towardsdatascience.com 2019-09-12 15:03 What to expect from a causal inference business project: an executive’s guide III Part III: Where causal inference stands in the current AI, Big Data, Data Science, Statistics, and Machine Learning scene?This is the third part of the post “What to expect from a causal inference...

towardsdatascience.com 2019-09-12 15:01 What to expect from a causal inference business project: an executive’s guide II Part II: Which are the project key points you need to knowThis is the second part of the post “What to expect from a causal inference business project: an executive’s guide”. You will find the...

towardsdatascience.com 2019-09-12 15:00 What to expect from a causal inference business project: an executive’s guide I Part I: When do you need casual inference?This is the fifth post on a series about causal inference and data science. The previous one was “Solving Simpson’s Paradox”. You will find the second...

towardsdatascience.com 2019-09-12 12:24 Building own Logistic Classifier in R [Logistic Trilogy, part 2] Building own Logistic Classifier in R [Logistic Trilogy, Part 2]If someone gets immense pleasure in developing own codes for data science projects or statistical projects then this is the right...

towardsdatascience.com 2019-09-11 14:43 Reducing Uncertainty: The less you know, the more you gain In an era where data has become so prevalent, we’ve become too accustomed on solving problems where we feel we have “enough data” and dismiss the ones where we feel there is a lack of. To make...

towardsdatascience.com 2019-09-11 13:43 Keeping Data Science Scientific Oil painting by Francesco Masci.What would Karl Popper make of the newest scientific discipline?Below, I’ve paraphrased the following research question originally raised in Daniel Khaneman’s...

towardsdatascience.com 2019-09-09 04:04 Sampling! An approach to solve the bird counting problemWhat do you do when you have a large dataset and your algorithms take forever to run? Or let's say if you want to find out the total number of people...

towardsdatascience.com 2019-09-09 00:38 An Introduction to Naïve Bayes Classifier From theory to practice, learn underlying principles of Naïve BayesSource: https://thatware.co/naive-bayes/This blog will cover following questions and topic:1. What is Naïve Bayes Classifier?2....

towardsdatascience.com 2019-09-08 12:48 Understanding the Central Limit Theorem Diving deep into one of the most important theorems in statisticsIn this article, I want to talk about the central limit theorem and its applications in statistics. The central limit theorem states...

towardsdatascience.com 2019-09-07 20:56 Practical Experiment Fundamentals All Data Scientists Should Know A How-to for Non-Parametric Power Analyses, p-values, Confidence Intervals, Checking for BiasCode SetupR for Data Science is a free, amazing read to learn how to use the tidyverse code used in...

towardsdatascience.com 2019-09-07 14:47 Modelling Efficient Military Deployments with Machine Learning — K-Means Clustering in R Modelling Efficient Military Deployments with Machine Learning — K-Means Clustering in REfficiently Deploying Naval Resources(If using a smart-phone this article is best viewed in landscape...

towardsdatascience.com 2019-09-07 03:03 Stochastic Gradient Descent — Clearly Explained !! Stochastic Gradient Descent — Clearly Explained !!Stochastic gradient descent is a very popular and common algorithm used in various Machine Learning algorithms, most importantly forms the...

towardsdatascience.com 2019-09-06 15:22 Bollinger Bands Statistics in Trading There are lots of traders use Bollinger Bands. I love Bollinger Bands as well. It uses and brings statistics into the trading world. But how accurate is it? Is it a correct way to use standard...

towardsdatascience.com 2019-09-05 14:41 Is overfitting really bad ? Is overfitting a bad thing ?source & credit — https://www.pexels.com/@brunoscramgnonOne of the fundamental principle that is taught in classical statistics and by extension in machine...

programmer group 2019-09-05 06:36 Let's review Data Science: The crazy regression analysis Regression analysis is a statistical analysis method to determine the quantitative relationship between two or more variables. Here, I will describe the difference between regression and machine...

towardsdatascience.com 2019-08-31 06:34 Importance Sampling Introduction Estimate Expectations from a Different DistributionImportance sampling is an approximation method instead of sampling method. It derives from a little mathematic transformation and is able to...

towardsdatascience.com 2019-08-26 21:24 Predicting FANG Stock Prices Source: All images in this article were generated by the author in R using dygraphsModeling cointegrated time series data using VECMsFinancial data is the best known resource for time series data....

towardsdatascience.com 2019-08-26 14:09 Machine Learning Algorithms are Not Black Boxes A Guide to Interpreting Neural NetworksData science is a very new field with few standards set in stone. This makes it an exciting field to work in because it is up to current data scientists to...

towardsdatascience.com 2019-08-24 21:53 The Poisson Process: Everything you need to know Learn about the Poisson process and how to simulate it using PythonLet me begin by tickling your curiosity a little bit. Let’s look at how a Poisson sequence might look like.A sample...

towardsdatascience.com 2019-08-24 18:29 Probability Theory Continued: Infusing Law of Total Probability Probability Theory Continued: Infusing Law of Total Probability With Kolmogorov’s Definition of Joint ProbabilityDemystifying an aspect of probability theory through an extension of Bayesian...

programmer group 2019-08-24 07:35 Derivation of Logistic Regression from Principle-Logit Transform and Potential Factor Error The application of Logistic Regression (LR) and the idea of engineering are generally introduced very clearly. Most methods start with Sigmoid function. This blog post attempts to reinterpret...

towardsdatascience.com 2019-08-23 10:18 Confounders made simple ABSTRACT: Not all covariates of treatment and outcome variables in an observational study should be adjusted for. By default, one should doubt studies which blindly adjust for many confounders...

towardsdatascience.com 2019-08-23 03:07 Why exclude highly correlated features when building regression model ?? Why exclude highly correlated features when building regression model ??If you are someone who has worked with data for quite some time, you must be knowing that the general practice is to exclude...

towardsdatascience.com 2019-08-21 12:09 ML in Snowflake Part 2: k-means clustering One of the ways we like to make sense of the world is by grouping similar things together.In design, we group colours into shades. In sales and marketing, customers are usually segmented to...

towardsdatascience.com 2019-08-21 10:49 Answering Monty Hall puzzle with Monte Carlo Monte Carlo is a conceptually simple but powerful technique that is widely used. It makes use of randomness to answer questions.In this post, I’ll explain how to solve the Monty Hall problem using...