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
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Programa especializado: Statistics with PythonUniversidad de MíchiganAcerca de este Curso
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Fechas límite flexibles
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Certificado para compartir
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100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 3 en
Nivel principiante
High school algebra
Aprox. 21 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
Qué aprenderás
Properly identify various data types and understand the different uses for each
Create data visualizations and numerical summaries with Python
Communicate statistical ideas clearly and concisely to a broad audience
Identify appropriate analytic techniques for probability and non-probability samples
Habilidades que obtendrás
- Statistics
- Data Analysis
- Python Programming
- Data Visualization (DataViz)
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
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
Nivel principiante
High school algebra
Aprox. 21 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), 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), 8 lecturas, 2 cuestionarios
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
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
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), 8 lecturas, 2 cuestionarios
Reseñas
- 5 stars76,04 %
- 4 stars18,58 %
- 3 stars3,62 %
- 2 stars0,85 %
- 1 star0,89 %
Principales reseñas sobre UNDERSTANDING AND VISUALIZING DATA WITH PYTHON
por LV28 de sep. de 2021
I've learned so much about the Python programming as well as general statistical skills. This course also lead me to change my initial university's major from Finance to Data Science.
por SV24 de ago. de 2020
The course is very well structured. Teaching and links to related articles help us understand the concepts better. Jupyter notebook based python learning is very comfortable and easy to use.
por FG3 de abr. de 2019
Excellent introductory course to statistics. Great use of NHANES dataset to demonstrate techniques on real dataset. I would appreciate a more demanding project at the course end.
por VM19 de ene. de 2021
This was a quick way of understanding the basics. I liked how detailed and basic the learning instructions were. Anyone, even those without a statistics background can begin from here
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.
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