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. 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.

• 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

• Yes. Candidates interested in pursuing the Big Data program are advised to complete ProgramX - Programming for Data Science before commencing this course. Visit the MicroMasters Big Data Program page for more information.