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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Statistics with Python
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Spezialisierung Statistics with PythonUniversity of MichiganÜber diesen Kurs
128.711 kürzliche Aufrufe
Flexible Fristen
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Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 1 von 3 in
Stufe „Anfänger“
High school algebra
Ca. 21 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Englisch, Spanisch
Was Sie lernen werden
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
Kompetenzen, die Sie erwerben
- Statistics
- Data Analysis
- Python Programming
- Data Visualization (DataViz)
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 1 von 3 in
Stufe „Anfänger“
High school algebra
Ca. 21 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Englisch, Spanisch
von
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
5 Stunden zum Abschließen
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 Stunden zum Abschließen
11 Videos (Gesamt 114 min), 8 Lektüren, 2 Quiz
5 Stunden zum Abschließen
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 Stunden zum Abschließen
8 Videos (Gesamt 92 min), 2 Lektüren, 3 Quiz
5 Stunden zum Abschließen
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 Stunden zum Abschließen
7 Videos (Gesamt 56 min), 3 Lektüren, 3 Quiz
6 Stunden zum Abschließen
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 Stunden zum Abschließen
15 Videos (Gesamt 223 min), 8 Lektüren, 2 Quiz
Bewertungen
- 5 stars75,91 %
- 4 stars18,61 %
- 3 stars3,71 %
- 2 stars0,83 %
- 1 star0,91 %
Top-Bewertungen von UNDERSTANDING AND VISUALIZING DATA WITH PYTHON
von VM19. Jan. 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
von DZ7. Apr. 2021
The material was explained thoroughly. It gave me the confidence to apply the knowledge in my own field of research and to explore new methods of visualization in the seaborn package.
von VV2. Aug. 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 :)
von DT27. Mai 2020
This course is very good for the people who are not from programming background as everything related to the concepts is very well explained (with programming support) throughout the course
Über den Spezialisierung 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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