| 01.Welcome.en.srt | 1.8 KB | ||
| 01.Welcome.mp4 | 6.9 MB | ||
| 02.What you need to know.en.srt | 1.5 KB | ||
| 02.What you need to know.mp4 | 1.2 MB | ||
| 03.Using the exercise files.en.srt | 1.4 KB | ||
| 03.Using the exercise files.mp4 | 3 MB | ||
| 04.Install Anaconda Python on OS X.en.srt | 5 KB | ||
| 04.Install Anaconda Python on OS X.mp4 | 8.1 MB | ||
| 05.Install Anaconda Python on Windows.en.srt | 4.2 KB | ||
| 05.Install Anaconda Python on Windows.mp4 | 10.3 MB | ||
| 06.Working with Jupyter Notebook.en.srt | 3.6 KB | ||
| 06.Working with Jupyter Notebook.mp4 | 6.5 MB | ||
| 07.Using Python in the cloud.en.srt | 3.3 KB | ||
| 07.Using Python in the cloud.mp4 | 8.8 MB | ||
| 08.The structure of data.en.srt | 2.7 KB | ||
| 08.The structure of data.mp4 | 3.1 MB | ||
| 09.Create tidy data tables.en.srt | 7.1 KB | ||
| 09.Create tidy data tables.mp4 | 11.2 MB | ||
| 10.Introducing pandas.en.srt | 8.3 KB | ||
| 10.Introducing pandas.mp4 | 18.9 MB | ||
| 11.Data cleaning.en.srt | 13.2 KB | ||
| 11.Data cleaning.mp4 | 33.4 MB | ||
| 12.✓ Challenge - Personal email analytics.en.srt | 1.6 KB | ||
| 12.✓ Challenge - Personal email analytics.mp4 | 3.4 MB | ||
| 13.✓ Solution - Personal email analytics.en.srt | 3.7 KB | ||
| 13.✓ Solution - Personal email analytics.mp4 | 10 MB | ||
| 14.The power of visualization.en.srt | 8.4 KB | ||
| 14.The power of visualization.mp4 | 20 MB | ||
| 15.Describe distributions.en.srt | 6.9 KB | ||
| 15.Describe distributions.mp4 | 9.1 MB | ||
| 16.Plot distributions.en.srt | 8.1 KB | ||
| 16.Plot distributions.mp4 | 18.8 MB | ||
| 17.Plots of two quantitative variables.en.srt | 5.8 KB | ||
| 17.Plots of two quantitative variables.mp4 | 16.7 MB | ||
| 18.More quantitative variables.en.srt | 7.3 KB | ||
| 18.More quantitative variables.mp4 | 21.5 MB | ||
| 19.Describe categorical variables.en.srt | 5.6 KB | ||
| 19.Describe categorical variables.mp4 | 12 MB | ||
| 20.Plot categorical variables.en.srt | 4.3 KB | ||
| 20.Plot categorical variables.mp4 | 11.9 MB | ||
| 21.Personal email analytics.en.srt | 10.6 KB | ||
| 21.Personal email analytics.mp4 | 29 MB | ||
| 22.✓ Challenge - More email analytics.en.srt | 512 B | ||
| 22.✓ Challenge - More email analytics.mp4 | 1.3 MB | ||
| 23.✓ Solution - More email analytics.en.srt | 1.6 KB | ||
| 23.✓ Solution - More email analytics.mp4 | 4.6 MB | ||
| 24.Statistical inference.en.srt | 2.3 KB | ||
| 24.Statistical inference.mp4 | 2.9 MB | ||
| 25.Confidence intervals.en.srt | 11.7 KB | ||
| 25.Confidence intervals.mp4 | 17.2 MB | ||
| 26.Bootstrapping.en.srt | 7.7 KB | ||
| 26.Bootstrapping.mp4 | 17.1 MB | ||
| 27.Hypothesis testing.en.srt | 9.1 KB | ||
| 27.Hypothesis testing.mp4 | 19.5 MB | ||
| 28.p values and confidence intervals.en.srt | 5.8 KB | ||
| 28.p values and confidence intervals.mp4 | 13.4 MB | ||
| 29.✓ Challenge - Bootstrapping grades.en.srt | 512 B | ||
| 29.✓ Challenge - Bootstrapping grades.mp4 | 1.1 MB | ||
| 30.✓ Solution - Bootstrapping grades.en.srt | 1.6 KB | ||
| 30.✓ Solution - Bootstrapping grades.mp4 | 4.3 MB | ||
| 31.Statistical modeling.en.srt | 2.6 KB | ||
| 31.Statistical modeling.mp4 | 2.4 MB | ||
| 32.Fitting models to data.en.srt | 9.8 KB | ||
| 32.Fitting models to data.mp4 | 16.5 MB | ||
| 33.Goodness of fit.en.srt | 8 KB | ||
| 33.Goodness of fit.mp4 | 14 MB | ||
| 34.Cross validation.en.srt | 7.1 KB | ||
| 34.Cross validation.mp4 | 13.1 MB | ||
| 35.Logistic regression.en.srt | 7.4 KB | ||
| 35.Logistic regression.mp4 | 11.1 MB | ||
| 36.Bayesian inference.en.srt | 11.1 KB | ||
| 36.Bayesian inference.mp4 | 19 MB | ||
| 37.✓ Challenge - Explaining baby weight at birth.en.srt | 1.2 KB | ||
| 37.✓ Challenge - Explaining baby weight at birth.mp4 | 2.8 MB | ||
| 38.✓ Solution - Explaining baby weight at birth.en.srt | 1.3 KB | ||
| 38.✓ Solution - Explaining baby weight at birth.mp4 | 4 MB | ||
| 39.Next steps.en.srt | 3.2 KB | ||
| 39.Next steps.mp4 | 2.9 MB | ||
| ▲ 78 total files | |||
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.
Stream Python Statistics Essential Training Course Online
https://tutorialpace.com/Python-Statistics-Essential-Training
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