Data Science with Keerthi (தமிழில்)
New video alert — 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧: All in One!Video - https://youtu.be/8FCbw0CgsWI?si=fs0T2... Instead of overwhelming definitions, I focused on what they are? who invented them? when to use them? why they matter? and how to understand them intuitively. 🎲𝐃𝐢𝐬𝐜𝐫𝐞𝐭𝐞 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 - (our yes/no world) 1. Bernoulli – basic “success or failure” 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘍𝘭𝘪𝘱 𝘢 𝘤𝘰𝘪𝘯 𝘰𝘯𝘤𝘦. 2. Binomial – multiple attempts of Bernoulli 𝘌𝘹𝘢𝘮𝘱𝘭𝘦:𝘍𝘭𝘪𝘱𝘱𝘪𝘯𝘨 𝘵𝘩𝘦 𝘤𝘰𝘪𝘯 10 𝘵𝘪𝘮𝘦𝘴. 3. Poisson – rare events in a fixed interval 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘈𝘤𝘤𝘪𝘥𝘦𝘯𝘵𝘴 𝘪𝘯 1 𝘩𝘰𝘶𝘳. 4. Geometric – how many tries until first success 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘏𝘰𝘸 𝘮𝘢𝘯𝘺 𝘧𝘭𝘪𝘱𝘴 𝘵𝘪𝘭𝘭 𝘧𝘪𝘳𝘴𝘵 𝘩𝘦𝘢𝘥. 📈 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 (our natural world) 1. Uniform – Every value is equally likely 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘙𝘢𝘯𝘥𝘰𝘮 𝘯𝘶𝘮𝘣𝘦𝘳 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 0 𝘢𝘯𝘥 100 2. Normal Distribution – heights, weights… all the “bell curve” stuff 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘏𝘦𝘪𝘨𝘩𝘵𝘴 𝘰𝘧 𝘴𝘵𝘶𝘥𝘦𝘯𝘵𝘴. 3. Standard Normal – why we convert to Z and compare across groups 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘊𝘰𝘮𝘱𝘢𝘳𝘪𝘯𝘨 𝘮𝘢𝘳𝘬𝘴 𝘢𝘤𝘳𝘰𝘴𝘴 𝘤𝘭𝘢𝘴𝘴𝘦𝘴. 4. Log Normal – when things grow multiplicatively (salaries, stock prices) 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘚𝘢𝘭𝘢𝘳𝘪𝘦𝘴, 𝘴𝘵𝘰𝘤𝘬 𝘱𝘳𝘪𝘤𝘦𝘴. 5. Pareto / Power Law – the classic 80–20 world (few have a lot, many have little) 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘞𝘦𝘢𝘭𝘵𝘩 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯. hashtag#Statistics hashtag#DataScience hashtag#Probability hashtag#Distributions hashtag#NormalDistribution hashtag#UniformDistribution hashtag#DataScienceWithKeerthi
2 weeks ago | [YT] | 8
Data Science with Keerthi (தமிழில்)
New video alert — 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧: All in One!
Video - https://youtu.be/8FCbw0CgsWI?si=fs0T2...
Instead of overwhelming definitions, I focused on
what they are?
who invented them?
when to use them?
why they matter? and how to understand them intuitively.
🎲𝐃𝐢𝐬𝐜𝐫𝐞𝐭𝐞 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 - (our yes/no world)
1. Bernoulli – basic “success or failure”
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘍𝘭𝘪𝘱 𝘢 𝘤𝘰𝘪𝘯 𝘰𝘯𝘤𝘦.
2. Binomial – multiple attempts of Bernoulli
𝘌𝘹𝘢𝘮𝘱𝘭𝘦:𝘍𝘭𝘪𝘱𝘱𝘪𝘯𝘨 𝘵𝘩𝘦 𝘤𝘰𝘪𝘯 10 𝘵𝘪𝘮𝘦𝘴.
3. Poisson – rare events in a fixed interval
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘈𝘤𝘤𝘪𝘥𝘦𝘯𝘵𝘴 𝘪𝘯 1 𝘩𝘰𝘶𝘳.
4. Geometric – how many tries until first success
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘏𝘰𝘸 𝘮𝘢𝘯𝘺 𝘧𝘭𝘪𝘱𝘴 𝘵𝘪𝘭𝘭 𝘧𝘪𝘳𝘴𝘵 𝘩𝘦𝘢𝘥.
📈 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 (our natural world)
1. Uniform – Every value is equally likely
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘙𝘢𝘯𝘥𝘰𝘮 𝘯𝘶𝘮𝘣𝘦𝘳 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 0 𝘢𝘯𝘥 100
2. Normal Distribution – heights, weights… all the “bell curve” stuff
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘏𝘦𝘪𝘨𝘩𝘵𝘴 𝘰𝘧 𝘴𝘵𝘶𝘥𝘦𝘯𝘵𝘴.
3. Standard Normal – why we convert to Z and compare across groups
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘊𝘰𝘮𝘱𝘢𝘳𝘪𝘯𝘨 𝘮𝘢𝘳𝘬𝘴 𝘢𝘤𝘳𝘰𝘴𝘴 𝘤𝘭𝘢𝘴𝘴𝘦𝘴.
4. Log Normal – when things grow multiplicatively (salaries, stock prices)
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘚𝘢𝘭𝘢𝘳𝘪𝘦𝘴, 𝘴𝘵𝘰𝘤𝘬 𝘱𝘳𝘪𝘤𝘦𝘴.
5. Pareto / Power Law – the classic 80–20 world (few have a lot, many have little) 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: 𝘞𝘦𝘢𝘭𝘵𝘩 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯.
hashtag#Statistics hashtag#DataScience hashtag#Probability hashtag#Distributions hashtag#NormalDistribution hashtag#UniformDistribution hashtag#DataScienceWithKeerthi
2 weeks ago | [YT] | 8