Interpreting Data Using Statistical Models with Python

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Janani Ravi

2:45:44

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01.Course Overview

01.01.Course Overview.mp4

01:59

02.Understanding Inferential Statistics

02.01.Module Overview.mp4

01:29

02.02.Prerequisites and Course Outline.mp4

01:20

02.03.Descriptive Statistics to Summarize Data.mp4

04:36

02.04.Introducing Hypothesis Testing.mp4

05:00

02.05.Lady Tasting Tea.mp4

04:50

02.06.The Power, Alpha and p-value of a Statistical Test.mp4

02:32

02.07.Introducing the t-test.mp4

04:04

02.08.One Sample Location t-test and the Z-Test.mp4

03:12

02.09.Other Types of t-tests.mp4

06:02

02.10.One-way ANOVA.mp4

06:35

02.11.Two-way ANOVA.mp4

02:48

02.12.Pearsons Chi2 Test.mp4

04:16

02.13.Module Summary.mp4

01:35

03.Performing Hypothesis Testing in Python

03.01.Module Overview.mp4

01:15

03.02.Demo Preparing Data for Hypothesis Testing.mp4

07:48

03.03.Demo Performing the Independent t-test.mp4

06:33

03.04.Demo Performing Welchs t-test.mp4

07:10

03.05.Demo Performing the Paired Difference t-test.mp4

05:11

03.06.Demo One-way ANOVA and Tukeys Honest Significant Difference Test.mp4

05:29

03.07.Demo Two-way ANOVA.mp4

07:40

03.08.Demo Chi2 Analysis.mp4

07:38

03.09.Module Summary.mp4

01:19

04.Implementing Predictive Models for Continuous Data

04.01.Module Overview.mp4

01:12

04.02.Introducing Linear Regression.mp4

03:53

04.03.Minimizing Mean Square Error.mp4

03:22

04.04.Multiple Regression and Adjusted R-square.mp4

04:27

04.05.Demo Preparing Data for Simple Linear Regression.mp4

05:56

04.06.Demo Linear Regression Using Analytical and Machine Learning Techniques.mp4

04:54

04.07.Demo Visualizing Correlations in Data.mp4

03:12

04.08.Demo Selecting Relevant Features for Multiple Regression Using Correlations.mp4

05:03

04.09.Demo Selecting Relevant Features for Multiple Regression Using Mutual Information.mp4

01:39

04.10.Module Summary.mp4

01:18

05.Implementing Predictive Models for Categorical Data

05.01.Module Overview.mp4

01:40

05.02.The Intuition behind Logistic Regression.mp4

06:12

05.03.Logistic Regression and Linear Regression.mp4

02:59

05.04.Accuracy, Precision, and Recall.mp4

05:41

05.05.Demo Performing Classification Using Logistic Regression.mp4

05:49

05.06.Demo Selecting Features Using Chi2, ANOVA, and Mutual Information.mp4

06:15

05.07.Summary and Further Study.mp4

01:51

Description

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.

What You'll Learn?

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.

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Python

Statistics

Janani Ravi

Instructor's Courses

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. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.