Data Analytics – Page 6

If You’re Trying to Get Into AI, This Is What You Need to Do

Data AnalyticsAugust 18, 202576Views 0Likes 0Comments

Image by Editor | ChatGPT # Introduction For a young person coming out of high school or college, or even for parents of young children, it can be quite overwhelming to choose a career. For an older professional trying to transition into the AI landscape, it can be even worse. Adding AI on…

Diffusion Models Demystified: Understanding the Tech Behind DALL-E and Midjourney

Data AnalyticsAugust 13, 2025105Views 0Likes 0Comments

Image by Author | Ideogram Generative AI models have emerged as a rising star in recent years, particularly with the introduction of large language model (LLM) products like ChatGPT. Using natural language that humans can understand, these models can process input and provide a suitable output. As a result of products like ChatGPT, other…

AI-Powered Feature Engineering with n8n: Scaling Data Science Intelligence

Data AnalyticsAugust 8, 202583Views 0Likes 0Comments

Image by Author | ChatGPT # Introduction Feature engineering gets called the 'art' of data science for good reason — experienced data scientists develop this intuition for spotting meaningful features, but that knowledge is tough to share across teams. You'll often see junior data scientists spending hours brainstorming potential features, while senior folks end…

Debugging and Tracing LLMs Like a Pro

Data AnalyticsAugust 3, 2025108Views 0Likes 0Comments

Image by Author | Canva # Introduction Traditional debugging with print() or logging works, but it’s slow and clunky with LLMs. Phoenix provides a timeline view of every step, prompt, and response inspection, error detection with retries, visibility into latency and costs, and a complete visual understanding of your app. Phoenix by Arize…

When Do I Need to Use an LLM?

Data AnalyticsJuly 29, 202594Views 0Likes 0Comments

Image by Author | ChatGPT # Introduction Over the last couple of years, large language models (LLMs) have become near-ubiquitous protagonists in the AI landscape and across media channels — being sometimes touted as the all-in-one solution to every problem. That might be a slight exaggeration on my part. Still, it's true that…

Why Python Pros Avoid Loops: A Gentle Guide to Vectorized Thinking

Data AnalyticsJuly 24, 2025106Views 0Likes 0Comments

Image by Author | Canva # Introduction When you’re new to Python, you usually use “for” loops whenever you have to process a collection of data. Need to square a list of numbers? Loop through them. Need to filter or sum them? Loop again. This is more intuitive for us as humans because…

7 Python Web Development Frameworks for Data Scientists

Data AnalyticsJuly 19, 2025117Views 0Likes 0Comments

Image by Author | Canva Python is widely known for its popularity among engineers and data scientists, but it’s also a favorite choice for web developers. In fact, many developers prefer Python over JavaScript for building web applications because of its simple syntax, readability, and the vast ecosystem of powerful frameworks and tools…

PyNarrative: An Excellent Python Library for Data Storytelling

Data AnalyticsJuly 14, 2025103Views 0Likes 0Comments

Image by Author | Canva Let’s say there are two people, person A and person B. You give them the same dataset to analyze. But somehow, A’s story comes out better than B’s. Why? Because it’s not just the data itself that matters. But how well you can turn that data into a story…

Building Modern Data Lakehouses on Google Cloud with Apache Iceberg and Apache Spark

Data AnalyticsJuly 9, 202594Views 0Likes 0Comments

Sponsored Content The landscape of big data analytics is constantly evolving, with organizations seeking more flexible, scalable, and cost-effective ways to manage and analyze vast amounts of data. This pursuit has led to the rise of the data lakehouse paradigm, which combines the low-cost storage and flexibility of data lakes with…

Efficient Knowledge Management for Data Teams Using Notion

Data AnalyticsOctober 2, 2024167Views 1Like 0Comments

Image by Editor | Ideogram An organization's data teams often encounter complex projects with a variety of resources and structures scattered around. As the number of projects and team members increases, the information becomes more tangled and increasingly complex to manage. This is why we need to consolidate the information in a single platform.…