(whooshing electronic accent) - [Instructor] For your challenge, I would like you to plot the distribution by hour of my sent messages separately for each day and then overlay the plots for each day. I'm giving you some starter codes to load the data set and convert the dates.
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Author
Michele Vallisneri
Released
7/17/2018
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.
Topics include:
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
2h 58m
Duration
96,407
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Introduction
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Welcome1m 9s
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Using the exercise files1m 2s
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1. Installation and Setup
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2. Importing and Cleaning Data
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The structure of data1m 52s
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Create tidy data tables5m 20s
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Introducing pandas7m 28s
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Data cleaning12m 6s
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3. Visualizing and Describing Data
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The power of visualization7m 12s
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Describe distributions5m 3s
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Plot distributions7m 34s
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More quantitative variables7m 58s
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Plot categorical variables4m 30s
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Personal email analytics10m 10s
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4. Introduction to Statistical Inference
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Statistical inference1m 27s
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Confidence intervals9m 30s
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Bootstrapping7m 10s
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Hypothesis testing7m 34s
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5. Introduction to Statistical Modeling
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Statistical modeling1m 35s
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Fitting models to data7m 36s
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Goodness of fit6m 13s
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Cross validation6m 22s
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Logistic regression5m 30s
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Bayesian inference9m 14s
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Conclusion
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Next steps1m 55s
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Video: ✓ Challenge: More email analytics