Applied Plotting, Charting & Data Representation in Python

Start Date: 11/29/2020

Course Type: Common Course

Course Link:

About Course

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib 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, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, 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.

Course Syllabus

In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.

Coursera Plus banner featuring three learners and university partner logos

Course Introduction

Applied Plotting, Charting & Data Representation in Python This course covers the fundamental techniques for data visualization and charting over a period of 4 weeks. We will cover the basics of Approximation, Overfitting, and Colorful charts. We will also discuss the various cartography techniques and their pros and cons. We’ll also cover the basics of Data Visualization which will allow us to create realistic-looking charts using a wide range of charting techniques.Approximation Overfitting Colorful Charting Approaches to Analyzing Data The course focuses on the main methods used to analyze data for different statistical disciplines, including probability, statistical inference, and modeling. It also covers techniques to improve analysis techniques, especially in the areas of randomization, control, and inference. All of these techniques are essential to the proper and efficient application of data analysis methods in practice. In this course, you will learn the following about methods of data analysis: - The different statistical methods available to analysts - The different types of statistical analysis - The basic concepts of the different statistical methods - The use of statistical modeling in data analysis - The use of randomization in data analysis - The use of control in data analysis - The use of inference in data analysis This course should be taken after: - Introduction to Statistics and Data Analysis, by taking both Honors and Specialization courses - Introduction to Probability and

Course Tag

Python Programming Data Virtualization Data Visualization (DataViz) Matplotlib

Related Wiki Topic

Article Example
Charting application A charting application is a computer program that is used to create a graphical representation (a chart) based on some non-graphical data that is entered by a user, most often through a spreadsheet application, but also through a dedicated specific scientific application (such as through a symbolic mathematics computing system, or a proprietary data collection application), or using an online spreadsheet service.
Data Applied Data Applied is a software vendor headquartered in Washington. Founded by a group of former Microsoft employees, the company specializes in data mining, data visualization, and business intelligence environments.
Network Data Representation Network Data Representation (NDR) is an implementation of the presentation layer in the OSI model.
Plotting room Telephone lines also ran from the plotting room to the guns and were used to relay firing data. Other devices, like range correction boards or deflection boards, were used in the plotting room to calculate corrected firing data or to adjust range and azimuth after spotters in remote observing stations had seen where prior shots had fallen.
External Data Representation External Data Representation (XDR) is a standard data serialization format, for uses such as computer network protocols. It allows data to be transferred between different kinds of computer systems. Converting from the local representation to XDR is called "encoding". Converting from XDR to the local representation is called "decoding". XDR is implemented as a software library of functions which is portable between different operating systems and is also independent of the transport layer.
Plotting room A plotting room was connected by telephone lines (and sometimes by radio) to base end stations (at left, top) that observed the locations of enemy ships and sent data to plotting room soldiers who used equipment like a plotting board to calculate where the guns should be pointed and when they should be fired.
Data Applied Data Applied implements a collection of visualization tools and algorithms for data analysis and data mining. The product supports several types of analytical tasks, including visual reporting, tree maps, time series forecasting, correlation analysis, outlier detection, decision trees, association rules, clustering, and self-organizing maps.
Representation term A representation term is a word, or a combination of words, that semantically represent the data type (value domain) of a data element. A representation term is commonly referred to as a "class word" by those familiar with data dictionaries. ISO/IEC 11179-5:2005 defines "representation term" as a "designation of an instance of a representation class" As used in ISO/IEC 11179, the representation term is that part of a data element name that provides a semantic pointer to the underlying data type. A "Representation class" is a class of representations. This "representation class" provides a way to classify or group data elements.
Plotting board A plotting board was a mechanical device used by the U.S. Coast Artillery to track the observed course of a target (typically a moving ship), project its future position, and derive the uncorrected data on azimuth (or direction) and range needed to direct the fire of the guns of a battery to hit that target. Plotting boards of this sort were first employed by the Coast Artillery around 1905, and were the primary means of calculating firing data until WW2.
Common Data Representation Common Data Representation (CDR) is used to represent structured or primitive data types passed as arguments or results during remote invocations on Common Object Request Broker Architecture (CORBA) distributed objects.
Common Data Representation It enables clients and servers written in different programming languages to work together. For example, it translates little-endian to big-endian. Assumes prior agreement on type, so no information is given with data representation in messages.
Representation term A value domain expresses the set of allowed values for a data element. The representation term (and typically the corresponding data type term) comprise a taxonomy for the value domains within a data set. This taxonomy is the representation class. Thus the representation term can be used to control proliferation of value domains by ensuring equivalent value domains use the same representation term.
Descartes (plotting tool) descartes is a platform-independent image, data, and function plotter with underlying Python scripting in the background. Its source code is released under the GNU GPL licence.
Applied Data Research Applied Data Research (ADR) was a large software vendor from the 1960s until the mid-1980s. ADR is often described as "the first independent software vendor".
Representation term A "Representation Term" may be thought of as an attribute of a data element in a metadata registry that classifies the data element according to the type of data stored in the data element.
Climate Data Analysis Tool The Climate Data Analysis Tool (CDAT) is plotting software used in atmospheric sciences and climatology.
Applied Digital Data Systems Applied Digital Data Systems (ADDS) was a supplier of video display computer terminals, founded in 1969 by Leeam Lowin and William J. Catacosinos. Lowin simultaneously founded Solid State Data Sciences (SSDS). SSDS was one of the first developers of the MOS/LSI integrated circuits that were key to ADDS's product line.
Comparison of Python based data storage "This is a review of Python-based data-stores and databases, with some history of an active user base."
Data discovery Data discovery is a type of business intelligence in that they both provide the end-user with an application that visualizes data. Traditional BI covered dashboards, static and parameterized reports, and pivot tables. Visualization of data in traditional BI incorporated standard charting, key performance indicators, and limited graphical representation and interactivity. BI is undergoing transformation in capabilities it offers, with a focus on end-user data analysis and discovery, access to larger volumes of data and an ability to create high fidelity presentations of information.
Python Paste The WSGI standard is an interface that allows applications to use Python code to handle HTTP requests. A WSGI application is passed a Python representation of an HTTP request by an application, and returns content which will normally eventually be rendered by a web browser. A common use for this is when a web server serves content created by Python code.