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AdelaideX: Computational Thinking and Big Data

230,00 €

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

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230,00 €

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

Categories ,

Discover the Course

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, Algorithms, Data Processing, Java (Programming Language), Statistical Modeling, R (Programming Language), Dimensionality Reduction, Pattern Recognition, Data Science, Big Data

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

Course Overview

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