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 course, “Statistics and Data Analysis with Excel, Part 2.”
Statistics and Data Analysis with Excel, Part 1
Statistics and Data Analysis with Excel, Part 1
Instructor: Charlie Nuttelman
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4.8
(21 reviews)
Beginner level
Recommended experience
16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
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Assessments
15 assignments
Taught in English
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There are 5 modules in this course
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.
What's included
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.
What's included
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.
What's included
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.
What's included
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.
What's included
14 videos4 readings4 assignments1 discussion prompt
Instructor
Instructor ratings
5.0 (10 ratings)Offered by
Recommended if you're interested in Probability and Statistics
University of Colorado Boulder
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