[TutorialPace.com] Lynda - Python Statistics Essential Training - [TP]

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[TutorialPace.com] Lynda - Python Statistics Essential Training - [TP] 4.4. Introduction to Statistical Inference
  • 29.✓ Challenge - Bootstrapping grades.en.srt (0.5 KB)
  • 30.✓ Solution - Bootstrapping grades.en.srt (1.6 KB)
  • 24.Statistical inference.en.srt (2.3 KB)
  • 28.p values and confidence intervals.en.srt (5.8 KB)
  • 26.Bootstrapping.en.srt (7.7 KB)
  • 27.Hypothesis testing.en.srt (9.1 KB)
  • 25.Confidence intervals.en.srt (11.7 KB)
  • 29.✓ Challenge - Bootstrapping grades.mp4 (1.1 MB)
  • 24.Statistical inference.mp4 (2.9 MB)
  • 30.✓ Solution - Bootstrapping grades.mp4 (4.3 MB)
  • 28.p values and confidence intervals.mp4 (13.4 MB)
  • 26.Bootstrapping.mp4 (17.1 MB)
  • 25.Confidence intervals.mp4 (17.2 MB)
  • 27.Hypothesis testing.mp4 (19.5 MB)
3.3. Visualizing and Describing Data
  • 22.✓ Challenge - More email analytics.en.srt (0.5 KB)
  • 23.✓ Solution - More email analytics.en.srt (1.6 KB)
  • 20.Plot categorical variables.en.srt (4.3 KB)
  • 19.Describe categorical variables.en.srt (5.6 KB)
  • 17.Plots of two quantitative variables.en.srt (5.8 KB)
  • 15.Describe distributions.en.srt (6.9 KB)
  • 18.More quantitative variables.en.srt (7.3 KB)
  • 16.Plot distributions.en.srt (8.1 KB)
  • 14.The power of visualization.en.srt (8.4 KB)
  • 21.Personal email analytics.en.srt (10.6 KB)
  • 22.✓ Challenge - More email analytics.mp4 (1.3 MB)
  • 23.✓ Solution - More email analytics.mp4 (4.6 MB)
  • 15.Describe distributions.mp4 (9.1 MB)
  • 20.Plot categorical variables.mp4 (11.9 MB)
  • 19.Describe categorical variables.mp4 (12.0 MB)
  • 17.Plots of two quantitative variables.mp4 (16.7 MB)
  • 16.Plot distributions.mp4 (18.8 MB)
  • 14.The power of visualization.mp4 (20.0 MB)
  • 18.More quantitative variables.mp4 (21.5 MB)
  • 21.Personal email analytics.mp4 (29.0 MB)
5.5. Introduction to Statistical Modeling
  • 37.✓ Challenge - Explaining baby weight at birth.en.srt (1.2 KB)
  • 38.✓ Solution - Explaining baby weight at birth.en.srt (1.3 KB)
  • 31.Statistical modeling.en.srt (2.6 KB)
  • 34.Cross validation.en.srt (7.1 KB)
  • 35.Logistic regression.en.srt (7.4 KB)
  • 33.Goodness of fit.en.srt (8.0 KB)
  • 32.Fitting models to data.en.srt (9.8 KB)
  • 36.Bayesian inference.en.srt (11.1 KB)
  • 31.Statistical modeling.mp4 (2.4 MB)
  • 37.✓ Challenge - Explaining baby weight at birth.mp4 (2.8 MB)
  • 38.✓ Solution - Explaining baby weight at birth.mp4 (4.0 MB)
  • 35.Logistic regression.mp4 (11.1 MB)
  • 34.Cross validation.mp4 (13.1 MB)
  • 33.Goodness of fit.mp4 (14.0 MB)
  • 32.Fitting models to data.mp4 (16.5 MB)
  • 36.Bayesian inference.mp4 (19.0 MB)
0.Introduction
  • 03.Using the exercise files.en.srt (1.4 KB)
  • 02.What you need to know.en.srt (1.5 KB)
  • 01.Welcome.en.srt (1.8 KB)
  • 02.What you need to know.mp4 (1.2 MB)
  • 03.Using the exercise files.mp4 (3.0 MB)
  • 01.Welcome.mp4 (6.9 MB)
2.2. Importing and Cleaning Data
  • 12.✓ Challenge - Personal email analytics.en.srt (1.6 KB)
  • 08.The structure of data.en.srt (2.7 KB)
  • 13.✓ Solution - Personal email analytics.en.srt (3.7 KB)
  • 09.Create tidy data tables.en.srt (7.1 KB)
  • 10.Introducing pandas.en.srt (8.3 KB)
  • 11.Data cleaning.en.srt (13.2 KB)
  • 08.The structure of data.mp4 (3.1 MB)
  • 12.✓ Challenge - Personal email analytics.mp4 (3.4 MB)
  • 13.✓ Solution - Personal email analytics.mp4 (10.0 MB)
  • 09.Create tidy data tables.mp4 (11.2 MB)
  • 10.Introducing pandas.mp4 (18.9 MB)
  • 11.Data cleaning.mp4 (33.4 MB)
6.Conclusion
  • 39.Next steps.en.srt (3.2 KB)
  • 39.Next steps.mp4 (2.9 MB)
1.1. Installation and Setup
  • 07.Using Python in the cloud.en.srt (3.3 KB)
  • 06.Working with Jupyter Notebook.en.srt (3.6 KB)
  • 05.Install Anaconda Python on Windows.en.srt (4.2 KB)
  • 04.Install Anaconda Python on OS X.en.srt (5.0 KB)
  • 06.Working with Jupyter Notebook.mp4 (6.5 MB)
  • 04.Install Anaconda Python on OS X.mp4 (8.1 MB)
  • 07.Using Python in the cloud.mp4 (8.8 MB)
  • 05.Install Anaconda Python on Windows.mp4 (10.3 MB)

Description

With this course, gain insight into key statistical concepts and build practical analytics skills using Python and powerful third-party libraries. Instructor Michele Vallisneri covers several major skills: cleaning, visualizing, and describing data, statistical inference, and statistical modeling. All concepts are introduced by analyzing intriguing real-world datasets and discussed from a machine-learning perspective—which assumes that powerful computation can replace complex mathematics.

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[TutorialPace.com] Lynda - Python Statistics Essential Training - [TP]

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431.2 MB
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[TutorialPace.com] Lynda - Python Statistics Essential Training - [TP]

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