# Managing, Describing, and Analyzing Data (Coursera)

##### Start Date
Nov 29th 2021
Course Auditing
Categories
Effort
Certification
Languages
Beginner level; no previous experience necessary.
Misc

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In this course, you will learn the basics of understanding the data you have and why correctly classifying data is the first step to making correct decisions. You will describe data both graphically and numerically using descriptive statistics and R software. You will learn four probability distributions commonly used in the analysis of data. You will analyze data sets using the appropriate probability distribution. Finally, you will learn the basics of sampling error, sampling distributions, and errors in decision-making.

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What You Will Learn

- Calculate descriptive statistics and create graphical representations using R software

- Solve problems and make decisions using probability distributions

- Explore the basics of sampling and sampling distributions with respect to statistical inference

- Classify types of data with scales of measurement

### Syllabus

WEEK 1

Data and Measurement

Upon completion of this module, students will be able to use R and R Studio to work with data and classify types of data using measurement scales.

WEEK 2

Describing Data Graphically

Upon completion of this module, students will be able to use R and R Studio to create visual representations of data.

WEEK 3

Describing Data Numerically

Upon completion of this module, students will be able to use R and R Studio to calculate descriptive statistics to describe location, spread and shape of data.

WEEK 4

Probability and Probability Distributions

Upon completion of this module, students will be able to apply the rules and conditions of probability and probability distributions to make decisions and solve problems using R and R Studio.

WEEK 5

Sampling Distributions and Error

Upon completion of this module, students will be able to use R and R Studio to characterize sampling and sampling distributions with respect to statistical inference.

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