| 1. Introduction of Seaborn.mp4 | 4.5 MB | ||
| 1. Introduction of Seaborn.srt | 1.7 KB | ||
| 1. Scatter Plot Part 1.mp4 | 82.5 MB | ||
| 1. Scatter Plot Part 1.srt | 11.1 KB | ||
| 1. Showing Multiple Relationships with Facets.mp4 | 52.5 MB | ||
| 1. Showing Multiple Relationships with Facets.srt | 8.2 KB | ||
| 10. Swarmplot Examples.mp4 | 65.7 MB | ||
| 10. Swarmplot Examples.srt | 5.6 KB | ||
| 11. Stripplot Examples.mp4 | 82.9 MB | ||
| 12. Catplot Examples.mp4 | 78.5 MB | ||
| 2. Categorical Scatterplots.mp4 | 83.4 MB | ||
| 2. Scatter Plot Part 2.mp4 | 77.5 MB | ||
| 2. Scatter Plot Part 2.srt | 5.4 KB | ||
| 3. Distributions of Observations within Categories.mp4 | 83.2 MB | ||
| 3. Distributions of Observations within Categories.srt | 8.4 KB | ||
| 3. Line Plots Part 1.mp4 | 90.6 MB | ||
| 3. Line Plots Part 1.srt | 8.4 KB | ||
| 4. Line Plots Part 2.mp4 | 86.7 MB | ||
| 4. Line Plots Part 2.srt | 6.6 KB | ||
| 4. Statistical Estimation within Categories.mp4 | 96 MB | ||
| 4. Statistical Estimation within Categories.srt | 9.4 KB | ||
| 5. Countplot Examples.mp4 | 60.3 MB | ||
| 6. Pointplot Examples.mp4 | 85 MB | ||
| 6. Pointplot Examples.srt | 7.6 KB | ||
| 7. Boxenplot Examples.mp4 | 126.1 MB | ||
| 7. Boxenplot Examples.srt | 9.9 KB | ||
| 8. Violenplot Examples.mp4 | 72 MB | ||
| 9. Barplot Examples.mp4 | 126.1 MB | ||
| 9. Barplot Examples.srt | 9.9 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 31 total files | |||
Python Data Visualization using Seaborn - Beginners
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 17 lectures (2 hour, 23 mins) | Size: 1.32 GB
Learn attractive and informative statistical graphics and data visualization in Python
What you'll learn
One will learn about introduction to seaborn, review of the training, different types of plots, distribution plot, scatterplot and heat map, case studies of scatter plot, boxplot, bank problem, case study on swarm plot, etc.
Other skills that are going to be covered under this training program is visualizing statistical relationships which include scatterplots, line plots, plotting with categorical data, showing multiple relationships with facets, categorical scatter plots, distribution of observations with categories, statistical estimation with categories, count plot, point plot, boxplot, bar plot, use of reference files, etc.
Requirements
The user should also have a mathematical background as most of the algorithms being used and the concepts which are discussed are mathematics-based.
The basic prerequisite for this course is that the student or the professional should have a basic knowledge and understanding of the machine learning tools and techniques and also should have a basic knowledge and overview of the data science techniques. Apart from this, he should also be aware of the basic analytical concepts which are a must while opting for this course.
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