Course description
Computational Thinking and Big Data
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.
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.
Upcoming start dates
Suitability - Who should attend?
Prerequisites
None
Outcome / Qualification etc.
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
Training Course Content
- Data in R
- Visualising relationships
- Manipulating and joining data
- Transforming data and dimension reduction
- Summarising data
- Introduction to Java
- Graphs
- Probability
- Hashing
- Bringing it all together
Course delivery details
This course is offered through University of Adelaide, a partner institute of EdX.
8-10 hours per week
Expenses
- Verified Track -$249
- Audit Track - Free