In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.
Este curso forma parte de Programa Especializado - Statistics with Python
ofrecido por
Programa Especializado - Statistics with PythonUniversidad de MíchiganAcerca de este Curso
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Resultados profesionales del estudiante
25%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 3 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel principiante
High school algebra
Aprox. 20 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (de Brasil), Coreano, Ruso (Russian), Inglés (English), Español (Spanish)
Habilidades que obtendrás
StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)
Resultados profesionales del estudiante
25%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 3 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel principiante
High school algebra
Aprox. 20 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (de Brasil), Coreano, Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Míchigan
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.
Programa - Qué aprenderás en este curso
5 horas para completar
WEEK 1 - INTRODUCTION TO DATA
In the first week of the course, we will review a course outline and discover the various concepts and objectives to be mastered in the weeks to come. You will get an introduction to the field of statistics and explore a variety of perspectives the field has to offer. We will identify numerous types of data that exist and observe where they can be found in everyday life. You will delve into basic Python functionality, along with an introduction to Jupyter Notebook. All of the course information on grading, prerequisites, and expectations are on the course syllabus and you can find more information on our Course Resources page.
5 horas para completar
11 videos (Total 114 minutos), 7 lecturas, 2 cuestionarios
11 videos
Understanding and Visualizing Data Guidelines3m
What is Statistics?9m
Interview: Perspectives on Statistics in Real Life28m
(Cool Stuff in) Data8m
Where Do Data Come From?12m
Variable Types5m
Study Design6m
Introduction to Jupyter Notebooks9m
Data Types in Python12m
Introduction to Libraries and Data Management13m
7 lecturas
Course Syllabus5m
Meet the Course Team!10m
About Our Datasets2m
Help Us Learn More About You!10m
Resource: This is Statistics10m
Let's Play with Data!10m
Data management and manipulation10m
2 ejercicios de práctica
Practice Quiz - Variable Types30m
Assessment: Different Data Types10m
5 horas para completar
WEEK 2 - UNIVARIATE DATA
In the second week of this course, we will be looking at graphical and numerical interpretations for one variable (univariate data). In particular, we will be creating and analyzing histograms, box plots, and numerical summaries of our data in order to give a basis of analysis for quantitative data and bar charts and pie charts for categorical data. A few key interpretations will be made about our numerical summaries such as mean, IQR, and standard deviation. An assessment is included at the end of the week concerning numerical summaries and interpretations of these summaries.
5 horas para completar
8 videos (Total 92 minutos), 2 lecturas, 3 cuestionarios
8 videos
Quantitative Data: Histograms12m
Quantitative Data: Numerical Summaries9m
Standard Score (Empirical Rule)7m
Quantitative Data: Boxplots6m
Demo: Interactive Histogram & Boxplot4m
Important Python Libraries21m
Tables, Histograms, Boxplots in Python25m
2 lecturas
What's Going on in This Graph?10m
Modern Infographics10m
3 ejercicios de práctica
Practice Quiz: Summarizing Graphs in Words15m
Assessment: Numerical Summaries10m
Python Assessment: Univariate Analysis10m
5 horas para completar
WEEK 3 - MULTIVARIATE DATA
In the third week of this course on looking at data, we’ll introduce key ideas for examining research questions that require looking at more than one variable. In particular, we will consider both numerically and visually how different variables interact, how summaries can appear deceiving if you don’t properly account for interactions, and differences between quantitative and categorical variables. This week’s assignment will consist of a writing assignment along with reviewing those of your peers.
5 horas para completar
7 videos (Total 56 minutos), 3 lecturas, 3 cuestionarios
7 videos
Looking at Associations with Multivariate Quantitative Data7m
Demo: Interactive Scatterplot2m
Introduction to Pizza Assignment2m
Multivariate Data Selection19m
Multivariate Distributions8m
Unit Testing5m
3 lecturas
Pitfall: Simpson's Paradox10m
Modern Ways to Visualize Data10m
Pizza Study Design Assignment Instructions10m
2 ejercicios de práctica
Practice Quiz: Multivariate Data10m
Python Assessment: Multivariate Analysis15m
6 horas para completar
WEEK 4 - POPULATIONS AND SAMPLES
In this week, you’ll spend more time thinking about where data come from. The highest-quality statistical analyses of data will always incorporate information about the process used to generate the data, or features of the data collection design. You’ll be exposed to important concepts related to sampling from larger populations, including probability and non-probability sampling, and how we can make inferences about larger populations based on well-designed samples. You’ll also learn about the concept of a sampling distribution, and how estimation of the variance of that distribution plays a critical role in making statements about populations. Finally, you’ll learn about the importance of reading the documentation for a given data set; a key step in looking at data is also looking at the available documentation for that data set, which describes how the data were generated.
6 horas para completar
15 videos (Total 223 minutos), 7 lecturas, 2 cuestionarios
15 videos
Probability Sampling: Part I10m
Probability Sampling: Part II15m
Non-Probability Sampling: Part I10m
Non-Probability Sampling: Part II9m
Sampling Variance & Sampling Distributions: Part I15m
Sampling Variance & Sampling Distributions: Part II7m
Demo: Interactive Sampling Distribution21m
Beyond Means: Sampling Distributions of Other Common Statistics10m
Making Population Inference Based on Only One Sample14m
Inference for Non-Probability Samples17m
Complex Samples23m
Sampling from a Biased Population15m
Randomness and Reproducibility14m
The Empirical Rule of Distribution18m
7 lecturas
Building on Visualization Concepts5m
Potential Pitfalls of Non-Probability Sampling: A Case Study10m
Resource: Seeing Theory10m
Article: Jerzy Neyman on Population Inference10m
Preventing Bad/Biased Samples10m
Optional: Deeper Dive Reference10m
Course Feedback10m
2 ejercicios de práctica
Assessment: Distinguishing Between Probability & Non-Probability Samples10m
Generating Random Data and Samples20m
Reseñas
Principales reseñas sobre UNDERSTANDING AND VISUALIZING DATA WITH PYTHON
por AT21 de may. de 2020
Excellent course materials, especially the videos, with content that is thoughtfully composed and carefully edited. Very good python training, great instructors, and overall great learning experience.
por PR3 de sep. de 2020
Very helpful course for newcomer in data science studies. Great in clearing fundamentals for descriptive statistics, use of python to get these insights,plotting. Overall provide good learning curve.
por VV2 de ago. de 2020
Great course to learn the basics! The supplementary material in Jupyter notebooks is extremely valuable. Really appreciate the PhD students who took the time to explain even the simplest of codes :)
por MC19 de abr. de 2020
Excellent high level introduction, would have like the assessment to be more challenging. The additional materials are just amazing for most of them. The notebooks to practice are also excellent.
Acerca de Programa especializado: Statistics with Python
This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
- es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
- Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
Is financial aid available?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.
¿Recibiré crédito universitario por completar el Curso?
Este Curso no otorga crédito universitario, pero algunas universidades pueden aceptar los Certificados del curso para obtener crédito. Consulta con tu institución para obtener más información. Los Títulos en línea y los Certificados Mastertrack™ de Coursera brindan la oportunidad de obtener créditos universitarios.
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