as R and Java
Apply methods for data preparation to large data sets
Understand mathematical and statistical techniques for attracting information from large data sets and illuminating relationships between data sets

More about this course

Computational thinking is an invaluable skill that can be used across every industry, as it allows you to formulate a problem and express a solution in such a way that a computer can effectively carry it out.
In this course, part of the Big Data MicroMasters program, you will learn how to apply computational thinking in data science. You will learn core computational thinking concepts including decomposition, pattern recognition, abstraction, and algorithmic thinking.
You will also learn about data representation and analysis and the processes of cleaning, presenting, and visualizing data. You will develop skills in data-driven problem design and algorithms for big data.

Computational Thinking and Big Data
at
The University of Adelaide
Curriculum

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.

Computational Thinking and Big Data
at
The University of Adelaide
Faculty details

Lewis Mitchell

Lewis is a lecturer in applied mathematics at the University of Adelaide. His research focusses on large-scale methods for extracting useful information from online social networks, and on statistical techniques for inference and prediction using these data.

Markus Wagner

Markus is a research-focussed lecturer in the School of Computer Science at the University of Adelaide. He is passionate about teaching and research, working actively on a range of projects from foundational courses to complex software engineering.