Claim OwnershipDharsan Decodes Data
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© Dharsan
Description
This podcast is a documentation of data concepts from scratch!
We can connect over Linkedin @mahadharsan
We can connect over Linkedin @mahadharsan
37 Episodes
Reverse
pip install requestsimport requests # Telling Python we want to use this power-up# The "Waiter's" Address (A free API for current weather)url = "https://api.open-meteo.com/v1/forecast?latitude=13.08&longitude=80.27¤t_weather=true"# 1. Send the requestresponse = requests.get(url)# 2. Convert the "mess" into a Python Dictionarydata = response.json()# 3. Dig into the Dictionary to find the temperature# (Based on the JSON structure the API provides)current_temp = data["current_weather"]["temperature"]print(f"The current temperature in Chennai is {current_temp}°C")
All basics of SQL that I included for beginners.
In this episode, we dive into SQL, the language of the data kingdom. Learn why SQL remains the essential foundation for every data engineer, the critical difference between "querying" and "modeling," and the core patterns like Joins and CTEs you need to build reliable, scalable pipelines.
For my Tamil Listeners :)
We are going to follow a syllabus of four arcs. Arc 1: The Foundation (The "Non-Negotiables")Arc 2: Data Architecture & ModelingArc 3: The Engineering Lifecycle (The "Plumbing")Arc 4: Modern & Future FrontiersWe are in our first arc now!
This is just a comparative analysis between Dbt vs other tools.
Why do we even need Dbt in the first place? Why organisations love it?Analogy for the Road: Think of traditional data analysis like a home cook trying to prepare a 10-course banquet by memory, using whatever pots are clean and hoping everything finishes at the same time. Using dbt is like moving that process into a professional industrial kitchen: there is a clear recipe for every dish (documentation), a head chef who ensures the appetizers come out before the main course (dependency management), and a health inspector checking the ingredients before they go in the pot (testing).
Principal Component Analysis explained.
This podcast explains kmeans and GMM using EM algorithm
Explained unsupervised machine learning contents
Basics of Image Processing in Data Science
I prepared for an interview so thought it might be useful for other people too.
Gradient Descent in Linear Regression.
An introduction to Gradient Descent
Summary of how to tackle Linear Regression
Supervised Machine Learning - SML
Explained OLS
Linear regression - modelOLS - parameter estimation statistical method
If you have any questions let me know mejoratamil@gmail.com
Bias and Variance is an important concept in the field of Data science. Yet, we all are noticing it in real life. So this podcast briefs about Bias and Variance using real life example.
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