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.
Ce cours fait partie de la Spécialisation Science des données appliquée avec Python
Offert par
Spécialisation Science des données appliquée avec PythonUniversité du MichiganÀ propos de ce cours
Ce que vous allez apprendre
Describe what makes a good or bad visualization
Understand best practices for creating basic charts
Identify the functions that are best for particular problems
Create a visualization using matplotlb
Compétences que vous acquerrez
- Python Programming
- Data Virtualization
- Data Visualization (DataViz)
- Matplotlib
Offert par
Université du Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Programme de cours : ce que vous apprendrez dans ce cours
Module 1: Principles of Information Visualization
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.
Module 2: Basic Charting
In this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.
Module 3: Charting Fundamentals
In this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.
Module 4: Applied Visualizations
In this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.
Avis
- 5 stars66,78 %
- 4 stars23,48 %
- 3 stars6,27 %
- 2 stars1,98 %
- 1 star1,46 %
Meilleurs avis pour APPLIED PLOTTING, CHARTING & DATA REPRESENTATION IN PYTHON
Beautifully designed course to grasp and utilize the knowledge gained. Also the assignments are meant to utilize real world data and practical solutions to it! wonder course, highly recommended!
I thought this was a really good introduction to matplotlib and some of the things you can do with it. The final project we got to apply what we'd learned to real data, which was a lot of fun.
Week 1 is a little bit theory and boring for me because that doesn't interest me but week 2 and week 4 is amazing. Especially week 4 assignment is too good. Overall the course is worth learning.
It was a great learning experience as an individual is forced to explore all the official documentations of plotting and charting.The assignments were also very versatile .Loved the course!
À propos du Spécialisation Science des données appliquée avec Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
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