Career Advice

Your inbox is a trap. So is your ticketing system. They are comfortable, orderly prisons that define your job and limit your impact. A request arrives. “Can you pull the numbers on the Q3 campaign?” You execute the query. You build the dashboard. You deliver the report. You have done your job perfectly. And you […]

You spend two weeks buried in data. You isolate signal from noise. You build a model, run a forecast, and uncover a multi-million dollar opportunity for your organization. This is the moment that defines your job, the insight that can change the company’s trajectory. You present your findings to leadership. You walk through the charts, […]

For decades, the analyst’s job has been to be a historian. You receive a request. You pull the data. You explain what already happened. “Why did sales dip in Q2?” “What was the conversion rate on last week’s campaign?” You are the keeper of the company’s memory, expertly explaining the past. This role is a […]

You have the framework for your new career. You have the commands to master your new tools. You understand why your role must evolve beyond automatable tasks. You know how to write prompts that get results. This will only get you so far. Using an LLM for one-off tasks is like putting a Formula 1 […]

Last week, I told you to get off the melting iceberg. The repetitive, rule-based work of the Operator quadrant in my Human-AI Career Nexus is being automated. This is not a threat. It is an opportunity to focus on what matters: investigation, design, and innovation. Many of you know this. You are using powerful Large […]

Over the past two weeks, we’ve laid the foundation for understanding inferential statistics — from its core principles to real-world applications and limitations. Now it’s time to build out the toolbox and explore the key techniques that make inferential statistics a practical part of your analytical approach. These methods help us compare groups, test hypotheses, […]

Last week, we explored the what and why of inferential statistics, diving into its core principles and significance. This week, let’s turn theory into practice by uncovering how inferential statistics are applied in real-world scenarios — and the limitations analysts must navigate when using them. Inferential statistics enable us to go beyond the immediate data […]

In previous posts, we explored the role of descriptive statistics in summarizing data and how correlation matrices can reveal connections between variables. Now, we’ll advance to the next stage: analyzing sample data to understand the broader population. This is where inferential statistics come into play, allowing us to make forecasts, test theories, and draw significant […]

A strong foundation is essential for any successful analysis. In data analytics, that foundation is created using Descriptive Statistics. These statistics provide a quick and thorough overview of the key attributes of your data which makes them an invaluable tool for understanding datasets. In this tutorial, we will explore descriptive statistics and how tools such […]

Customer Lifetime Value (CLV) is a crucial metric for understanding your customer base and their value to your business. It plays a significant role in developing personalized strategies that focus on maximizing value by identifying and investing in high-CLV customers. Pioneers like Peter Fader and Dan McCarthy have demonstrated the power of prioritizing customer value […]