<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Dennis Ganzaroli</title>
    <description>The latest articles on DEV Community by Dennis Ganzaroli (@deganza).</description>
    <link>https://dev.to/deganza</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F1233957%2F217cfdb3-cc75-48d0-90d1-1e8d8aa543ac.jpeg</url>
      <title>DEV Community: Dennis Ganzaroli</title>
      <link>https://dev.to/deganza</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/deganza"/>
    <language>en</language>
    <item>
      <title>new Book: The Visual Data Language (The KNIME Way)</title>
      <dc:creator>Dennis Ganzaroli</dc:creator>
      <pubDate>Thu, 21 Aug 2025 07:37:11 +0000</pubDate>
      <link>https://dev.to/deganza/new-book-the-visual-data-language-the-knime-way-2f9j</link>
      <guid>https://dev.to/deganza/new-book-the-visual-data-language-the-knime-way-2f9j</guid>
      <description>&lt;p&gt;My 𝗯𝗼𝗼𝗸 📖 is now out as a 𝗽𝗮𝗽𝗲𝗿𝗯𝗮𝗰𝗸 (also as Kindle and KU)!&lt;br&gt;
👉 check it out here: &lt;a href="https://mybook.to/thevisualdatalanguage" rel="noopener noreferrer"&gt;https://mybook.to/thevisualdatalanguage&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;🚀 𝗞𝗡𝗜𝗠𝗘 – the best kept secret in data science.&lt;br&gt;
I’m so happy to finally share it with you.&lt;br&gt;
I’m convinced this 𝗯𝗼𝗼𝗸 works, because it really 𝗵𝗲𝗹𝗽𝘀 𝗽𝗲𝗼𝗽𝗹𝗲 move forward.&lt;/p&gt;

&lt;p&gt;It contains a wide range of helpful 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 and explains the logic of the 𝘃𝗶𝘀𝘂𝗮𝗹 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 language for data analysis. It even shows how to build your own 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝗴𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 to work smarter.&lt;/p&gt;

&lt;p&gt;💡 A fresh way to think and work 𝗦𝗠𝗔𝗥𝗧𝗘𝗥 with data.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fedudwud8qyi576lbnzvw.png" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fedudwud8qyi576lbnzvw.png" alt=" " width="732" height="642"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;h1&gt;
  
  
  DataScience #KNIME #VisualDataScience #Analytics  #DataInfrastructure #BookLaunch #DataScienceBook
&lt;/h1&gt;

</description>
      <category>datascience</category>
      <category>dataviz</category>
      <category>books</category>
      <category>productivity</category>
    </item>
    <item>
      <title>The Visual Data Language</title>
      <dc:creator>Dennis Ganzaroli</dc:creator>
      <pubDate>Mon, 04 Aug 2025 12:27:05 +0000</pubDate>
      <link>https://dev.to/deganza/the-visual-data-language-5h2c</link>
      <guid>https://dev.to/deganza/the-visual-data-language-5h2c</guid>
      <description>&lt;p&gt;Coming this month!&lt;br&gt;
A book for data folks, by a data person.&lt;br&gt;
Your guide to success in the data world — packed with practical tips for Data Engineering, Data Science,AI &amp;amp; KNIME.&lt;br&gt;
Stay tuned!&lt;/p&gt;

&lt;h1&gt;
  
  
  datascience #dataengineering #knime #ai #ml #booklaunch
&lt;/h1&gt;

</description>
      <category>datascience</category>
      <category>dataengineering</category>
      <category>ai</category>
      <category>knime</category>
    </item>
    <item>
      <title>KNIME &amp; fbProphet: Time Series Forecasting with a few clicks</title>
      <dc:creator>Dennis Ganzaroli</dc:creator>
      <pubDate>Fri, 05 Jul 2024 10:10:44 +0000</pubDate>
      <link>https://dev.to/deganza/knime-fbprophet-time-series-forecasting-with-a-few-clicks-kl5</link>
      <guid>https://dev.to/deganza/knime-fbprophet-time-series-forecasting-with-a-few-clicks-kl5</guid>
      <description>&lt;p&gt;Build a sophisticated time series forecast with a few clicks using a component in KNIME with Facebook Prophet.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://medium.com/low-code-for-advanced-data-science/knime-fbprophet-time-series-forecasting-with-a-few-clicks-4d527460ba8e"&gt;https://medium.com/low-code-for-advanced-data-science/knime-fbprophet-time-series-forecasting-with-a-few-clicks-4d527460ba8e&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media.dev.to/cdn-cgi/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F1gjill8snljjitp93981.jpg" class="article-body-image-wrapper"&gt;&lt;img src="https://media.dev.to/cdn-cgi/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F1gjill8snljjitp93981.jpg" alt="Image description" width="800" height="563"&gt;&lt;/a&gt;&lt;/p&gt;

</description>
      <category>fbprophet</category>
      <category>timeseries</category>
      <category>knime</category>
      <category>python</category>
    </item>
    <item>
      <title>Recreating Minard’s greatest ever Chart with KNIME’s K-AI and Python</title>
      <dc:creator>Dennis Ganzaroli</dc:creator>
      <pubDate>Wed, 24 Jan 2024 10:26:09 +0000</pubDate>
      <link>https://dev.to/deganza/recreating-minards-greatest-ever-chart-with-knimes-k-ai-and-python-294l</link>
      <guid>https://dev.to/deganza/recreating-minards-greatest-ever-chart-with-knimes-k-ai-and-python-294l</guid>
      <description>&lt;h1&gt;
  
  
  Prompting with #KAI and #Python one of the best #dataviz of all time:
&lt;/h1&gt;

&lt;p&gt;Minard’s #map of #Napoleon’s lost #campaign in Russia in 1812.&lt;/p&gt;

&lt;p&gt;Read my new article for free on #medium:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://medium.com/low-code-for-advanced-data-science/recreating-minards-greatest-ever-chart-with-knime-s-k-ai-and-python-f75e86117e51"&gt;https://medium.com/low-code-for-advanced-data-science/recreating-minards-greatest-ever-chart-with-knime-s-k-ai-and-python-f75e86117e51&lt;/a&gt;&lt;/p&gt;

&lt;h1&gt;
  
  
  datascience #dataviz #maps #ai #KAI #historicalmaps #dataanalyst #KNIME #lowcode #nocode #opensource #visualprogramming
&lt;/h1&gt;

</description>
    </item>
    <item>
      <title>K-AI powered Python Code Generation in KNIME</title>
      <dc:creator>Dennis Ganzaroli</dc:creator>
      <pubDate>Mon, 08 Jan 2024 10:38:40 +0000</pubDate>
      <link>https://dev.to/deganza/k-ai-powered-python-code-generation-in-knime-2dci</link>
      <guid>https://dev.to/deganza/k-ai-powered-python-code-generation-in-knime-2dci</guid>
      <description>&lt;p&gt;Now you can ask #KNIME directly what you want.&lt;br&gt;
Read my new article about:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://medium.com/low-code-for-advanced-data-science/k-ai-powered-python-code-generation-in-knime-and-the-evaluation-of-sports-scoring-systems-76de7399409f"&gt;https://medium.com/low-code-for-advanced-data-science/k-ai-powered-python-code-generation-in-knime-and-the-evaluation-of-sports-scoring-systems-76de7399409f&lt;/a&gt;&lt;/p&gt;

&lt;h1&gt;
  
  
  Python #KAI #AI #GenAI #datascience #ai #python #artificialassistant #sportratings #ratingsystems #tennis #soccer #KNIME #lowcode #nocode #opensource #visualprogramming
&lt;/h1&gt;

</description>
    </item>
    <item>
      <title>Install TensorFlow on Mac M1/M2 with GPU support</title>
      <dc:creator>Dennis Ganzaroli</dc:creator>
      <pubDate>Mon, 18 Dec 2023 05:49:34 +0000</pubDate>
      <link>https://dev.to/deganza/install-tensorflow-on-mac-m1m2-with-gpu-support-2nf</link>
      <guid>https://dev.to/deganza/install-tensorflow-on-mac-m1m2-with-gpu-support-2nf</guid>
      <description>&lt;p&gt;Install TensorFlow on Mac M1/M2 with GPU support&lt;br&gt;
and benefit from the native performance of the new Mac ARM64 architecture. &lt;/p&gt;

&lt;p&gt;What makes the Macs M1 and the new M2 stand out is not only their outstanding performance, but also the extremely low power consumption. &lt;br&gt;
In a world where energy consumption is becoming more critical every day, efficient use of resources must also be a priority.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580"&gt;https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580&lt;/a&gt;&lt;/p&gt;

&lt;h1&gt;
  
  
  deeplearning #tensorflow #AppleM1 #gpu #JupyterNotebook #datascience #energy #python
&lt;/h1&gt;

</description>
      <category>deeplearning</category>
      <category>macm1</category>
      <category>python</category>
      <category>datascience</category>
    </item>
    <item>
      <title>A Data Engineer’s Cheat Sheet on Pandas and Jupyter-Notebooks</title>
      <dc:creator>Dennis Ganzaroli</dc:creator>
      <pubDate>Sun, 17 Dec 2023 09:23:39 +0000</pubDate>
      <link>https://dev.to/deganza/a-data-engineers-cheat-sheet-on-pandas-and-jupyter-notebooks-50h7</link>
      <guid>https://dev.to/deganza/a-data-engineers-cheat-sheet-on-pandas-and-jupyter-notebooks-50h7</guid>
      <description>&lt;p&gt;The Pandas 101 for Data Engineer&lt;br&gt;
Learn the basic commands to use#Pandas in #JupyterNotebook to accomplish the most important Data Enginnering tasks.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://medium.com/geekculture/the-basics-of-a-jupyter-notebook-pandas-cheat-sheet-for-data-engineers-c1099791a93c"&gt;https://medium.com/geekculture/the-basics-of-a-jupyter-notebook-pandas-cheat-sheet-for-data-engineers-c1099791a93c&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/deganza/jupyter_pandas_cheat_sheet"&gt;https://github.com/deganza/jupyter_pandas_cheat_sheet&lt;/a&gt;&lt;/p&gt;

&lt;h1&gt;
  
  
  cheatsheet #python #pandasdataframe #knime #ETL #dataengineering #JupyterNotebook #opensource
&lt;/h1&gt;

</description>
      <category>cheatsheet</category>
      <category>pandasdataframe</category>
      <category>python</category>
      <category>dataengineering</category>
    </item>
    <item>
      <title>Automate your GitHub Stats Reporting with KNIME</title>
      <dc:creator>Dennis Ganzaroli</dc:creator>
      <pubDate>Fri, 15 Dec 2023 10:02:22 +0000</pubDate>
      <link>https://dev.to/deganza/automate-your-github-stats-reporting-with-knime-393c</link>
      <guid>https://dev.to/deganza/automate-your-github-stats-reporting-with-knime-393c</guid>
      <description>&lt;h1&gt;
  
  
  Automate your #GitHub #Reporting with #KNIME for free and gain valuable insights into your  #Repository performance.
&lt;/h1&gt;

&lt;p&gt;&lt;a href="https://medium.com/low-code-for-advanced-data-science/automate-your-github-stats-reporting-with-knime-5ada3ffde3eb"&gt;https://medium.com/low-code-for-advanced-data-science/automate-your-github-stats-reporting-with-knime-5ada3ffde3eb&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;#opensource  #DataAnalytics  #API&lt;/p&gt;

</description>
      <category>github</category>
      <category>reporting</category>
      <category>api</category>
      <category>knime</category>
    </item>
  </channel>
</rss>
