TDSP: When Agile Meets Data Science
A practical guide to applying agile principles to data science projects- 27778Murphy ≡ DeepGuide
Understanding Noisy Data and Uncertainty in Machine Learning
The actual reason your machine learning model isn't working- 27392Murphy ≡ DeepGuide
3 Ways to Help CMOs Increase Consumer Engagement and Drive Marketing Performance
A Data Lens, personalized- 25758Murphy ≡ DeepGuide
How Companies Can Stop Failing at AI and Data-Driven Decision-Making
Four levers can help business leaders succeed in making the best use of data- 27393Murphy ≡ DeepGuide
Missing features in the data product movement
The Missing Features in Your Data Product Engagement, delight, and trust as deliverables I lead a monthly data discussion group at Zendesk, where I’m fortunate to get to hear a variety of thoughts and perspectives from smart, diverse, and talented p- 25113Murphy ≡ DeepGuide
7 Uses of Marketing Data Science
What is Marketing Data Science- 25759Murphy ≡ DeepGuide
The Secret to Better ROI: Implementing a Full-Funnel Marketing Approach
Building deeper connections with customers while driving lower funnel efficiencies.- 28706Murphy ≡ DeepGuide
Optimizing Vacation Cabin Rental Revenues
A brief look at the science of revenue management with a Python demonstration- 29511Murphy ≡ DeepGuide
Build Customer Journeys Using SQL
Learn to track consumers across multiple channels- 24663Murphy ≡ DeepGuide
A Pathway Towards Responsible AI Generated Content
Warnings from privacy, bias, toxicity, misinformation and IP issues- 22329Murphy ≡ DeepGuide
The Infinite Babel Library of LLMs
Open-source, data, and attention: How the future of LLMs will change- 20639Murphy ≡ DeepGuide
From Data Warehouses and Lakes to Data Mesh: A Guide to Enterprise Data Architecture
Understand how data works at large companies- 21045Murphy ≡ DeepGuide
From Data Lakes to Data Mesh: A Guide to the Latest Enterprise Data Architecture
Understand why large companies are embracing data mesh- 30110Murphy ≡ DeepGuide
AI Frontiers Series: Supply Chain
Recently, I’ve pondered how I can provide equal value to both technical and business-oriented professionals in my writings. Fortunately, my role as a data science consultant naturally offers a wealth of interesting topics. Beyond coding, we consiste- 25240Murphy ≡ DeepGuide
Intro to Data Analysis: The "Google Method"
Ask, Analyse & Act- 21525Murphy ≡ DeepGuide
AI Frontiers Series: Human Resources
An introduction to the AI puzzle in untapped territory- 25681Murphy ≡ DeepGuide
The Path to Success in Data Science Is About Your Ability to Learn. But What to Learn?
Many great developments in data science have been made in the last decade but despite these achievements, many projects never see the light of day. As data scientists we must not only show strong technical skills but also understand the business context,- 20300Murphy ≡ DeepGuide
Data Democratisation: 5 ‘Data For All' Strategies Embraced by Large Companies
In 2006, the Harvard Business Review published an article titled "Competing on Analytics". This influential piece by academics Thomas Davenport and Jeanne Harris sparked widespread discussion on the idea of leveraging analytics as a competitive- 24967Murphy ≡ DeepGuide
OpenAI's Web Crawler and FTC Missteps
OpenAI launches a default opt-in crawler to scrape the Internet, while FTC pursues an obscure consumer deception investigation- 23881Murphy ≡ DeepGuide
Is Generative AI Taking Over the World?
Businesses are jumping on a bandwagon of creating something, anything that they can launch as a "Generative AI" feature or product.- 21417Murphy ≡ DeepGuide
We look at an implementation of the HyperLogLog cardinality estimati
Using clustering algorithms such as K-means is one of the most popul
Level up Your Data Game by Mastering These 4 Skills
Learn how to create an object-oriented approach to compare and evalu
When I was a beginner using Kubernetes, my main concern was getting
Tutorial and theory on how to carry out forecasts with moving averag