| 1. Functions to Draw linear Regression Models.mp4 | 51.7 MB | ||
| 1. Functions to Draw linear Regression Models.srt | 8.1 KB | ||
| 1. Introduction to Seaborn Intermediate.mp4 | 4.2 MB | ||
| 1. Introduction to Seaborn Intermediate.srt | 1.8 KB | ||
| 1. Plotting Univariate Distributions.mp4 | 79.7 MB | ||
| 1. Plotting Univariate Distributions.srt | 10.9 KB | ||
| 2. Fitting Different Kinds of Models.mp4 | 72 MB | ||
| 2. Fitting Different Kinds of Models.srt | 7.4 KB | ||
| 2. Plotting Bivariate Distributions.mp4 | 89.4 MB | ||
| 2. Plotting Bivariate Distributions.srt | 9.5 KB | ||
| 3. Conditioning on Other Variables.mp4 | 87.7 MB | ||
| 3. Conditioning on Other Variables.srt | 9.7 KB | ||
| 4. Examples on KDEPLOT.mp4 | 97.7 MB | ||
| 5. Examples on PAIRPLOT.mp4 | 58.8 MB | ||
| 6. JOINTPLOT and LMPLOT.mp4 | 88.7 MB | ||
| 6. JOINTPLOT and LMPLOT.srt | 9.3 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 18 total files | |||
Python Data Visualization using Seaborn – Intermediate
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 9 lectures (1 hour, 12 mins) | Size: 630 MB
Be proficient with seaborn technology that is widely used for statistical data visualization techniques
What you'll learn
Under this part of the training program, advanced concepts of seaborn tool that are an introduction to seaborn advance, building structure multiplot grids, conditional small multiplies, use of custom functions, plotting pairwise data relationships, choosing color palettes, use of different seaborn figure styles, setting different color palettes, use of reference files in advance, etc.
This part of the training program what we learn about intermediate functionalities of seaborn which includes plotting univariate distribution, clotting of bivariate distributions, visualizing linear relationships, functions to draw linear regression models, filtering different kinds of models, conditioning all other variables, join plot and LM plot, use of reference files, KDE plot, etc.
Course
Requirements
Basic knowledge about Data and analytics and Python programming would be preferable
Anyone serious about learning Data Visualization and wants to make a career in this Field
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