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1 | | -# Interactive-Data-Visualization-with-Python |
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3 | | -With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Master Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. |
4 | | - |
5 | | -You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirement. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You’ll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. |
| 2 | +[](https://github.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/issues) |
| 3 | +[](https://github.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/network) |
| 4 | +[](https://github.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/stargazers) |
| 5 | +[](https://github.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/pulls) |
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7 | | -By the end of the course, you’ll have a new skill set that’ll make you the go-to person for transforming data visualizations into engaging and interesting stories. |
| 7 | +# Interactive Data Visualization with Python |
8 | 8 |
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9 | | -# What you will learn |
| 9 | +With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Master Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. |
10 | 10 |
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11 | | -• Understand similarities and differences between data visualization types<br> |
12 | | -• Manipulate plotting parameters and styles to make appealing plots<br> |
13 | | -• Select appropriate Python libraries based on the context of data visualization<br> |
14 | | -• Introduce a variety of interactive functionality in your data visualizations<br> |
15 | | -• Know limitations and caveats of available interactive visualization libraries |
| 11 | +You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirement. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You’ll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. |
16 | 12 |
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17 | | -# Hardware Requirements |
| 13 | +By the end of the course, you’ll have a new skill set that’ll make you the go-to person for transforming data visualizations into engaging and interesting stories. |
18 | 14 |
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19 | | -For the optimal experience, we recommend the following hardware configuration: <br> |
20 | | -Processors:<br> |
21 | | -• Intel® Core™ i5 processor 4300M at 2.60 GHz or 2.59 GHz (1 socket, 2 cores, 2 threads per core), 8 GB of DRAM<br> |
22 | | -• Intel® Xeon® processor E5-2698 v3 at 2.30 GHz (2 sockets, 16 cores each, 1 thread per core), 64 GB of DRAM<br> |
23 | | -• Intel® Xeon Phi™ processor 7210 at 1.30 GHz (1 socket, 64 cores, 4 threads per core), 32 GB of DRAM, 16 GB of MCDRAM (flat mode enabled)<br> |
24 | | -Disk space: 2 to 3 GB<br> |
25 | | -Operating systems: Windows® 10, macOS, and Linux<br> |
| 15 | +# What you will learn |
26 | 16 |
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27 | | -# Minimum System Requirements |
| 17 | +• Understand similarities and differences between data visualization types<br> |
| 18 | +• Manipulate plotting parameters and styles to make appealing plots<br> |
| 19 | +• Select appropriate Python libraries based on the context of data visualization<br> |
| 20 | +• Introduce a variety of interactive functionality in your data visualizations<br> |
| 21 | +• Know limitations and caveats of available interactive visualization libraries |
28 | 22 |
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29 | | -• Processors: Intel Atom® processor or Intel® Core™ i3 processor<br> |
30 | | -• Disk space: 1 GB<br> |
31 | | -• Operating systems: Windows* 7 or later, macOS, and Linux |
| 23 | +<b> The examples of this title have been implemented in Windows/Mac/Linux operating system.</b> |
32 | 24 |
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33 | 25 | # Software Requirements |
34 | 26 |
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35 | 27 | We also recommend that you have the following software installed in advance:<br> |
36 | | -• Browser: Google Chrome or Mozilla Firefox<br> |
37 | | -• Git latest version <br> |
38 | | -• Anaconda 3.7 Python distribution<br> |
39 | | -• Python 3.7<br> |
40 | | -• The following Python libraries installed: numpy, pandas, matplotlib, seaborn, |
| 28 | +• Browser: Google Chrome or Mozilla Firefox<br> |
| 29 | +• Git latest version <br> |
| 30 | +• Anaconda 3.7 Python distribution<br> |
| 31 | +• Python 3.7<br> |
| 32 | +• The following Python libraries installed: numpy, pandas, matplotlib, seaborn, |
41 | 33 | plotly, bokeh, altair, and geopandas |
42 | 34 |
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