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
von
Spezialisierung Statistics with PythonUniversity of MichiganÜber diesen Kurs
393,792 kürzliche Aufrufe
Karriereergebnisse der Lernenden
25%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
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
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Anfänger“
High school algebra
Ca. 20 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch
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
StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)
Karriereergebnisse der Lernenden
25%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
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
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Anfänger“
High school algebra
Ca. 20 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch
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), 7 Lektüren, 2 Quiz
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 Lektüren
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 praktische Übungen
Practice Quiz - Variable Types30m
Assessment: Different Data Types10m
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
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 Lektüren
What's Going on in This Graph?10m
Modern Infographics10m
3 praktische Übungen
Practice Quiz: Summarizing Graphs in Words15m
Assessment: Numerical Summaries10m
Python Assessment: Univariate Analysis10m
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
7 Videos
Looking at Associations with Multivariate Quantitative Data7m
Demo: Interactive Scatterplot2m
Introduction to Pizza Assignment2m
Multivariate Data Selection19m
Multivariate Distributions8m
Unit Testing5m
3 Lektüren
Pitfall: Simpson's Paradox10m
Modern Ways to Visualize Data10m
Pizza Study Design Assignment Instructions10m
2 praktische Übungen
Practice Quiz: Multivariate Data10m
Python Assessment: Multivariate Analysis15m
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), 7 Lektüren, 2 Quiz
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 Lektüren
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 praktische Übungen
Assessment: Distinguishing Between Probability & Non-Probability Samples10m
Generating Random Data and Samples20m
Bewertungen
Top-Bewertungen von UNDERSTANDING AND VISUALIZING DATA WITH PYTHON
von AT21. Mai 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.
von MC19. Apr. 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.
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
von MR2. Juni 2020
Never have I come across a course half as interactive as this and it was a much needed confidence booster for a beginner like me. I look forward to completing the specialization : )
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.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse der Spezialisierung und Sie erhalten nach Abschluss aller Arbeiten ein Zertifikat. Ihr elektronisches Zertifikat wird zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
Wie erfolgen Rückerstattungen?
Wenn Sie ein Abonnement abgeschlossen haben, erhalten Sie eine 7-tägige, kostenlose Testversion, die Sie gebührenfrei wieder kündigen können. Danach gewähren wir keine Rückerstattungen mehr, aber Sie können Ihr Abonnement jederzeit kündigen. Lesen Sie unsere vollständige Rückerstattungsrichtlinie.
Ist finanzielle Unterstützung möglich?
Ja, Coursera bietet für Kursteilnehmer, die sich die Kursgebühr nicht leisten können, finanzielle Unterstützung an. Bewerben Sie sich dafür, indem Sie auf den Link für finanzielle Unterstützung links unter der Schaltfläche „Anmelden“ klicken. Sie werden zum Ausfüllen eines Antrags aufgefordert und werden bei Genehmigung benachrichtigt. Diesen Schritt müssen Sie für jeden Kurs der Spezialisierung ausführen, auch für das Abschlussprojekt. Mehr erfahren
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