Writing
Writing
Essays and working notes on data science, machine learning, Azure, and building with generative AI. Includes articles published on Analytics Vidhya.
Exploratory Data Analysis
Exploratory Data Analysis(EDA) is the process of understanding and studying the data in detail to discover patterns, spot anomalies and outliers to maximize the insights we derive from the data set. We use data visualization techniques to recognize patterns and draw inferences that are not readily visible in the raw data.
Summary statistics can deceive. Here’s how you can get smarter with data visualization — Anscombe’s quartet
To make the case, let me introduce you to Anscombe’s quartet. It comprises four datasets which have similar statistical summary. They have the same measures of central tendency, correlation, and even the same linear regression model. They look alike in every way. However, you throw in data visualization and a completely different story begins to emerge. In the following blog, you would find detailed notes on the dataset and it’s visualization using python libraries.
Q-Q plot — Ensure Your ML Model is Based on the Right Distribution
Q-Q plots are also known as Quantile-Quantile plots. As the name suggests, they plot the quantiles of a sample distribution against quantiles of a theoretical distribution. Doing this helps us determine if a dataset follows any particular type of probability distribution like normal, uniform, exponential
Everything you need to know about Hypothesis Testing in Machine Learning
Any data science project starts with exploring the data. When we perform an analysis on a sample through exploratory data analysis and inferential statistics we get information about the sample. Now, we want to use this information to predict values for the entire population.
Functional Programming — Map, Reduce and Filter in Python
Functional programming, as the name suggests, computes through the evaluation of functions. They allow us to write simpler, shorter code with faster implementation methods. In functional programming, code relies entirely on the evaluation of pure functions.
Everything you need to know about SQL for Data Science
Understanding and learning SQL can easily get complicated and overwhelming. In this blog, I will unpack the SQL concepts into understandable bits that will help you get up to speed on SQL in under 5 minutes.
A Complete Guide on Sampling Techniques for Data Science
What sampling is, the main sampling techniques, and the industry use cases: getting a sample that closely represents the population.
Azure Machine Learning: A Step-by-Step Guide
A comprehensive walkthrough of Azure Machine Learning: operationalising machine learning tasks and iterative processes on Microsoft Azure.
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