The hottest Data Visualization Substack posts right now

And their main takeaways
Category
Top Technology Topics

Boosting Data Visualization Productivity With ChatGPT, Python & Streamlit

Data at Depth • 0 implied HN points • 03 May 23
🕹 Technology Data Visualization Python Productivity
  1. Using ChatGPT and Python with Streamlit can help beginners create data visualization applications easily.
  2. Even without experience, individuals can leverage ChatGPT to generate Python code for creating maps and charts.
  3. Consider trying the 7-day free trial to access more content on boosting data visualization productivity with ChatGPT, Python, and Streamlit.

Python Data Visualization Mastery: Selecting the Right Tool for Powerful Storytelling

Data at Depth • 0 implied HN points • 02 May 23
🕹 Technology Data Visualization
  1. In the era of big data, it's crucial to present complex information clearly and engagingly.
  2. Choosing the right data visualization techniques is key to telling a compelling story that captivates your audience.
  3. Consider using stacked area charts to visualize data, such as the top-5 CO2 emitting countries globally, to create impactful visual narratives.
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ChatGPT & Python Accelerated: 5 Lessons Learned for Quicker Data Visualizations

Data at Depth • 0 implied HN points • 29 Apr 23
🕹 Technology Data Visualization ChatGPT Python Coding
  1. The post discusses lessons for quicker data visualizations using ChatGPT and Python, showcasing a rapid visualization created by ChatGPT-4 in Python and plotly.
  2. The author shares insights gained through a month of ChatGPT prompt engineering, highlighting practical experience in coding with Python libraries.
  3. Readers can access more content by subscribing to Data at Depth, and can start with a 7-day free trial for full post archives.

Boosting Your Productivity with ChatGPT: Data Visualizations in Python.

Data at Depth • 0 implied HN points • 14 Apr 23
🕹 Technology Data Analysis Data Visualization Python
  1. A tool like ChatGPT can help visualize data by finding datasets, conducting analysis, and generating code for visualization.
  2. Python is essential as middleware to help ChatGPT in visualizing data effectively.
  3. Utilize a 7-day free trial of Data at Depth to access full post archives and learn more about boosting productivity with data visualizations in Python.

Re-evaluating Comp Sci Education: Scraping And Visualizing Data in Python With ChatGPT

Data at Depth • 0 implied HN points • 06 Apr 23
🕹 Technology Programming Data Visualization
  1. The use of ChatGPT has revolutionized the delivery and assessment of computer programming curriculum for professors in Computer Science education.
  2. Scraping and visualizing data in Python is an essential skill that can be enhanced with tools like ChatGPT.
  3. Access to quality curriculum resources has been significantly improved through the use of ChatGPT in education.

Bulk Data Discovery

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 25 Jan 24
🕹 Technology Data science Machine Learning AI Development Data Visualization Open Source
  1. Data discovery is crucial for understanding unstructured data. It helps find user intent and classifies interactions effectively.
  2. Using embeddings allows us to visualize data by grouping similar meanings. This helps spot patterns and outliers in conversations.
  3. Data preparation involves identifying, collecting, and analyzing data. This step helps reveal valuable insights that support decision-making.

[In case you missed it] Data Science Weekly - Issue 462

Data Science Weekly Newsletter • 0 implied HN points • 02 Oct 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Programming
  1. Teaching students about scientific failure is important. It helps them understand resilience and learn from mistakes.
  2. AI systems are advancing rapidly, with new tools like video generation from text prompts. This opens up new opportunities for creators.
  3. Understanding uncertainties in deep learning is key for improving model performance. It helps practitioners make better decisions.

[in case you missed it] Data Science Weekly - Issue 460

Data Science Weekly Newsletter • 0 implied HN points • 18 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Data Visualization
  1. Data scientists need soft skills like communication and teamwork. These skills help them work better with others and tell stories from data.
  2. There's a lot of free, live-streamed data science content available on Twitch. This makes it easier for everyone to learn and connect with the data science community.
  3. Understanding how to use AI tools for content generation can open up new creative possibilities. These tools can help enhance projects in various ways.

[in case you missed it] Data Science Weekly - Issue 443

Data Science Weekly Newsletter • 0 implied HN points • 22 May 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. There's a new initiative where you can share what you're up to, and they might include your story in the newsletter. It's a nice way to connect with others in the data science community.
  2. There's a focus on improving software development skills for data scientists by following best practices like version control and automatic testing. This can help teams work better together.
  3. AI-generated art is being debated, with some arguing it's just imitation and not true art. It raises questions about the value of creativity and human experience in art.

[in case you missed it ] Data Science Weekly - Issue 436

Data Science Weekly Newsletter • 0 implied HN points • 03 Apr 22
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. Aggregating data too much can hide important details. It's better to keep the complexity to find new insights.
  2. Waymo is testing fully autonomous cars in San Francisco. This shows how self-driving technology is becoming part of everyday life.
  3. Graph Neural Networks can handle missing information in data efficiently. They help make better use of connected data even when some details are missing.

[in case you missed it] Data Science Weekly - Issue 421

Data Science Weekly Newsletter • 0 implied HN points • 19 Dec 21
🕹 Technology Data science Machine Learning AI Development Data Visualization Research
  1. Lee Wilkinson made big contributions to how we visualize data, helping us understand graphics better.
  2. A new journal for machine learning research will use a transparent review process to improve scholarly communication.
  3. Feature engineering is still important in data science despite the rise of deep learning, showing that sometimes traditional methods still apply.

[in case you missed it] Data Science Weekly - Issue 411

Data Science Weekly Newsletter • 0 implied HN points • 10 Oct 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Statistical Analysis
  1. Freelancing in data visualization can be tricky. It's important to learn from mistakes and adjust strategies for better outcomes.
  2. Combining AI with art can bring lost masterpieces back to life. Using algorithms to mimic an artist's style can recreate vibrant colors in old, damaged artworks.
  3. Building a strong data team is essential for businesses. Companies need to focus on data strategy, governance, and analytics to harness the power of data effectively.

[in case you missed it] Data Science Weekly - Issue 400

Data Science Weekly Newsletter • 0 implied HN points • 25 Jul 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. A new documentary used AI to generate Anthony Bourdain's voice, raising questions about ethics in media. It's important to think about how technology like this affects what we perceive as real.
  2. Deep learning is becoming more effective despite challenges, and understanding its success can help bridge gaps between traditional statistics and modern AI. Bigger and deeper models often yield better results, even with less data.
  3. Combining different AI models, like Transformers and convolutional neural networks, can lead to better performance in tasks like image recognition. This shows that mixing approaches can help overcome the limitations of each technology on its own.

[in case you missed it] Data Science Weekly - Issue 399

Data Science Weekly Newsletter • 0 implied HN points • 18 Jul 21
🕹 Technology Data science Machine Learning AI Analytics Data Visualization
  1. There's a growing movement called 'Data for Good', which focuses on using data to help improve society. It's important to understand the different groups and initiatives within this space.
  2. Peer review in data science is crucial, especially for startups, but the process can be tricky. It's good to learn from experiences about what works and what doesn't.
  3. Big companies like Amazon collect a lot of data about their users, often more than people realize. It's important to be aware of how this data is being tracked and used.

[in case you missed it] Data Science Weekly - Issue 388

Data Science Weekly Newsletter • 0 implied HN points • 02 May 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization
  1. Cluster analysis can be tricky since you often don't know how many groups to create. A new method called clustergram helps visualize data better as you adjust the number of clusters.
  2. Bayesian and frequentist methods in statistics provide different types of results, so they shouldn't be compared directly. They answer different questions rather than yielding similar outputs.
  3. Netflix is working on a feature called 'Play Something' to combat decision fatigue. This feature plays a show automatically, similar to turning on a TV, making it easier for users to start watching.

[in case you missed it] Data Science Weekly - Issue 381

Data Science Weekly Newsletter • 0 implied HN points • 14 Mar 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Computer Vision
  1. Data sharing in Africa faces challenges due to issues like historical power imbalances and Western-centric policies. It's important to recognize these factors when discussing data access and usage.
  2. Machine learning models can struggle when tested on data that is different from what they were trained on. Research is being done to improve how these models generalize to new situations.
  3. New tools like Dolt combine Git and MySQL to help data scientists collaborate better on datasets. This makes it easier for teams to work together without overwriting each other's changes.

[in case you missed it] Data Science Weekly - Issue 315

Data Science Weekly Newsletter • 0 implied HN points • 07 Dec 19
🕹 Technology AI Machine Learning Data science Neural Networks Data Visualization
  1. AI technology is helping scientists study animals better, but it's also creating a lot of data that needs managing. There are smart solutions emerging to help handle this data overload.
  2. Machine learning platforms are still quite complicated and unique, making it hard for researchers to reproduce results. There's a need for more simplicity and standardization in these tools.
  3. Recent studies using machine learning have uncovered new insights into classic literature, revealing which parts of Shakespeare's plays may have been written by others. This shows the power of AI in analyzing texts.

Integrations, Part III

Pine • 0 implied HN points • 19 Sep 24
🕹 Technology Software Web Development Programming User Interface Data Visualization
  1. Pine now allows frontend extensions to show info from other tools directly in its interface. This means users can see more useful data without leaving the app.
  2. Creating these extensions just needs basic knowledge of JavaScript, HTML, and CSS. It's great for beginners to start coding and making their own tools.
  3. The server library names have been updated for clarity. This helps users understand which library to use for client-side versus backend work.