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Computational Thinking and Big Data

Computational Thinking and Big Data

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8.3/10 (Our Score)
Product is rated as #36 in category 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.

Instructor Details

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. He works on building tools for real-time prediction of events like disease outbreaks, elections, and civil unrest.

Specification: Computational Thinking and Big Data

Duration 90 hours
Year 2020
Level Beginner
Certificate Yes
Quizzes No

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    Rahmawati

    I just begin the course and have not strategic yet.. So very excited with this topic since it relates to science education

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