Acerca de este Curso
4.5
2,119 calificaciones
388 revisiones
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Nivel intermedio

Nivel intermedio

Horas para completar

Aprox. 17 horas para completar

Sugerido: 5 hours/week...
Idiomas disponibles

Inglés (English)

Subtítulos: Inglés (English), Coreano

Qué aprenderás

  • Check

    Create a visualization using matplotlb

  • Check

    Describe what makes a good or bad visualization

  • Check

    Identify the functions that are best for particular problems

  • Check

    Understand best practices for creating basic charts

Habilidades que obtendrás

Python ProgrammingData VirtualizationData Visualization (DataViz)Matplotlib
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Nivel intermedio

Nivel intermedio

Horas para completar

Aprox. 17 horas para completar

Sugerido: 5 hours/week...
Idiomas disponibles

Inglés (English)

Subtítulos: Inglés (English), Coreano

Programa - Qué aprenderás en este curso

Semana
1
Horas para completar
5 horas para completar

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. ...
Reading
7 videos (Total 37 minutos), 6 readings, 2 quizzes
Video7 videos
About the Professor: Christopher Brooks1m
Tools for Thinking about Design (Alberto Cairo)8m
Graphical heuristics: Data-ink ratio (Edward Tufte)4m
Graphical heuristics: Chart junk (Edward Tufte)5m
Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)3m
The Truthful Art (Alberto Cairo)8m
Reading6 lecturas
Syllabus10m
Help us learn more about you!10m
Notice for Coursera Learners: Assignment Submission10m
Dark Horse Analytics (Optional)10m
Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts30m
Graphics Lies, Misleading Visuals10m
Semana
2
Horas para completar
7 horas para completar

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....
Reading
7 videos (Total 42 minutos), 2 readings, 1 quiz
Video7 videos
Matplotlib Architecture6m
Basic Plotting with Matplotlib7m
Scatterplots8m
Line Plots8m
Bar Charts4m
Dejunkifying a Plot3m
Reading2 lecturas
Matplotlib30m
Ten Simple Rules for Better Figures30m
Semana
3
Horas para completar
8 horas para completar

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....
Reading
6 videos (Total 39 minutos), 2 readings, 2 quizzes
Video6 videos
Histograms9m
Box Plots7m
Heatmaps3m
Animation5m
Interactivity5m
Reading2 lecturas
Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)10m
Assignment Reading10m
Semana
4
Horas para completar
5 horas para completar

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....
Reading
3 videos (Total 18 minutos), 2 readings, 1 quiz
Video3 videos
Seaborn8m
Becoming an Independent Data Scientist1m
Reading2 lecturas
Spurious Correlations10m
Post-course Survey10m
4.5
388 revisionesChevron Right
Dirección de la carrera

44%

comenzó una nueva carrera después de completar estos cursos
Beneficio de la carrera

40%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones

por SBNov 3rd 2017

Loved the course! This course teaches you details about matplotlib and enables you to produce beautiful and accurate graphs.. Assignments are challanging, and helps to build a solid foundation.

por MLJun 28th 2017

Good course to learned matplotlib and other Graphs libraries, but the course goes further than Python and also encourages the studies to create more meaningful and beautiful Graphic views.

Acerca de University of 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....

Acerca del programa especializado Applied Data Science with 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. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate....
Applied Data Science with Python

Preguntas Frecuentes

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