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Learn Statistics in Excel and RStudio. Learn statistics from ground zero then utilize Excel and RStudio for statistical calculations and data visualization.
Instructor: Charlie Nuttelman
Included with
(12 reviews)
Recommended experience
Beginner level
A basic math and logical thinking background as well as an understanding of Excel is important. Otherwise, basic computer navigation is required.
(12 reviews)
Recommended experience
Beginner level
A basic math and logical thinking background as well as an understanding of Excel is important. Otherwise, basic computer navigation is required.
Create mathematical regression models that can be used in a predictive manner.
Perform one-sample and two-sample statistical hypothesis tests.
Perform a variety of statistical tests, create regression models, and analyze data using Microsoft Excel as well as R (RStudio).
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A comprehensive understanding of statistics and data analysis is paramount for the fields of data science, data analytics, and machine learning. In the first course of this specialization, you will learn traditional and applied statistics (descriptive statistics, probability, and discrete and continuous probability distributions) from ground zero (i.e., beginner level).
The second course is all about inferential statistics and making decisions (sampling distributions, one- and two-sample hypothesis tests, analysis of variance) and creating predictive mathematical models (linear and nonlinear regression). Throughout both of the first two courses, you will learn how to visualize data and solve various statistical problems using Microsoft Excel.
In the final course of the specialization, you will use the statistical computing software R (using RStudio) for statistical hypothesis tests, data visualization, and analysis of variance (ANOVA).
Applied Learning Project
Parts 1 and 2 of the specialization have “guided” workshop quizzes during each week of the course for learners to work along with the instructor through relevant and exciting statistics related problems. In Part 3 of the course, assignments are in-application (i.e., in the programming language R) and are submitted online.
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.
Perform one- and two-sample hypothesis tests on the mean and variance to make statistical decisions.
Create and interpret predictive regression models (linear and multiple) from experimental data.
Use ANOVA (analysis of variance) to compare means of multiple samples.
Use statistical functions in RStudio to solve problems related to discrete and continuous probability distributions.
Create simple linear, polynomial, and multilinear regression models in RStudio and use those models to make predictions.
Perform one-sample and two-sample hypothesis tests and create confidence and prediction intervals on various statistics.
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Working approximately 5 hours a week, you should be able to complete the 3-course specialization within 4 months. If you double the time (10 hours per week), you should be able to complete the specialization in 2 months. If you still have more time (20 hours/week), then expect to complete the courses within a month or so.
A basic math and logical thinking background as well as an understanding of Excel is important. Otherwise, basic computer navigation is required.
Yes, it is strongly recommended that learners take the three courses in sequential order.
No. At this time, the University of Colorado Boulder does not offer a for-credit version of the Statistics and Applied Data Analysis Specialization.
Upon completing this specialization, you will be familiar with all of the common discrete and continous probability distributions and be able to calculate probabilities associated with those distributions in Excel and RStudio. You will be able to perform one-sample hypothesis tests (T-tests, for example) and will be able to perform comparison tests (comparing the means of two populations, for example) both in Excel and RStudio. Finally, you will be able to create mathematical regression models that relate single or multiple inputs to an output and you will be able to perform analysis of variance (ANOVA) in Excel and RStudio.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
Financial aid available,
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