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[FreeCoursesOnline.Me] Pluralsight - Interpreting Data Using Statistical Models With Python

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	f7e92dff8aa8f4f40672ad50ca158eec80b82689
Size:	416.5 MB in 42 files
Release Date(s):	Torrent Release: 08-02-2020 by user
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DESCRIPTION

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By Janani Ravi
Source https://www.pluralsight.com/courses/interpreting-data-using-statistical-models-python

This course covers techniques from inferential statistics, including hypothesis testing, t-tests, and Pearson’s chi-squared test, along with ANOVA, which is used to analyze effects across categorical variables and the interaction between variables.

Course info

Level: Beginner
Updated: Oct 29, 2019
Duration: 2h 46m

Description

Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and modeling tools, so what differs is how those models are applied to the data. Today, more than ever, it is really important that you know your data well. In this course, Interpreting Data using Statistical Models with Python you will gain the ability to go one step beyond visualizations and basic descriptive statistics, by harnessing the power of inferential statistics. First, you will learn how hypothesis testing, which is the foundation of inferential statistics, helps posit and test assumptions about data. Next, you will discover how the classic t-test can be used in a variety of common scenarios around estimating means. You will also learn about related tests such as the Z-test, Pearson’s Chi-squared test, Levene’s test and Welch’s t-test for dealing with populations that have unequal variances. Finally, you will round out your knowledge by using ANOVA, a powerful statistical technique used to measure statistical properties across different categories of data. When you’re finished with this course, you will have the skills and knowledge to use powerful techniques from hypothesis testing, including t-tests, ANOVA and regression tests in order to measure the strength of statistical relationships within your data.

About the author

Janani Ravi

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

Course Overview

Hi, my name is Janani Ravi, and welcome to this course on Interpreting Data Using Statistical Models with Python. A little about myself, I have a master’s degree in electrical engineering from Stanford, and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and modeling tools, so what differs is how these models are applied to the data. Today more than ever, it’s really important that you know your data well. In this course, you will gain the ability to go one step beyond visualizations and basic descriptive statistics by harnessing the power of inferential statistics. First, you will learn how hypothesis testing, which is the foundation of inferential statistics, helps posit and test assumptions about data. Next you will discover how the classic T-test can be used in a variety of common scenarios around estimating means. You will also learn about related tests such as the Z-test, Pearson’s chi-squared test, Levene’s test, and Welch’s t-test for dealing with populations that have unequal variances. Finally, you’ll round out your knowledge by using ANOVA, a powerful statistical technique used to measure statistical properties across different categories of data. When you’re finished with this course, you will have the skills and knowledge to use powerful techniques from hypothesis testing including T-tests, ANOVA, and regression tests in order to measure the strength of statistical relationships within your data.

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FILE LIST

Filename		Size
	0. Websites you may like/How you can help our Group!.txt	208 B
	01.Course Overview/01.01.Course Overview.mp4	11.1 MB
	02.Understanding Inferential Statistics/02.01.Module Overview.mp4	7.1 MB
	02.Understanding Inferential Statistics/02.02.Prerequisites and Course Outline.mp4	5.2 MB
	02.Understanding Inferential Statistics/02.03.Descriptive Statistics to Summarize Data.mp4	7.7 MB
	02.Understanding Inferential Statistics/02.04.Introducing Hypothesis Testing.mp4	9.1 MB
	02.Understanding Inferential Statistics/02.05.Lady Tasting Tea.mp4	9.1 MB
	02.Understanding Inferential Statistics/02.06.The Power, Alpha and p-value of a Statistical Test.mp4	4.5 MB
	02.Understanding Inferential Statistics/02.07.Introducing the t-test.mp4	7.4 MB
	02.Understanding Inferential Statistics/02.08.One Sample Location t-test and the Z-Test.mp4	5.6 MB
	02.Understanding Inferential Statistics/02.09.Other Types of t-tests.mp4	11.6 MB
	02.Understanding Inferential Statistics/02.10.One-way ANOVA.mp4	11.8 MB
	02.Understanding Inferential Statistics/02.11.Two-way ANOVA.mp4	4.6 MB
	02.Understanding Inferential Statistics/02.12.Pearson's Chi2 Test.mp4	8.1 MB
	02.Understanding Inferential Statistics/02.13.Module Summary.mp4	2.7 MB
	03.Performing Hypothesis Testing in Python/03.01.Module Overview.mp4	6.6 MB
	03.Performing Hypothesis Testing in Python/03.02.Demo Preparing Data for Hypothesis Testing.mp4	18.9 MB
	03.Performing Hypothesis Testing in Python/03.03.Demo Performing the Independent t-test.mp4	15 MB
	03.Performing Hypothesis Testing in Python/03.04.Demo Performing Welch's t-test.mp4	15.4 MB
	03.Performing Hypothesis Testing in Python/03.05.Demo Performing the Paired Difference t-test.mp4	12.3 MB
	03.Performing Hypothesis Testing in Python/03.06.Demo One-way ANOVA and Tukey's Honest Significant Difference Test.mp4	12.6 MB
	03.Performing Hypothesis Testing in Python/03.07.Demo Two-way ANOVA.mp4	20.6 MB
	03.Performing Hypothesis Testing in Python/03.08.Demo Chi2 Analysis.mp4	17.6 MB
	03.Performing Hypothesis Testing in Python/03.09.Module Summary.mp4	2.5 MB
	04.Implementing Predictive Models for Continuous Data/04.01.Module Overview.mp4	2.1 MB
	04.Implementing Predictive Models for Continuous Data/04.02.Introducing Linear Regression.mp4	6.4 MB
	04.Implementing Predictive Models for Continuous Data/04.03.Minimizing Mean Square Error.mp4	5.8 MB
	04.Implementing Predictive Models for Continuous Data/04.04.Multiple Regression and Adjusted R-square.mp4	8.3 MB
	04.Implementing Predictive Models for Continuous Data/04.05.Demo Preparing Data for Simple Linear Regression.mp4	14.2 MB
	04.Implementing Predictive Models for Continuous Data/04.06.Demo Linear ..Techniques.mp4	11.9 MB
	04.Implementing Predictive Models for Continuous Data/04.07.Demo Visualizing Correlations in Data.mp4	8.4 MB
	04.Implementing Predictive Models for Continuous Data/04.08.Demo Selecting..Using Correlations.mp4	13.2 MB
	04.Implementing Predictive Models for Continuous Data/04.09.Demo Selecting..Mutual Information.mp4	6.2 MB
	04.Implementing Predictive Models for Continuous Data/04.10.Module Summary.mp4	2.2 MB
	05.Implementing Predictive Models for Categorical Data/05.01.Module Overview.mp4	2.9 MB
	05.Implementing Predictive Models for Categorical Data/05.02.The Intuition behind Logistic Regression.mp4	10.8 MB
	05.Implementing Predictive Models for Categorical Data/05.03.Logistic Regression and Linear Regression.mp4	5.6 MB
	05.Implementing Predictive Models for Categorical Data/05.04.Accuracy, Precision, and Recall.mp4	9.9 MB
	05.Implementing Predictive Models for Categorical Data/05.05.Demo Performing Classification Using Logistic Regression.mp4	14.9 MB
	05.Implementing Predictive Models for Categorical Data/05.06.Demo Selecting..Information.mp4	53.1 MB
	05.Implementing Predictive Models for Categorical Data/05.07.Summary and Further Study.mp4	8.7 MB
	Exercise_file.zip	4.7 MB

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