Data Literacy: Exploring and Describing Data

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Barton Poulson

5:18:08

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01.01-make better decisions with your data.mp4

01:31

02.01-the meaning of data fluency.mp4

06:40

02.02-data fluency is for everyone.mp4

02:27

02.03-data fluency in practice.mp4

06:58

02.04-making intuitive thinking explicit.mp4

05:14

02.05-thinking about causes.mp4

05:01

02.06-how to develop data fluency.mp4

04:26

02.07-data-driven decision-making.mp4

09:38

02.08-roi and the 8020 rule for data fluency.mp4

04:55

02.09-putting data in context.mp4

06:58

03.01-data ethics.mp4

07:39

03.02-use in-house data.mp4

04:37

03.03-use open data.mp4

06:48

03.04-gather new data.mp4

06:28

03.05-use third-party data.mp4

04:13

03.06-assess the quality of data.mp4

04:47

03.07-assess the generalizability of data.mp4

06:59

03.08-assess the meaning of data.mp4

04:05

03.09-assess the ambiguities in data.mp4

04:24

04.01-sort data.mp4

05:13

04.02-filter data.mp4

03:24

04.03-combine and split categories.mp4

06:02

04.04-code text.mp4

10:00

04.05-calculate sums and means.mp4

06:27

04.06-calculate rates.mp4

05:33

04.07-calculate ratios.mp4

04:10

04.08-adjust ratios in practice.mp4

03:42

05.01-visual primacy the importance of starting with pictures.mp4

07:52

05.02-bar charts.mp4

06:51

05.03-grouped bar charts.mp4

06:34

05.04-pie charts.mp4

08:04

05.05-dot plots.mp4

05:18

05.06-box plots.mp4

06:49

05.07-histograms.mp4

04:37

05.08-line charts.mp4

08:23

05.09-sparklines.mp4

05:02

05.10-scatterplots.mp4

08:22

05.11-data maps.mp4

03:29

06.01-numerical descriptions.mp4

01:24

06.02-describe measures of center.mp4

07:30

06.03-describe variability with the range and iqr.mp4

04:39

06.04-describe variability with the variance and standard deviation.mp4

07:10

06.05-rescale data with z-scores.mp4

03:08

06.06-interpret z-scores.mp4

05:19

06.07-describe group differences with effect sizes.mp4

07:53

06.08-predict scores with regression.mp4

07:29

06.09-describe associations with correlations.mp4

04:50

06.10-effect size for correlation and regression.mp4

04:37

06.11-exploring tables.mp4

07:43

07.01-basic probability.mp4

07:43

07.02-conditional probability.mp4

05:19

07.03-expected values.mp4

06:39

07.04-sampling variation.mp4

05:32

07.05-inference as describing populations.mp4

04:59

08.01-next steps and additional resources.mp4

06:34

Ex Files Data Fluency Exploring and Describing.zip

Description

Data analysis isn’t just for specialists who need to make sense of massive datasets. Decision-makers in every industry can benefit from a basic understanding of the goals and concepts of applied data analysis. In this course, Barton Poulson focuses on the fundamentals of data fluency, or the ability to work with data to extract insights and determine your next steps. Barton shows how exploring data with graphs and describing data with statistics can help you reach your goals and make better decisions. Instead of focusing on particular tools, he concentrates on general procedures that can help you solve specific problems. Barton covers how to prepare and adapt data, explore it visually, and use statistical methods to describe it. He goes in depth on probability and interference and also touches on data ethics and explainable AI.

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Data Science

Barton Poulson

Instructor's Courses

Founder of datalab.cc, author for LinkedIn Learning, associate professor of psychology at Utah Valley University. I teach people how to use data to find practical solutions to real-life problems. #DataIsForDoing

Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.