Critical Thinking, Innovation, Research and Development, Commercialization
Computational Thinking and Big Data
Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets.
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.
Estimated 10 weeks
8-10 hours per week
Self-paced
Progress at your own speed
Show More 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. The course will also explain mathematical representations, probabilistic and statistical models, dimension reduction and Bayesian models. You will use tools such as R and Java data processing libraries in associated language environments. Show Less
What you’ll learn
- 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
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.
Show More Section 3: Manipulating and joining data Section 4: Transforming data and dimension reduction Section 5: Summarising data Section 6: Introduction to Java Section 7: Graphs Section 8: Probability Section 9: Hashing Section 10: Bringing it all together Show Less
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.
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.
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.
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.
Use complex data structures; Implement your own data structures to organise data; Explain the differences between classes and objects; Motivate object-orientation.
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..
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.
Use complex data structures; Implement your own data structures to organise data; Explain the differences between classes and objects; Motivate object-orientation.
Understand the context of big data in programming.
At a glance
- Institution: AdelaideX
- Subject:Computer Science
- Level: Intermediate
- Prerequisites: None
- Langauge: English
- Video Transcript: English
- Associated programs:
MicroMasters® Program in Big Data
About the instructors
Lewis Mitchell
Lecturer in Applied Mathematics at University of Adelaide
About the instructors
Markus Wagner
Senior Lecturer, School of Computer Science at University of Adelaide
Simon Tuke
Lecturer in Statistics at University of Adelaide
Frequently Asked Questions
Question: This course is self-paced, but is there a course end date? Show More Show Less
Answer: Yes.
Thesecondrelease of the course started on December 1, 2018 and ends on December 1, 2020.
The third release of the course starts on March 1, 2019 andends on December 1, 2020.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. Show More Show Less
Ways to take this course
Choose your path when you enroll
Price
Verified Track
$249 usd
Access to course materials
Unlimited
World Class institution and universities
edx suppport
Shareable certificate upon completion
Graded assignments and exams
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