Tag: Data Science

Newsletter: Data & AI Digest #2

Generated with DALL-E
Generated with DALL-E

👋 Hello Data & AI Enthusiasts,

Welcome to another edition of the Data & AI Digest! We’re excited to bring you a curated selection of the week’s most compelling stories in the realm of data science, artificial intelligence, and more. Whether you’re a seasoned expert or a curious beginner, there’s something here for everyone.

  1. [AI] Understanding AI Performance: Discover how modern AI models often match or exceed human capabilities in tests, yet struggle in real-world applications. Read more
  2. [AI] Generative AI Strategy for Tech Leaders: CIOs and CTOs need to integrate generative AI into their tech architecture effectively. Explore 5 key elements for successful implementation. Read more
  3. [Statistics] Mastering the Central Limit Theorem in R: Understand the Central Limit Theorem, a cornerstone in statistics, and learn how to simulate it using R in this step-by-step tutorial. Read more
  4. [Graph Theory] Comprehensive Introduction to Graph Theory: This quarter-long course covers everything from simple graphs to Eulerian circuits and spanning trees. Read more
  5. [SQL] SQL Konferenz Highlights on Microsoft Fabric: Get an in-depth look at Microsoft Fabric and its role as a Data Platform for the Era of AI. Read more
  6. [Microsoft] Forbes Insights on Microsoft’s Copilots: Learn six critical things every business owner should know about Microsoft Copilot. Read more
  7. [GitHub] How GitHub’s Copilot is Being Used: GitHub’s Copilot remains the most popular AI-based code completion service. Find out the latest usage trends. Read more
  8. [Apple] iPhone 15 Pro’s Spatial Videos: Teased at Apple’s latest keynote, learn about the new spatial video capabilities of the iPhone 15 Pro. Read more
  9. [Geopolitics] China’s AI Influence Campaign: Researchers from Microsoft and other organizations discuss Beijing’s rapid change in disinformation tactics through AI. Read more

That’s a wrap for this week’s Data & AI Digest! We hope you found these articles insightful and thought-provoking. If you enjoyed this issue, help us make it bigger and better by sharing it with colleagues and friends. 🚀

Don’t forget, for real-time updates and discussions, join our LinkedIn Data & AI User Group. We look forward to your active participation and valuable insights.

Get the Data & AI Digest newsletter delivered to your email weekly.

Until next week, happy reading and exploring!

Newsletter: Data & AI Digest #1

Generated with DALL-E
Generated with DALL-E

Hello and welcome to the first issue of Data & AI Digest! We’ve curated an exceptional list of articles that delve into a wide array of topics—from the triumphs of data visualization in public health to the ethical dilemmas surrounding AI-generated images. Are you curious about running Python directly in Excel? We’ve got you covered!

  • [dataviz] The Triumph Over Tobacco: A Public Health Milestone: Explore how a blend of regulation, taxation, and education led to a significant decline in cigarette sales and lung cancer deaths in the U.S. Read more
  • [dataviz] Master the Art of Data Visualization with These Must-Read Books: Whether you’re a novice or a pro, discover five essential books that guide you to data visualization mastery. Read more
  • [analytics] Python Meets Excel: A New Era in Data Analysis: Announcing Python support in Microsoft Excel, enabling data analysis directly within the Excel grid—no separate Python installation needed. Read more
  • [powerbi] Unlock Power BI’s Full Potential with DAX: Discover 20 essential DAX tricks to enhance your Power BI reports, suitable for both beginners and experts. Read more
  • [ethics] Controversy Over ‘SmashOrPassAI’ Site: A new site that allows users to rate AI-generated women sparks backlash, raising ethical concerns. Read more
  • [public opinion] Rising Concerns Over AI’s Role in Daily Life: A new Pew Research survey reveals growing apprehension among Americans about the role of AI in daily life, with views varying by age and use cases. Read more
  • [privacy] GDPR Complaint Against OpenAI Over ChatGPT: OpenAI faces allegations of GDPR violations regarding its ChatGPT model, as filed by a privacy researcher. Read more
  • [coding] Meet Code Llama: A New Large Language Model for Coding: Meta is introducing Code Llama, a state-of-the-art large language model designed to assist with coding tasks. Read more

We hope you find this week’s digest both informative and inspiring. Enjoy the newsletter? Help us make it bigger and better by sharing it with colleagues and friends. For even more curated content, discussions, and networking opportunities, don’t forget to join our LinkedIn Data & AI User Group.

Get the Data & AI Digest newsletter delivered to your email weekly.

Stay curious and keep innovating!

Introducing “Data & AI Digest”: Your Essential AI Newsletter for Weekly Insights on Data Science and Artificial Intelligence

Data & AI Digest - the AI newsletter (image generated with DALL-E)
Data & AI Digest – the AI newsletter (image generated with DALL-E)

Why You Can’t Afford to Miss the “Data & AI Digest”

In today’s fast-paced digital landscape, keeping up with the ever-changing worlds of Data Science and Artificial Intelligence (AI) can be challenging. From breakthroughs in machine learning algorithms to ethical debates surrounding AI, the scope of what you need to know is vast and continuously expanding.

Enter the Data & AI Digest, a weekly newsletter curated to keep you updated on the most pertinent news, articles, and discussions in the realms of data and AI. Whether you are a seasoned professional, a student, or simply an enthusiast, this newsletter is designed with you in mind.

Continue reading “Introducing “Data & AI Digest”: Your Essential AI Newsletter for Weekly Insights on Data Science and Artificial Intelligence”

#datamustread Data Viz Essentials: The Must-Read Books to Master Data Visualization

#DataVizEssentials 2023: The Must-Read Books to Master Data Visualization
#DataVizEssentials 2023: The Must-Read Books to Master Data Visualization

Building on the previous #datamustread recommendations, I’m excited to present the data viz edition of #datamustread. In this post, we’re focusing on the indispensable skill of data visualization. Whether you’re a beginner or a seasoned pro, these five books will guide you to mastery:

  1. 📖 The Big Book of Dashboards
  2. 📖 Storytelling with Data
  3. 📖 The Truthful Art
  4. 📖 Show Me the Numbers
  5. 📖 Teach Yourself VISUALLY Power BI

The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios

A comprehensive guide filled with real-world solutions for building effective business dashboards across various industries and platforms. It’s a go-to resource for matching great dashboards with real-world scenarios.

Storytelling with Data: A Data Visualization Guide for Business Professionals

Cole Nussbaumer Knaflic shares practical guidance on creating compelling data stories. Learn how to make your data visually appealing, engaging, and resonant with your audience.

Continue reading “#datamustread Data Viz Essentials: The Must-Read Books to Master Data Visualization”

Die Top 5 Bücher für erfolgreiche Data Science: Unverzichtbare Lektüre für angehende Data Scientists

Welche Data Science Bücher sollten Sie lesen um als Data Scientist erfolgreich zu sein? | Photo Credit: via Sebastian Sikora
Welche Data Science Bücher sollten Sie lesen um als Data Scientist erfolgreich zu sein? | Photo Credit: via Sebastian Sikora

Möchten Sie eine Karriere in Data Science verfolgen und fragen sich, welche Bücher Ihnen auf diesem Weg helfen können? In diesem Blogbeitrag präsentiere ich Ihnen die fünf entscheidenden Bücher, die für Ihre Ausbildung und Ihren beruflichen Werdegang in Data Science unerlässlich sind. 📚

1. Einstieg und Überblick: “Data Science in der Praxis”

Einstieg und Überblick: Data Science in der Praxis

Data Science in der Praxis von Tom Alby bietet Ihnen einen umfassenden Einstieg in die Welt der Daten. Dieses Buch vermittelt Ihnen nicht nur die Grundlagen von Data Science, sondern bietet auch praktische Beispiele und Fallstudien, die Ihnen helfen, das Gelernte anzuwenden und zu vertiefen.

2. Python Crashkurs: “Data Science mit Python”

Python Crashkurs: Data Science mit Python

Python ist die universelle Programmiersprache, die sich hervorragend zur Lösung von Data-Science-Fragestellungen eignet. Mit Data Science mit Python von Jake VanderPlas lernen Sie Python auf effiziente Weise und bereiten sich auf komplexere Data-Science-Aufgaben vor.

3. Der Statistikwerkzeugkasten: “Statistik I und II für Dummies”

Der Statistikwerkzeugkasten: Statistik I und II für Dummies

Statistik ist das Rückgrat von Data Science. Statistik für Dummies und Statisik II für Dummies von Deborah J. Rumsey bieten eine umfassende und leicht verständliche Einführung in die Statistik. Die Bücher decken eine Vielzahl von Themen ab, einschließlich Regression, Varianzanalyse, Chi-Quadrat-Tests und nichtparametrische Verfahren.

4. Für große Datenmengen: “Hadoop: The Definitive Guide”

Für große Datenmengen: Hadoop: The Definitive Guide

Hadoop: The Definitive Guide von Tom White ist unerlässlich, wenn Sie mit großen Datenmengen arbeiten. Dieses Buch führt Sie durch die Komplexitäten von Hadoop und hilft Ihnen, das Potenzial Ihrer Daten voll auszuschöpfen.

5. Mehrwert durch Data Science: “Data Science für Unternehmen”

Dieses #datamustread Buch von Foster Provost und Tom Fawcett zeigt auf, wie Data Science für Unternehmen Mehrwert schafft. Es erklärt die Grundlagen der Data Science und zeigt auf, wie Sie die Prinzipien auf reale Geschäftssituationen anwenden können.

Gewonnene Erkenntnisse visualisieren: Bücher zu Datenvisualisierung

Sie möchten Ihre mit Data Science gewonnenen Erkenntnisse visualisieren und interessieren sich für Bücher zum Thema Datenvisualisierung? Dann werfen Sie gerne einen Blick in meine Bücher!

Haben Sie andere Empfehlungen für Data Science Bücher? Teilen Sie Ihre Gedanken und Kommentare in diesem Tweet:

Continue reading “Die Top 5 Bücher für erfolgreiche Data Science: Unverzichtbare Lektüre für angehende Data Scientists”