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  • Author: buby2009
  • Date: 6-06-2017, 21:59
6-06-2017, 21:59

Free Download Applied Descriptionting, Charting & Data Representation in Python

Category: Tutorials / Programming

Applied Descriptionting, Charting & Data Representation in Python
----- Coursera Video Training -----
Duration: 4 hours | Video: h264, 960x540 | Audio: AAC, 44100 Hz, 2 Ch | 280 MB
Genre: eLearning | Language: English | Level Intermediate

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matDescriptionlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matDescriptionlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will describe the gamut of functionality available in matDescriptionlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data.

This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

Who is this class for: This course is part of "Applied Data Science with Python" and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
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