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November 17, 2024
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This course is part of Statistics and Applied Data Analysis Specialization
Instructor: Charlie Nuttelman
5,974 already enrolled
Included with
(25 reviews)
Recommended experience
Beginner level
Basic math skills (e.g., how to perform basic calculations with a calculator); basic Microsoft Excel skills are recommended.
(25 reviews)
Recommended experience
Beginner level
Basic math skills (e.g., how to perform basic calculations with a calculator); basic Microsoft Excel skills are recommended.
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.
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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."
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.
3 videos6 readings2 assignments1 discussion prompt
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.
17 videos3 readings3 assignments1 discussion prompt
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.
11 videos3 readings3 assignments1 discussion prompt
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.
13 videos4 readings3 assignments1 discussion prompt
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.
14 videos4 readings4 assignments1 discussion prompt
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Reviewed on Feb 28, 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
Reviewed on Sep 17, 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.
Reviewed on Jun 9, 2024
It is a perfect introduction to statistics, especially for the learner without a background in Statistics.
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