edX
edXComputational Thinking and Big Data
42 studentsBEGINNEREnglish
Korshub's take
Computational Thinking for Data Science
Computational thinking—breaking problems into pieces, spotting patterns, abstracting essence, designing algorithms—is a universal skill. In this beginner course, you'll apply it to data science: decomposing data challenges, cleaning and visualizing data, building predictive models.
Tools like R and Java put theory into practice. The course anchors in the Big Data MicroMasters program, making it ideal for aspiring data scientists and analysts who want to move from curiosity to competence in data-driven decision-making.
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.
What will you learn?
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 visualising 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.
Who should study this computational thinking course?
This course forms part of the Big Data MicroMasters Program, which provides existing or aspiring data scientists and business analysts with the the skills to make informed, data-driven business decisions.
Price
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