Coursera - Statistics and Applied Data Analysis Specialization

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Free Download Coursera - Statistics and Applied Data Analysis Specialization
Released 12/2024
By Charlie Nuttelman
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 184 Lessons ( 25h 12m ) | Size: 4.12 GB

Learn Statistics in Excel and RStudio. Learn statistics from ground zero then utilize Excel and RStudio for statistical calculations and data visualization.
What you'll learn
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).
Skills you'll gain
Statistics
Data Analytics
Microsoft Excel
Data Analysis
RStudio
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.
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