Statistics and Applied Data Analysis

University of Colorado Boulder via Coursera Specialization

Overview

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).

Syllabus

Course 1: Statistics and Data Analysis with Excel, Part 1
- Offered by University of Colorado Boulder. Designed for students with no prior statistics knowledge, this course will provide a foundation ... Enroll for free.

Course 2: Statistics and Data Analysis with Excel, Part 2
- Offered by University of Colorado Boulder. This course is meant to be a direct continuation of "Statistics and Data Analysis with Excel, ... Enroll for free.

Course 3: Statistics and Data Analysis with R
- Offered by University of Colorado Boulder. This course is the third course in a 3-part specialization entitled "Statistics and Applied Data ... Enroll for free.

Courses

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16 hours 40 minutes
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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."
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21 hours 20 minutes
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This course is meant to be a direct continuation of "Statistics and Data Analysis with Excel, Part 1." Therefore, it is not recommended to take Part 2 unless you've also taken Part 1. Building on the topics learned in Part 1 of the course (probability, probability mass and density functions, the normal and standard normal distributions), this course dives into a more applied side of statistics. Topics in Part 2 include sampling distributions; one-sample hypothesis tests on the mean, variance, and binomial proportion; two-sample hypothesis tests (comparison of means, variances, and binomial proportions of samples drawn from two populations); simple (straight-line) regression; multilinear regression; and analysis of variance (ANOVA). Statistical techniques are taught with the help of Microsoft Excel, which is an intuitive software package that has many built-in functions and tools for statistical analysis. This course is the second course out of three that comprise the specialization "Statistics and Applied Data Analysis." Course 3 ("Statistics and Data Analysis with R") focuses on statistical analysis in the statistical software package RStudio.
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1 day 22 minutes
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This course is the third course in a 3-part specialization entitled "Statistics and Applied Data Analysis." The course is meant for those familiar with statistics but unfamiliar with the programming language R. The purpose of this course is to teach learners how to use the popular open-source (and thus, free) integrated development environment RStudio to perform basic and complex statistical calculations. After an introduction to basic calculations, vector, matrices, data frames, and how to import data from common file types (.xlsx, .csv, .txt), learners are taught how to solve probability and counting problems in R, followed by discrete and continuous probability distribution calculations, one-sample hypothesis tests, and two-sample hypothesis tests (comparisons). Finally, participants will learn how to create regression models in R and perform analysis of variance (ANOVA). One of the most beneficial aspect of the course are the programming assignments, which are completed online in the R programming language in Jupyter notebooks.