Four Steps to Remove Analytics Waste
Accelerate Decision Making by Removing Analytics Waste- 20504Murphy ≡ 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
The Importance of Storytelling in Data Science
Why we need stories to explain Data Science- 27664Murphy ≡ DeepGuide
Data Platform Architecture Types
How well does it answer your business needs? Dilemma of a choice.- 22782Murphy ≡ DeepGuide
Digital Marketing Analysis with Python and MySQL
A digital marketing analytics exercise with explained step-by-step code in both SQL and Python environments.- 28029Murphy ≡ DeepGuide
Five Powerful Prioritization Techniques from Product Management
As a Data Scientist, Analyst, or anyone developing a product or solution, you can borrow tools that Product Managers use to prioritize requirements. With limited resources and time, it’s important to focus on the features and requirements that will- 28994Murphy ≡ DeepGuide
Data Observability for Analytics and ML teams
Principles, practices, and examples for ensuring high quality data flows- 28196Murphy ≡ DeepGuide
NBA Analytics Using PySpark
Win ratio for back-to-back games, mean and standard deviation of game scores, and more with Python code- 21573Murphy ≡ DeepGuide
Shaping Your Data with SQL
Improve & optimize your data analytical process with different techniques for data shaping- 25510Murphy ≡ DeepGuide
A Serverless Query Engine from Spare Parts
An open-source implementation of a Data Lake with DuckDB and AWS Lambdas- 26952Murphy ≡ DeepGuide
A better way to analyze feature release impact
Or - why naive "before-after" comparisons can drive bad product decisions- 26442Murphy ≡ DeepGuide
How to identify your business-critical data
Practical steps to identifying business-critical data models and dashboards and drive confidence in your data- 26536Murphy ≡ DeepGuide
Intro to Data Analysis: The "Google Method"
Ask, Analyse & Act- 21525Murphy ≡ DeepGuide
The 5 Efficient Ways to Find and Resolve Data Issues
Uncovering Hidden Anomalies and Inconsistencies- 25527Murphy ≡ DeepGuide
From analytics to actual application: the case of Customer Lifetime Value
Part one of a comprehensive, practical guide to CLV techniques and real-world use-cases- 25547Murphy ≡ DeepGuide
The Ultimate Visualization Assistant
As the sun began to dim and the city lights came to life, the inevitability of a late night in the office settled in. I found myself in a race against time. A crucial sales presentation was looming less than a day away, and success hinged on an unfulfille- 23456Murphy ≡ DeepGuide
Exploratory Data Analysis: Unraveling the Story Within Your Dataset
As a data enthusiast, exploring a new dataset is an exciting endeavour. It allows us to gain a deeper understanding of the data and lays the foundation for successful analysis. Getting a good feeling for a new dataset is not always easy, and takes time. H- 23557Murphy ≡ DeepGuide
EDA with Polars: Step-by-Step Guide to Aggregate and Analytic Functions (Part 2)
Advanced aggregates and rolling averages at lightning speed with Polars- 23144Murphy ≡ DeepGuide
Layers of Data Quality
With the recent surge of interest in generative AI and LLMs, data quality has received a resurgence of interest. Not that the space needed much help: companies like Monte Carlo, Soda, Bigeye, Sifflet, Great Expectations, and dbt Labs have been developing- 24157Murphy ≡ 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