Hello everyone! Welcome to another Interesting Tech Update on Analytics! Recently, I came across an interesting update about  chDB in the  TDLR Newsletter, which motivated me to explore it further. Until now, I’ve mostly worked with server‑based  SQL engines such as  PostgreSQL, MySQL, and SQL Server. I’ve also used serverless  SQL engines on many challenge platforms like DataLemur, LeetCode, and HackerRank. These platforms typically use in‑process  SQL engines, which don’t

Healthcare organizations generate huge amounts of patient and provider data daily. In this blog, we use Python to analyze the HC Dataset from Kaggle, focusing on patient discharge trends, provider specialties, and ambulatory visit patterns. Key insights include variations in length of stay (LOS), discharge dispositions, and visit volumes, which help hospitals optimize staffing, improve operational efficiency, and improve patient care.

I. The Goal: Testing the Hype In my last post, I documented how I built a custom dataset by pulling live data directly from Wikipedia. It was a process of turning messy web tables into a structured CSV file. Today, I’m putting that data to work to answer a question many of us have: Are record-breaking box office numbers a consistent new trend or are we just seeing a few lucky hits? By looking at the last 30 years of cinema, I wanted to see if the "Billion Dollar Club" is actu

Introduction: Challenging the Traditional Analytics Workflow Usually, the roadmap for building a data portfolio follows a very specific, linear path. The formula is familiar to almost everyone entering the field: Find  a pre-cleaned dataset on a portal like Kaggle. Download  the static CSV file. Clean  any minor remaining errors using Excel, SQL or Power BI. Visualize  the results in a standard dashboard. It’s a safe and effective process for learning the basics. However, as

As I began my journey as a Data Analyst, I often found myself questioning a fundamental part of the job: If I have already done the math, why do I need the picture?  I was meticulously calculating means, variances, and correlations for every dataset that crossed my desk. I felt that if the math was accurate, the story was told. Why spend hours perfecting a chart when the 'truth' was already sitting there in the totals? That skepticism vanished the moment I came across Anscomb

Decoding the Silent Language: How Visuals Turn Bio-Data into Action Your body is constantly speaking to you, but it speaks in a language of numbers that most of us can’t instinctively understand. Every heartbeat and sleep cycle is a data point in the vast experiment of the "Quantified Self". As a Data Analyst, I believe our job is to act as translators. If we use the wrong visual "lens," the story of our health becomes noise. But with the right representation, we can finally

The "Why": From Reporting to Empowering As a data analyst, I’ve realized that insights are often "trapped" within the technical environments where they are created. In my previous analysis, I utilized Tableau  to create a visual representation of the heart disease data. While Tableau is exceptional for discovery, I realized that for a clinical tool to be truly interactive and "live" in a custom web environment, a doctor or researcher needs a workspace that exists on their own

Python makes data analysis simple and complete. You can clean data, merge files and databases, run statistical tests, and even prepare it for charts and models—all in one tool, for free.

Hi team! Check out my latest blog on why data cleaning is critical for accurate visualizations and better business decisions. I also show a real-world COVID-19 dataset case study using Python.

The Python hackathon gave me hands-on experience analyzing the Flatten COVID-19 dataset in Jupyter Notebook—the digital lab book where data scientists prototype Python, R, and more. Before writing a single line of code, I learned the first rule of data analysis: deeply understand your dataset first. Dataset Reality Check: This wasn't hospital lab data. It contained self-reported COVID-19 symptom surveys from Ontario residents during early 2020, split across 3 evolving schemas

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Visual storytelling  in data analysis is the practice of using visuals—charts, graphs, plots, and annotated figures—to communicate...

Imagine you're sitting on an enormous pile of data, wishing you could instantly spot trends, outliers, or weird patterns? Whether you're...

Suze Orman once said, “Cleanliness is a state of purity, clarity, and precision. ” This not only applies to our day-to-day cleaning, but...

STEP 1: Imports and reading data When working with Python, importing libraries is essential for extending the functionality of your code....

Python virtual environments are useful for managing dependencies and isolating projects. They allow you to set up different environments...

Joins Joins in the data world means combining two tables horizontally. Join functions/queries help us to consolidate two separate tables...

This is an article taking the user step by step to install Anaconda 2024 version in Windows laptop for the purposes of using Jupyter...

Introduction: Lambda, Map, Filter, and Reduce functions in Python streamline code, enhancing readability and efficiency. Explore their...

Python plays a crucial role in the world of Artificial Intelligence (AI) & data analysis. It has become one of the most popular...