Designed for students with no prior statistics knowledge, this course will provide a foundation for further study in data science, data analytics, or machine learning. Topics include descriptive statistics, probability, and discrete and continuous probability distributions. Assignments are conducted in Microsoft Excel (Windows or Mac versions). Designed to be taken with the follow-up courses, “Statistics and Data Analysis with Excel, Part 2" and "Statistics and Data Analysis with R". All three courses make up the specialization "Statistics and Applied Data Analysis."
Statistics and Data Analysis with Excel, Part 1
Ce cours fait partie de Spécialisation Statistics and Applied Data Analysis
Instructeur : Charlie Nuttelman
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(23 avis)
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Ce que vous apprendrez
Calculate descriptive statistics (traditional and robust estimators).
Understand probability and apply probability rules.
Utilize statistical functions in Microsoft Excel.
Visualize univariate and bivariate data in Microsoft Excel.
Compétences que vous acquerrez
- Catégorie : Probability And Statistics
- Catégorie : Parametric Statistics
- Catégorie : Microsoft Excel
- Catégorie : Statistical Graphics
- Catégorie : Excel Formulas
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Il y a 5 modules dans ce cours
Welcome to the course! In this module, you will orient yourself to the course policies and will learn a few of the basics related to statistics.
Inclus
3 vidéos6 lectures2 devoirs1 sujet de discussion
During Week 2, you will learn how to calculate population and sample statistics as well as quartiles and percentiles. Data visualization is important in the field of statistics - you will learn all about histograms, which are used for presenting univariate data in graphical format, as well as scatter plots and column plots. You will learn how to visualize univariate data in a box plot, which is a nice technique for identifying outliers. Finally, you will learn how to clean and transform data and use robust estimators in data sets that are highly affected by outliers.
Inclus
17 vidéos3 lectures3 devoirs1 sujet de discussion
In Week 3, you will learn all about probability and counting techniques. A thorough understanding of probability is paramount for the study of statistics. There are several rules and axioms that govern probability, and you will explore these rules in several screencasts. Finally, you will learn about conditional probability, which is the foundation for Bayes' Theorem.
Inclus
11 vidéos3 lectures3 devoirs1 sujet de discussion
Week 4 focuses on discrete probability distributions, in which the random variable is constrained to discrete values. Discrete probability distributions allow statisticians to make probabilistic predictions related to discrete stochastic models. These distributions include the binomial, geometric, negative binomial, hypergeometric, multinomial, and Poisson distributions.
Inclus
13 vidéos4 lectures3 devoirs1 sujet de discussion
Building on what you learned about probability distributions in Week 4, you will explore continuous random variables and continuous probability distributions in Week 5. These distributions include the common normal distribution and standard normal distribution, but we'll also delve into the exponential distribution, gamma distribution, and others. These distributions allow us to make probabilistic predictions related to stochastic models.
Inclus
14 vidéos4 lectures4 devoirs1 sujet de discussion
Instructeur
Offert par
Recommandé si vous êtes intéressé(e) par Probability and Statistics
LearnQuest
Duke University
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23 avis
- 5 stars
91,30 %
- 4 stars
4,34 %
- 3 stars
0 %
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- 1 star
4,34 %
Affichage de 3 sur 23
Révisé le 28 févr. 2024
easily one of the best stats courses i have ever taken. The Hands on application really reinforces the lectures. Full semester in just a few hours. Thanks Professor Nuttelman
Révisé le 9 juin 2024
It is a perfect introduction to statistics, especially for the learner without a background in Statistics.
Révisé le 17 sept. 2024
The Specialization is a very thorough learning about Statistics with many examples for us to understand the principles. Some modules are hard but fun.
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