Decision Science

  Want to crack your next Data Science or ML interview? These 5 ChatGPT prompts will help you practice like never before ( Not a medium member? No problem!. You can read here ) Interviews for Data Science and Machine Learning roles are tough. You’ll be asked to solve business cases, explain technical concepts, and even tackle behavioral questions under pressure. But here’s the good news: with the right ChatGPT prompts, you can simulate real interview scenarios, practice your answers, and walk into the room with confidence. Below are 5 sample prompts (from my new ebook The Ultimate Prompt Book: 100+ ChatGPT Prompts for Data Science & ML Success ) — along with example outputs — that can help you prep like a pro. Prompt 1: Simulated Interview Questions Prompt:   “Act as an interviewer for a Data Scientist role. Ask me 5 technical questions covering: statistics, SQL, ML concepts, business case, and behavioral. After I respond, evaluate my answer and give specific feedbac...

 Ever wondered how to statistically compare two groups when you already know the population standard deviation? That’s where the two-sample Z-test comes in — especially handy for large datasets. In this step-by-step guide, we’ll walk through a fully solved numerical example that shows exactly how this test works in practice. Whether you’re prepping for a stats exam, brushing up on hypothesis testing, or applying it in real-world data analysis, this breakdown will make the concept click — without overwhelming jargon. Lets start with the problem statement! Problem Statement: A researcher wants to compare the average daily calorie intake of male and female adults in a city. Two independent random samples are taken: Sample 1 (Males): n1=40, mean X1=2500, population standard deviation σ1=300 Sample 2 (Females): n2=35, mean X2=2300, population standard deviation σ2=250 At 5% level of significance , test whether there is a significant difference in the mean calorie intake between ...

 Understanding probability distributions is essential for anyone working in data science , statistics , or machine learning . In this blog, we’ll break down three of the most common distributions  —  Normal , Binomial , and Poisson  — along with easy-to-follow Python examples using real-world data. Whether you’re building a predictive model or analyzing data patterns, mastering these distributions will sharpen your skills. Let’s dive in! What Are Probability Distributions? A probability distribution describes how the values of a random variable are distributed. It tells you the probability of different outcomes — kind of like a weather report, but for data! There are two broad types: Discrete distributions : Deal with countable outcomes (e.g., number of cars). Continuous distributions : Deal with outcomes that can take any value within a range (e.g., height, weight). 1. Normal Distribution — The Bell Curve Superstar What is it? The Normal distribution is a continuous distri...

  Data collection is the foundation of any statistical study, business decision, or research project. Without reliable data, even the most sophisticated models and strategies fall apart. Whether you’re building a survey for customer feedback or gathering industry reports, understanding how to collect data properly is essential. In this guide, we’ll break down:  1. Methods of Collecting Data  2. How to Draft a Questionnaire  3. Sources of Secondary Data 1. Methods of Collecting Data The methods of data collection are broadly classified into two types: Primary Data Collection Secondary Data Collection Primary Data Collection Methods Primary data is collected first-hand by the researcher for a specific purpose. Here are the common ways to collect primary data: a) Direct Personal Investigation Researcher personally collects data by interacting directly with the respondent. Highly accurate, but time-consuming and costly. Example: A manager interviewing customers at the...

 A step-by-step guide to sampling methods: random, stratified, systematic, and cluster sampling explained with Python implementation. Perfect for data science learning. In the world of data science , statistics , and analytics , it’s often impossible to collect data from the entire population. That’s where sampling methods come to the rescue — helping us pick a smaller group that still represents the whole. In this blog, we’ll break down four major sampling techniques : Random Sampling Stratified Sampling Systematic Sampling Cluster Sampling We’ll also show you how to implement them step-by-step in Python using the famous Titanic dataset ! What is Sampling? Sampling is simply selecting a subset of individuals from a larger population, so we can study and make conclusions about the entire group without examining every individual. A good sampling method ensures that your sample is representative , unbiased , and accurate . 1. Simple Random Sampling Every individual has an e...

  This is the third part of most frequently asked interview questions and answers, along with explanations on Hypothesis Testing. You can read the first two parts here: Frequently Asked Hypothesis Testing Questions for Data Scientist Interviews (part 1) Frequently Asked Hypothesis Testing Interview Questions for Aspiring Data Scientists (Part 2) We’ve covered a long journey, and it will continue in this guide too, which will cover key interview questions and answers on hypothesis testing, focusing on topics like testing means with two independent samples, one-sample proportion tests, two-proportion tests, and even how to implement these tests in Python. Ready to boost your hypothesis testing knowledge? Let’s dive in! 🚀 1. What is the Purpose of Hypothesis Testing in Statistics? Let’s start with the revision! Question: What is the main purpose of hypothesis testing in statistics? A) To confirm a theory by providing absolute proof B) To calculate correlation coefficients. C) To ...