01. Introduction to Computational Thinking and Data Science

“Computational thinking involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science. Computational thinking includes a range of mental tools that reflect the breadth of the field of computer science.” Jeannette Wing, “Computational Thinking”

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02. Data

DATA!!  You think you have it, but do you really?  When you just step into the data world, you must learn the different concepts and terms that relate to data. What is “data”, exactly, to a data scientist?

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03. Data Analysis Software

To process and analyze data, we use software.  It is important to understand the difference between algorithms and software, the importance of open source software, and the principles and mechanics of how non-programmers use software.

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04. Multi-­step data analysis as workflows

A “workflow” usually describes how activities are organized to achieve a common goal.  What is a computational workflow, and how does it help non-programmers analyze data?

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06. Data Analysis Tasks (1)

Introduction, Classification. There are many different kinds of data analytics tasks.  Are you looking to characterize objects, or just find repeating patterns on your data?   Classification is a common data analytics task, which involves assigning a category (i.e., a class) for a new instance.

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07. Data analysis tasks (II)

Pattern Discovery, Clustering, Simulation. Discovering patterns in data, clustering objects, and simulating scenarios for prediction are all data analytics tasks.

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08. Data analysis tasks (III)

Causality Causal models of data can help understand complex phenomena beyond simple correlation.  Causal discovery is one of the most advanced and challenging data analytics tasks.

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09. Data Pre-Processing

The majority of the time in a data science project is spent preparing the data for processing, by cleaning, reformatting, and reorganizing data. There are many possible steps that you can take to get data ready for processing.

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10. The Data Lifecycle

You should understand how data is managed and all the aspects of the data lifecycle from collection to presentation.

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12. Analyzing different kinds of data (I)

Analyzing Text Data Text is an important source of information.  You may want to analyze text in customer reviews, social media, Web sites, etc. How to process text data?

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