Об этом курсе: 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.
Для кого этот курс: 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 and/or a tutorial of the matplotlib system. 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.
Преподаватели: Christopher Brooks
| Основные сведения | Курс 2 из 5 — Applied Data Science with Python Specialization |
| Уровень | Учащийся среднего уровня |
| Язык | English, Субтитры: Korean |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Reading: Syllabus
- Reading: Help us learn more about you!
- Видео: About the Professor: Christopher Brooks
- Видео: Tools for Thinking about Design (Alberto Cairo)
- Reading: Notice for Coursera Learners: Assignment Submission
- LTI Item: Hands-on Visualization Wheel
- Видео: Graphical heuristics: Data-ink ratio (Edward Tufte)
- Reading: Dark Horse Analytics (Optional)
- Видео: Graphical heuristics: Chart junk (Edward Tufte)
- Reading: Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts
- Видео: Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)
- Видео: The Truthful Art (Alberto Cairo)
- Discussion Prompt: Must a visual be enlightening?
- Reading: Graphics Lies, Misleading Visuals
- Notebook: Module 2 Jupyter Notebook
- Видео: Matplotlib Architecture
- Reading: Matplotlib
- Reading: Ten Simple Rules for Better Figures
- Видео: Basic Plotting with Matplotlib
- Видео: Scatterplots
- Видео: Line Plots
- Видео: Bar Charts
- Видео: Dejunkifying a Plot
- Notebook: Plotting Weather Patterns
- Notebook: Module 3 Jupyter Notebook
- Видео: Histograms
- Reading: Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)
- Видео: Box Plots
- Видео: Heatmaps
- Видео: Animation
- Видео: Interactivity
- Notebook: Practice Assignment: Understanding Distributions Through Sampling
- Peer Review: Practice Assignment: Understanding Distributions Through Sampling
- Notebook: Building a Custom Visualization
- Reading: Assignment Reading
- Notebook: Module 4 Jupyter Notebook
- Видео: Seaborn
- Reading: Spurious Correlations
- Видео: Becoming an Independent Data Scientist
- Notebook: Project Description
- Reading: Post-course Survey
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Well structured course, gave a nice overview of the matplotlib tool and the basics for data visualization. I liked the pace more than the first course
pretty good
great
This course gives me an overview of data visualization, and I felt pretty accomplished with that goal. Data science is a broad subject, this course is a good place to start. It is an intermediate level course.