├── .gitignore ├── Lectures ├── .DS_Store ├── 01 Introduction to Data Visualization │ ├── .DS_Store │ ├── 01 Importance of Data Visualization.ipynb │ ├── 02 Overview of Popular Visualization Libraries in Python.ipynb │ └── images │ │ ├── banner.png │ │ ├── bokeh.png │ │ ├── data-visualization.png │ │ ├── data-visualizations.png │ │ ├── data-viz-applications.png │ │ ├── images.png │ │ ├── interactive-data-viz-tools.png │ │ ├── matplotlib.webp │ │ ├── plotly.avif │ │ ├── plotly_phidgets.jpg │ │ ├── seaborn.png │ │ ├── static-data-viz-tools.png │ │ ├── types-of-data.png │ │ └── visualization-libraries-comparison.png ├── 03 Plotting with Matplotlib │ ├── 01 Introduction to Matplotlib.ipynb │ ├── 02 Figure and Axes Objects.ipynb │ ├── 03 Basic Plot Types.ipynb │ ├── 04 Text, Labels, and Annotations.ipynb │ └── images │ │ ├── banner.png │ │ └── matplotlib-figure-anatomy.webp ├── 04 Plotting with Seaborn │ ├── 01 Introduction to Seaborn.ipynb │ ├── 02 Introduction to Seaborn Plot Types.ipynb │ ├── 03 Relational Plots.ipynb │ ├── 04 Distribution Plots.ipynb │ ├── 05 Categorical Plots.ipynb │ ├── 06 Regression Plots.ipynb │ ├── 07 Matrix Plots.ipynb │ ├── 08 Building Structured Multi-plot Grids.ipynb │ ├── 09 Controlling figure aesthetics.ipynb │ └── images │ │ ├── banner.png │ │ └── seaborn-overview.png └── 06 Real-world Projects │ ├── 01 Titanic.ipynb │ ├── 02 Housing.ipynb │ ├── 03 Breast Cancer.ipynb │ ├── 04 Life Expectance Dashboard.ipynb │ ├── 05 Movie Review Sentiment Analysis.ipynb │ └── datasets │ ├── housing.csv │ ├── iranian_books.csv │ └── resume.pdf ├── README.md └── images ├── banner.png └── pytopia-course.png /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; 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For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | test.ipynb 162 | .DS_Store 163 | -------------------------------------------------------------------------------- /Lectures/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytopia/Data-Visualization/8d7371fdf1e265862f96ca47b5451b3b9180a4b1/Lectures/.DS_Store -------------------------------------------------------------------------------- /Lectures/01 Introduction to Data Visualization/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytopia/Data-Visualization/8d7371fdf1e265862f96ca47b5451b3b9180a4b1/Lectures/01 Introduction to Data Visualization/.DS_Store -------------------------------------------------------------------------------- /Lectures/01 Introduction to Data Visualization/01 Importance of Data Visualization.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "# Importance of Data Visualization" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "Organizations across all industries are collecting and generating vast amounts of information. However, without effective tools and techniques for making sense of this data, it can quickly become overwhelming and meaningless. This is where data visualization comes in. By representing complex data through visual means, we can unlock a wide range of benefits that help us gain valuable insights, communicate effectively, and drive better outcomes." 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "Data visualization offers numerous benefits that help us extract valuable insights, communicate effectively, and drive better decision-making. Some key benefits include:\n", 36 | "\n", 37 | "- **Facilitating data exploration and understanding**: Visualization tools allow us to explore complex datasets quickly and easily, enabling us to identify areas of interest and develop a deeper understanding of the data's structure and relationships.\n", 38 | "\n", 39 | "- **Identifying patterns, trends, and outliers**: By representing data graphically, we can spot clusters, correlations, and anomalies that might be hidden in the raw data. This helps us uncover valuable insights and make data-driven decisions.\n", 40 | "\n", 41 | "- **Enabling faster and better decision-making**: Well-designed dashboards and infographics provide a clear and concise view of the data, allowing decision-makers to quickly grasp key insights and respond to changing circumstances in real-time.\n", 42 | "\n", 43 | "- **Enhancing communication and collaboration**: Data visualizations create a shared visual language that fosters alignment, consensus-building, and cross-functional collaboration within teams and organizations.\n", 44 | "\n", 45 | "- **Providing persuasive and impactful presentations**: Compelling visuals capture the audience's attention and convey messages with clarity and conviction, inspiring action and driving change.\n" 46 | ] 47 | }, 48 | { 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "Real-world examples demonstrate the power of data visualization:\n", 53 | "\n", 54 | "- In public health, interactive dashboards and maps have been crucial in tracking and communicating the spread of COVID-19, enabling informed decisions about prevention and response efforts.\n", 55 | "- Companies like Walmart and Coca-Cola have used data visualization to optimize supply chain operations and marketing strategies by identifying efficiencies, anticipating challenges, and adapting to changing conditions.\n" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "By leveraging the power of visual communication, data visualization transforms raw data into actionable insights, compelling stories, and meaningful change, driving success in various domains." 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "**Table of contents** \n", 70 | "- [Real-world Applications](#toc1_) \n", 71 | "- [Key Principles of Effective Data Visualization](#toc2_) \n", 72 | "- [Common Pitfalls and Challenges](#toc3_) \n", 73 | "- [Tools and Technologies for Data Visualization](#toc4_) \n", 74 | " - [Static Data Visualization Tools](#toc4_1_) \n", 75 | " - [Interactive Data Visualization Tools](#toc4_2_) \n", 76 | " - [Importance of Choosing the Right Tool for the Job](#toc4_3_) \n", 77 | " - [Emerging Trends and Future Directions](#toc4_4_) \n", 78 | "- [Case Studies and Examples](#toc5_) \n", 79 | "- [Conclusion](#toc6_) \n", 80 | "\n", 81 | "\n", 88 | "" 89 | ] 90 | }, 91 | { 92 | "cell_type": "markdown", 93 | "metadata": {}, 94 | "source": [ 95 | "## [Real-world Applications](#toc0_)" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "Data visualization is a powerful tool used across various industries to gain insights, make decisions, and communicate effectively. Here are some key applications:\n", 110 | "\n", 111 | "1. Business and Finance\n", 112 | " - Market analysis and sales reporting\n", 113 | " - Tracking KPIs and identifying trends\n", 114 | " - Optimizing operations and resource allocation\n", 115 | "\n", 116 | "2. Healthcare and Scientific Research\n", 117 | " - Clinical trials: visualizing treatment efficacy and safety\n", 118 | " - Epidemiology: tracking disease spread and risk factors\n", 119 | " - Exploring complex biological systems and identifying intervention targets\n", 120 | "\n", 121 | "3. Social Sciences and Public Policy\n", 122 | " - Demographic studies: visualizing population distributions and migration patterns\n", 123 | " - Political science: analyzing voting behavior and political coalitions\n", 124 | " - Economics: exploring relationships between variables like income, education, and health\n", 125 | "\n", 126 | "4. Journalism and Media\n", 127 | " - Creating engaging infographics and data-driven stories\n", 128 | " - Investigating complex social and political issues\n", 129 | " - Informing public discourse and inspiring action\n" 130 | ] 131 | }, 132 | { 133 | "cell_type": "markdown", 134 | "metadata": {}, 135 | "source": [ 136 | "Real-world examples:\n", 137 | "- Walmart: optimizing supply chain and inventory management\n", 138 | "- COVID-19 dashboards: tracking virus spread and informing public policy\n", 139 | "- Urban Institute: exploring racial and economic inequality through interactive maps\n", 140 | "- ProPublica: investigating political ad spending, environmental hazards, and police misconduct\n" 141 | ] 142 | }, 143 | { 144 | "cell_type": "markdown", 145 | "metadata": {}, 146 | "source": [ 147 | "As data volumes continue to grow, effective data visualization will become increasingly crucial for understanding and interacting with the world around us." 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "## [Key Principles of Effective Data Visualization](#toc0_)" 155 | ] 156 | }, 157 | { 158 | "cell_type": "markdown", 159 | "metadata": {}, 160 | "source": [ 161 | "To create impactful and meaningful visualizations, it's essential to follow certain key principles. These principles ensure that your visualizations are clear, accurate, and effectively convey the intended message to your audience.\n", 162 | "\n", 163 | "1. **Choosing the right visualization type for the data and purpose:**\n", 164 | " - Select a visualization type that aligns with the nature of your data (e.g., categorical, numerical, temporal)\n", 165 | " - Consider the purpose of your visualization (e.g., comparison, relationship, distribution) and choose a chart type that effectively communicates that purpose\n", 166 | "\n", 167 | "2. **Ensuring clarity, accuracy, and readability:**\n", 168 | " - Present data accurately and avoid distorting or misrepresenting information\n", 169 | " - Use clear and concise labels, titles, and annotations to guide the viewer's understanding\n", 170 | " - Ensure that the visualization is easily readable by selecting appropriate font sizes, styles, and spacing\n", 171 | "\n", 172 | "3. **Using appropriate colors, scales, and labels:**\n", 173 | " - Choose a color scheme that is visually appealing, distinguishable, and accessible to all viewers, including those with color vision deficiencies\n", 174 | " - Use scales (e.g., axes, legends) that are appropriate for the data range and granularity\n", 175 | " - Label data points, axes, and legends clearly and consistently\n", 176 | "\n", 177 | "4. **Minimizing clutter and focusing on key insights:**\n", 178 | " - Remove unnecessary elements (e.g., gridlines, borders) that do not add value to the visualization\n", 179 | " - Highlight the most important data points or trends to draw the viewer's attention\n", 180 | " - Use whitespace effectively to create visual hierarchy and improve readability\n", 181 | "\n", 182 | "5. **Considering the target audience and context:**\n", 183 | " - Tailor your visualization to the knowledge level and interests of your target audience\n", 184 | " - Provide sufficient context and explanations to help viewers interpret the data accurately\n", 185 | " - Consider the medium and platform where the visualization will be presented (e.g., print, digital, interactive) and optimize accordingly\n" 186 | ] 187 | }, 188 | { 189 | "cell_type": "markdown", 190 | "metadata": {}, 191 | "source": [ 192 | "By adhering to these principles, you can create data visualizations that are not only visually appealing but also effective in communicating insights and driving understanding.\n" 193 | ] 194 | }, 195 | { 196 | "cell_type": "markdown", 197 | "metadata": {}, 198 | "source": [ 199 | "Remember, the goal is to tell a clear and compelling story with your data. By carefully selecting the right visualization type, ensuring clarity and accuracy, using appropriate visual elements, minimizing clutter, and considering your audience, you can unlock the full potential of your data and make a meaningful impact." 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "## [Common Pitfalls and Challenges](#toc0_)" 207 | ] 208 | }, 209 | { 210 | "cell_type": "markdown", 211 | "metadata": {}, 212 | "source": [ 213 | "While data visualization offers numerous benefits, it's essential to be aware of common pitfalls and challenges that can undermine the effectiveness and integrity of visual representations. Some key issues to watch out for include:\n", 214 | "\n", 215 | "- **Misleading or deceptive visualizations**: \n", 216 | " - Manipulating scales, axes, or color schemes to exaggerate or downplay certain aspects of the data can lead to false conclusions.\n", 217 | " - Cherry-picking data points or selectively presenting information can create a biased or incomplete picture.\n", 218 | " - It's crucial to maintain honesty and transparency in visual representations to ensure trust and credibility.\n", 219 | "\n", 220 | "- **Overcomplicating or oversimplifying the data**:\n", 221 | " - Packing too much information into a single visualization can make it cluttered, confusing, and difficult to interpret.\n", 222 | " - On the other hand, oversimplifying the data by omitting important details or context can lead to misleading or incomplete conclusions.\n", 223 | " - Striking the right balance between complexity and simplicity is key to effective communication.\n", 224 | "\n", 225 | "- **Ignoring the limitations and biases of the data**:\n", 226 | " - Every dataset has its own limitations, uncertainties, and potential biases that should be acknowledged and addressed.\n", 227 | " - Failing to consider factors such as sample size, data quality, or collection methods can lead to flawed analyses and interpretations.\n", 228 | " - It's important to be transparent about the limitations of the data and avoid overstating the conclusions that can be drawn from it.\n", 229 | "\n", 230 | "- **Failing to provide context or explanations**:\n", 231 | " - Visualizations should not be presented in isolation without the necessary context or explanations to help the audience understand and interpret them correctly.\n", 232 | " - Lack of clear labels, legends, or annotations can leave viewers confused or misinterpreting the data.\n", 233 | " - Providing accompanying text, narratives, or interactive elements can help guide the audience and ensure accurate comprehension.\n" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "metadata": { 239 | "vscode": { 240 | "languageId": "plaintext" 241 | } 242 | }, 243 | "source": [ 244 | "To learn more about misleading data visualizations, check out the following resources:\n", 245 | "- [How to Spot Visualization Lies](https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/)\n", 246 | "- [Data Visualizations Designed to Mislead](https://www.datapine.com/blog/misleading-data-visualization-examples/)" 247 | ] 248 | }, 249 | { 250 | "cell_type": "markdown", 251 | "metadata": {}, 252 | "source": [ 253 | "To illustrate these pitfalls, consider the following examples:\n", 254 | "\n", 255 | "- A misleading graph that uses a truncated y-axis to exaggerate small differences between data points, creating a false impression of significant variation.\n", 256 | "- An overly complex network diagram with numerous nodes and edges, making it difficult for viewers to discern the key relationships and insights.\n", 257 | "- A heatmap that fails to normalize the data, leading to biased comparisons between regions with vastly different population sizes.\n", 258 | "- An infographic that presents statistics without specifying the data source, sample size, or timeframe, leaving the audience without the necessary context to evaluate the information.\n" 259 | ] 260 | }, 261 | { 262 | "cell_type": "markdown", 263 | "metadata": {}, 264 | "source": [ 265 | "By being aware of these common pitfalls and challenges, data visualization practitioners can take steps to avoid them and ensure that their visual representations are accurate, informative, and effective. This involves:\n", 266 | "\n", 267 | "- Carefully selecting the appropriate visualization techniques and design choices to accurately represent the data.\n", 268 | "- Striving for clarity and simplicity while still providing sufficient detail and context.\n", 269 | "- Being transparent about the limitations, uncertainties, and potential biases of the data.\n", 270 | "- Providing clear explanations, labels, and annotations to guide the audience's interpretation.\n" 271 | ] 272 | }, 273 | { 274 | "cell_type": "markdown", 275 | "metadata": {}, 276 | "source": [ 277 | "By addressing these challenges head-on, we can create data visualizations that are not only visually appealing but also trustworthy, informative, and impactful." 278 | ] 279 | }, 280 | { 281 | "cell_type": "markdown", 282 | "metadata": {}, 283 | "source": [ 284 | "## [Tools and Technologies for Data Visualization](#toc0_)" 285 | ] 286 | }, 287 | { 288 | "cell_type": "markdown", 289 | "metadata": {}, 290 | "source": [ 291 | "To create effective and engaging data visualizations, you need the right tools and technologies at your disposal. In this section, we'll explore some of the most popular software and libraries used for data visualization, starting with static visualization tools and then moving on to interactive ones. We'll also discuss the importance of choosing the right tool for the job.\n" 292 | ] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "metadata": {}, 297 | "source": [ 298 | "### [Static Data Visualization Tools](#toc0_)\n", 299 | "\n", 300 | "1. **Matplotlib:**\n", 301 | " - A fundamental plotting library in Python for creating static, animated, and interactive visualizations\n", 302 | " - Offers a MATLAB-like interface and integrates well with other Python libraries like NumPy and Pandas\n", 303 | " - Suitable for creating a wide range of chart types, from simple line plots to complex heatmaps\n", 304 | "\n", 305 | "2. **Seaborn:**\n", 306 | " - A statistical data visualization library in Python built on top of Matplotlib\n", 307 | " - Provides a high-level interface for creating attractive and informative statistical graphics\n", 308 | " - Offers built-in themes and color palettes for enhancing the aesthetics of the plots\n" 309 | ] 310 | }, 311 | { 312 | "cell_type": "markdown", 313 | "metadata": {}, 314 | "source": [ 315 | "" 316 | ] 317 | }, 318 | { 319 | "cell_type": "markdown", 320 | "metadata": {}, 321 | "source": [ 322 | "### [Interactive Data Visualization Tools](#toc0_)\n", 323 | "\n", 324 | "1. **Tableau:**\n", 325 | " - A powerful and user-friendly data visualization and business intelligence platform\n", 326 | " - Offers drag-and-drop functionality and a wide range of interactive chart types and dashboards\n", 327 | " - Enables users to explore and interact with data through filtering, highlighting, and drilling down\n", 328 | "\n", 329 | "2. **Microsoft Power BI:**\n", 330 | " - A business analytics service that provides interactive visualizations and business intelligence capabilities\n", 331 | " - Offers a user-friendly interface for creating interactive dashboards and reports\n", 332 | " - Allows users to interact with data through slicers, filters, and cross-filtering between visuals\n", 333 | "\n", 334 | "3. **Plotly:**\n", 335 | " - A web-based plotting library for creating interactive and publication-quality visualizations\n", 336 | " - Supports various programming languages, including Python, R, and JavaScript\n", 337 | " - Enables the creation of interactive charts, 3D plots, and animations with hover effects and zooming capabilities\n", 338 | "\n", 339 | "4. **Dash:**\n", 340 | " - A Python framework for building analytical web applications and dashboards\n", 341 | " - Integrates with Plotly for creating interactive and reactive visualizations\n", 342 | " - Allows for the development of full-stack data visualization applications with user interactions and real-time updates\n", 343 | "\n", 344 | "5. **Bokeh:**\n", 345 | " - A Python library for creating interactive visualizations in web browsers\n", 346 | " - Provides a flexible and powerful way to create interactive plots, dashboards, and data applications\n", 347 | " - Supports interactive tools like hover tooltips, pan and zoom, and selection and filtering\n" 348 | ] 349 | }, 350 | { 351 | "cell_type": "markdown", 352 | "metadata": {}, 353 | "source": [ 354 | "" 355 | ] 356 | }, 357 | { 358 | "cell_type": "markdown", 359 | "metadata": {}, 360 | "source": [ 361 | "### [Importance of Choosing the Right Tool for the Job](#toc0_)\n" 362 | ] 363 | }, 364 | { 365 | "cell_type": "markdown", 366 | "metadata": {}, 367 | "source": [ 368 | "With the plethora of data visualization tools available, it's crucial to select the one that best suits your specific needs and requirements. Consider the following factors when choosing a tool:\n", 369 | "\n", 370 | "- Static vs. interactive visualization requirements\n", 371 | "- Ease of use and learning curve\n", 372 | "- Flexibility and customization options\n", 373 | "- Compatibility with your data sources and formats\n", 374 | "- Scalability and performance for handling large datasets\n", 375 | "- Integration with your existing workflow and technology stack\n", 376 | "- Cost and licensing considerations\n" 377 | ] 378 | }, 379 | { 380 | "cell_type": "markdown", 381 | "metadata": {}, 382 | "source": [ 383 | "### [Emerging Trends and Future Directions](#toc0_)\n" 384 | ] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": {}, 389 | "source": [ 390 | "As technology advances, the field of data visualization continues to evolve. Here are some emerging trends and future directions:\n", 391 | "\n", 392 | "1. **Interactive and Immersive Visualizations:**\n", 393 | " - The increasing popularity of interactive visualizations that allow users to explore and manipulate data dynamically\n", 394 | " - The rise of virtual reality (VR) and augmented reality (AR) technologies for creating immersive data experiences\n", 395 | "\n", 396 | "2. **Real-time and Streaming Data Visualization:**\n", 397 | " - The need for tools that can handle real-time data streams and provide live updates\n", 398 | " - The development of dashboards and monitoring systems that enable real-time decision-making\n", 399 | "\n", 400 | "3. **AI-Powered Visualizations:**\n", 401 | " - The integration of artificial intelligence and machine learning techniques into data visualization tools\n", 402 | " - The use of AI to automatically generate insights, suggest optimal chart types, and assist in data storytelling\n", 403 | "\n", 404 | "4. **Collaborative and Cloud-Based Platforms:**\n", 405 | " - The growth of cloud-based data visualization platforms that enable collaboration and sharing\n", 406 | " - The ability to access and update visualizations from anywhere, on any device\n" 407 | ] 408 | }, 409 | { 410 | "cell_type": "markdown", 411 | "metadata": {}, 412 | "source": [ 413 | "By staying up-to-date with the latest tools and technologies, you can create data visualizations that are not only informative but also engaging and impactful. Remember to choose the right tool for the job, consider emerging trends, and continuously explore new possibilities in the ever-evolving field of data visualization." 414 | ] 415 | }, 416 | { 417 | "cell_type": "markdown", 418 | "metadata": {}, 419 | "source": [ 420 | "## [Case Studies and Examples](#toc0_)" 421 | ] 422 | }, 423 | { 424 | "cell_type": "markdown", 425 | "metadata": {}, 426 | "source": [ 427 | "Explore the following real-world examples and case studies that showcase the power and potential of data visualization across various domains:\n", 428 | "\n", 429 | "1. COVID-19 Dashboard by Johns Hopkins University\n", 430 | " - Link: [https://coronavirus.jhu.edu/map.html](https://coronavirus.jhu.edu/map.html)\n", 431 | " - Johns Hopkins University created an interactive dashboard that visualizes the global spread of COVID-19 in real-time.\n", 432 | " - The dashboard combines data from multiple sources to provide a comprehensive view of the pandemic, allowing users to explore the data at different levels of granularity.\n", 433 | " - It has become a go-to resource for researchers, policymakers, and the general public, helping to inform critical decisions about public health interventions.\n", 434 | "\n", 435 | "2. Gapminder's World Health Chart\n", 436 | " - Link: [https://www.gapminder.org/tools/#$chart-type=bubbles](https://www.gapminder.org/tools/#$chart-type=bubbles)\n", 437 | " - Gapminder's interactive bubble chart visualizes global health data, allowing users to explore the relationship between life expectancy, fertility rate, and income per capita for countries worldwide.\n", 438 | " - The visualization's dynamic and engaging design has made it a popular tool for educators, researchers, and policymakers, helping to promote a fact-based understanding of global development trends.\n", 439 | "\n", 440 | "3. NASA's Climate Time Machine\n", 441 | " - Link: [https://climate.nasa.gov/interactives/climate-time-machine](https://climate.nasa.gov/interactives/climate-time-machine)\n", 442 | " - NASA's Climate Time Machine is an interactive visualization that allows users to explore how key climate indicators, such as sea level, carbon dioxide concentration, and global temperature, have changed over time.\n", 443 | " - The visualization's intuitive design and compelling animations help to communicate the urgency of the climate crisis and the need for action to a broad audience.\n" 444 | ] 445 | }, 446 | { 447 | "cell_type": "markdown", 448 | "metadata": {}, 449 | "source": [ 450 | "These case studies and examples demonstrate the diverse applications and impact of data visualization across fields like public health, business, journalism, education, and environmental science. By exploring these projects and following the provided links, you can gain inspiration and practical insights for your own visualization work." 451 | ] 452 | }, 453 | { 454 | "cell_type": "markdown", 455 | "metadata": {}, 456 | "source": [ 457 | "## [Conclusion](#toc0_)" 458 | ] 459 | }, 460 | { 461 | "cell_type": "markdown", 462 | "metadata": {}, 463 | "source": [ 464 | "Throughout this lecture, we have explored the importance and power of data visualization in today's data-driven world. We have seen how visual representations can help us make sense of complex information, uncover insights, and communicate ideas more effectively.\n" 465 | ] 466 | }, 467 | { 468 | "cell_type": "markdown", 469 | "metadata": {}, 470 | "source": [ 471 | "Let's recap some of the key points we covered:\n", 472 | "\n", 473 | "- Data visualization is a critical tool for turning raw data into meaningful and actionable insights.\n", 474 | "- The human brain is wired to process visual information quickly and efficiently, making data visualization a powerful means of communication.\n", 475 | "- Effective data visualization offers numerous benefits, including facilitating data exploration, identifying patterns and trends, enabling better decision-making, enhancing collaboration, and creating compelling presentations.\n", 476 | "- However, creating effective visualizations requires careful consideration and avoiding common pitfalls, such as misleading representations, overcomplexity, and lack of context.\n", 477 | "- Real-world case studies and examples demonstrate the wide-ranging applications and impact of data visualization across various domains, from public health and climate science to journalism and business.\n" 478 | ] 479 | }, 480 | { 481 | "cell_type": "markdown", 482 | "metadata": {}, 483 | "source": [ 484 | "As we conclude this lecture, I encourage you to incorporate data visualization into your own work, whether you are a researcher, analyst, journalist, or business professional. By harnessing the power of visual communication, you can unlock new insights, make better decisions, and tell more compelling stories with your data.\n" 485 | ] 486 | }, 487 | { 488 | "cell_type": "markdown", 489 | "metadata": {}, 490 | "source": [ 491 | "To support you in your data visualization journey, here are some valuable resources for further learning and exploration:\n", 492 | "\n", 493 | "- \"Storytelling with Data\" by Cole Nussbaumer Knaflic - A practical guide to creating effective data visualizations and communicating insights with clarity and impact.\n", 494 | "- \"Data Visualization: A Practical Introduction\" by Kieran Healy - A comprehensive introduction to the principles and techniques of data visualization using R and ggplot2.\n", 495 | "- Tableau Public Gallery - A collection of inspiring data visualization examples created with Tableau, covering a wide range of topics and industries: [https://public.tableau.com/en-us/gallery/?tab=viz-of-the-day&type=viz-of-the-day](https://public.tableau.com/en-us/gallery/?tab=viz-of-the-day&type=viz-of-the-day)\n", 496 | "- D3.js - A powerful JavaScript library for creating interactive and dynamic data visualizations for the web: [https://d3js.org/](https://d3js.org/)\n", 497 | "- Flowing Data - A blog by data visualization expert Nathan Yau, featuring tutorials, examples, and insights on the latest trends and techniques in data visualization: [https://flowingdata.com/](https://flowingdata.com/)\n" 498 | ] 499 | }, 500 | { 501 | "cell_type": "markdown", 502 | "metadata": {}, 503 | "source": [ 504 | "Remember, the key to effective data visualization is to always keep your audience and purpose in mind, strive for clarity and simplicity, and let the data tell its story. By following these principles and continuously learning and experimenting, you can harness the full potential of data visualization to inform, inspire, and drive change.\n" 505 | ] 506 | } 507 | ], 508 | "metadata": { 509 | "language_info": { 510 | "name": "python" 511 | } 512 | }, 513 | "nbformat": 4, 514 | "nbformat_minor": 2 515 | } 516 | -------------------------------------------------------------------------------- /Lectures/01 Introduction to Data Visualization/images/banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytopia/Data-Visualization/8d7371fdf1e265862f96ca47b5451b3b9180a4b1/Lectures/01 Introduction to Data Visualization/images/banner.png -------------------------------------------------------------------------------- /Lectures/01 Introduction to Data Visualization/images/bokeh.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 2 | 3 | ![GitHub last commit](https://img.shields.io/github/last-commit/pytopia/data-visualization) 4 | ![GitHub repo size](https://img.shields.io/github/repo-size/pytopia/data-visualization) 5 | ![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/pytopia/data-visualization) 6 | ![GitHub Repo stars](https://img.shields.io/github/stars/pytopia/data-visualization) 7 | ![GitHub top language](https://img.shields.io/github/languages/top/pytopia/data-visualization) 8 | [![Website](https://img.shields.io/badge/Visit-Website-blue)](https://www.pytopia.ai) 9 | [![Telegram](https://img.shields.io/badge/Join-Telegram-blue)](https://t.me/pytopia_ai) 10 | [![Instagram](https://img.shields.io/badge/Follow-Instagram-red)](https://instagram.com/pytopia.ai) 11 | [![YouTube](https://img.shields.io/badge/Subscribe-YouTube-red)](https://www.youtube.com/@pytopia) 12 | [![LinkedIn](https://img.shields.io/badge/Follow-LinkedIn-blue)](https://linkedin.com/company/pytopia) 13 | [![Twitter](https://img.shields.io/badge/Follow-Twitter-blue)](https://twitter.com/pytopia_ai) 14 | 15 | Welcome to the Data Visualization for Machine Learning course repository! This practical course is designed to equip you with the essential skills and techniques for creating compelling and informative visualizations that are crucial in the field of Machine Learning. Whether you're a beginner looking to enhance your data visualization capabilities or an experienced practitioner seeking to leverage visualizations for better insights, this course has something to offer. 16 | 17 | # 🎯 Course Objectives 18 | 19 | By the end of this course, you will: 20 | 21 | - Understand the fundamental principles and best practices of data visualization 22 | - Master the popular data visualization libraries: Matplotlib, Seaborn, and Pandas 23 | - Create a wide range of visualizations, from basic plots to advanced statistical graphics 24 | - Learn how to customize and fine-tune your visualizations for maximum impact 25 | - Gain hands-on experience through real-world projects that simulate Machine Learning scenarios 26 | - Discover how effective data visualization can enhance your Machine Learning workflow 27 | 28 | # 📚 Course Contents 29 | 30 | The course is divided into the following chapters: 31 | 32 | 1. Introduction to Data Visualization 33 | 2. Getting Started with Matplotlib 34 | 3. Basic Plotting with Matplotlib 35 | 4. Advanced Matplotlib Concepts 36 | 5. Introduction to Seaborn 37 | 6. Statistical Plotting with Seaborn 38 | 7. Customizing Seaborn Plots 39 | 8. Data Visualization with Pandas 40 | 9. Real-world Projects 41 | 42 | Each chapter includes a combination of theoretical explanations, practical examples, and hands-on exercises to reinforce your understanding of the concepts and their applications in Machine Learning. The real-world projects in the final chapter provide you with the opportunity to apply your data visualization skills to realistic Machine Learning scenarios, giving you valuable experience and a portfolio of visualizations to showcase. 43 | 44 | # ✅ Prerequisites 45 | 46 | To get the most out of this course, you should have: 47 | 48 | - Basic knowledge of Python programming 49 | - Basic familiarity with data manipulation using libraries like NumPy and Pandas 50 | - Enthusiasm to explore the power of data visualization in Machine Learning! 51 | 52 | # 📚 Learn with Us! 53 | We also offer a [course on these contents](https://www.pytopia.ai/courses/data-visualization) where learners can interact with peers and instructors, ask questions, and participate in online coding sessions. By registering for the course, you also gain access to our dedicated Telegram group. Enroll now and start learning! Here are some useful links: 54 | 55 | - [Data Visualization Course](https://www.pytopia.ai/courses/data-visualization) 56 | - [Pytopia Public Telegram Group](https://t.me/pytopia_ai) 57 | - [Pytopia Website](https://www.pytopia.ai/) 58 | 59 | [](https://www.pytopia.ai/courses/data-visualization) 60 | 61 | # 🚀 Getting Started 62 | 63 | To get started with the course, follow these steps: 64 | 65 | 1. Clone this repository to your local machine using the following command: 66 | ``` 67 | git clone https://github.com/pytopia/data-visualization.git 68 | ``` 69 | 70 | 2. Navigate to the cloned repository: 71 | ``` 72 | cd data-visualization 73 | ``` 74 | 75 | 3. Set up the required dependencies and environment by following the instructions in the `setup.md` file. 76 | 77 | 4. Start exploring the course materials, beginning with the first chapter. 78 | 79 | Throughout the course, you will learn how to create visually appealing and informative plots, charts, and graphs that can help you gain insights from your data, communicate findings effectively, and enhance your Machine Learning models. By the end of this course, you will have a strong foundation in data visualization techniques and be able to apply them confidently in your Machine Learning projects. 80 | 81 | # 📞 Contact Information 82 | 83 | Feel free to reach out to us! 84 | 85 | - 🌐 Website: [pytopia.ia](https://www.pytopia.ai) 86 | - 💬 Telegram: [pytopia_ai](https://t.me/pytopia_ai) 87 | - 🎥 YouTube: [pytopia](https://www.youtube.com/@pytopia) 88 | - 📸 Instagram: [pytopia.ai](https://www.instagram.com/pytopia.ai) 89 | - 🎓 LinkedIn: [pytopia](https://www.linkedin.com/in/pytopia) 90 | - 🐦 Twitter: [pytopia_ai](https://twitter.com/pytopia_ai) 91 | - 📧 Email: [pytopia.ai@gmail.com](mailto:pytopia.ai@gmail.com) 92 | -------------------------------------------------------------------------------- /images/banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytopia/Data-Visualization/8d7371fdf1e265862f96ca47b5451b3b9180a4b1/images/banner.png -------------------------------------------------------------------------------- /images/pytopia-course.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pytopia/Data-Visualization/8d7371fdf1e265862f96ca47b5451b3b9180a4b1/images/pytopia-course.png --------------------------------------------------------------------------------