AdelaideX: Computational Thinking and Big Data
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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.
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.
At a Glance:
Institution: AdelaideX
Subject: Computer Science
Level: Introductory
Prerequisites:
None
Language: English
Video Transcript: English
Associated programs:
MicroMasters® Program in Big Data
Associated skills:Computational Thinking, R (Programming Language), Java (Programming Language), Data Processing, Data Science, Big Data, Pattern Recognition, Algorithms, Statistical Modeling, Dimensionality Reduction
What You’ll Learn:
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.
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.
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