Category: Featured

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.   Announcement Learn why DataRobot is included for the first time as a Visionary in the 2019 Gartner Magic Quadrant for Data Science and Machine Learning Platforms. Access the report to: Get current insight on the data science and machine learning market See how augmented data science capabilities compare to traditional approaches Make an informed decision platform that’s best ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: ajit jaokar. This blog is a part of the learn machine learning coding basics in a weekend . We recommend the book Python Data Science Handbook by Jake VanderPlas There are four main libraries in Python that you need to know: numpy, pandas, mathplotlib and sklearn The Python built-in list type does not allow for efficient array manipulation. The NumPy package is concerned with manipulation of multi-dimensional arrays. NumPy is at the foundation of almost all the other packages covering the Data Science aspects of Python. From a Data Science perspective, ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Capri Granville. This article is a solid introduction to statistical testing, for beginners, as well as a reference for practitioners. It includes numerous examples as well as illustrations and definitions for concepts such as rejecting the null hypothesis, one sample hypothesis testing, P-values, critical values, and Bayesian hypothesis testing. It has references to additional topics, such as  What is Ad Hoc Testing? What is a Rejection Region? What is a Two Tailed Test? How to Decide if a Hypothesis Test is a One Tailed Test or a Two Tailed Test. How to ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Michael Grogan. Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast. For instance, image classifiers will increasingly be used to: Replace passwords with facial recognition Allow autonomous vehicles to detect obstructions Identify geographical features from satellite imagery These are just a few of many examples of how image classification will ultimately shape the future of the world we live in. So, let's take a look at ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Andrea Manero-Bastin. This article was written by Jason Brownlee.  Artificial neural networks have two main hyperparameters that control the architecture or topology of the network: the number of layers and the number of nodes in each hidden layer. You must specify values for these parameters when configuring your network. The most reliable way to configure these hyperparameters for your specific predictive modeling problem is via systematic experimentation with a robust test harness. This can be a tough pill to swallow for beginners to the field of machine learning, looking for an analytical way to ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Here we describe a simple methodology to produce predictive scores that are consistent over time and compatible across various clients, to allow for meaningful comparisons and consistency in actions resulting from these scores, such as offering a loan. Scores are used in various contexts, such as web page rankings in search engines, credit score, risk score attached to loans or credit card transactions, the risk that someone might become a terrorist, and more. Typically a score is a function of a probability attached to some particular future event. They ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  True prescriptive analytics requires the use of real optimization techniques that very few applications actually use.  Here’s a refresher on optimization with examples of where and how they’re best used. Predictive analytics and optimization have gone hand in hand since the very beginning.  But in 2014 some erudite journal decided we needed another phrase for this combo and it became Prescriptive Analytics, theoretically differentiating what could happen (predictive) from what should happen (prescriptive) through the application of optimization. Originally I felt strongly that this was a distinction without ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. It all depends on the classes that you attended. Some are worth listing, some are best not to mention. Here I review of few of these data science curricula, and the impression it can make on hiring managers, depending on your profile, work experience, and strength (or lack of) of these programs. Three categories of data science programs Type 1: cheap The cheap version, is usually based on a monthly subscription model. It typically costs less than $30 per month and gives you access to a wide array of ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Tansel Arif. PREFACE Previously, I tackled the Gambler's Ruin problem using conditional probability and difference equations as well as visualising the simulations of the problem in a random walk style using Python/Pygame. This can be found here: https://www.datasciencecentral.com/profiles/blogs/gambler-s-ruin-si.... Continuing this gambling style, the following demonstrates a simple application of Bayesian Inference, probability generating functions and conjugacy to a process involving multiple random variables. This article has 5 sections:0- Introduction: Introduction to a process dependent on two random variables1- The Problem: Setting up the wheel of fortune problem2- The Solution: The solution to ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Benjamin Waxer. In this 5 Minute Analysis we'll preprocess, map, and explore complicated sales data for liquor stores in Iowa. Then we’ll extract the relevant latitude and longitude from a problematic column of the data and discover the city with the most sales. Next we’ll filter the data to that city and prepare the data for easy loading into Business Analysis tools such as Tableau and PowerBI. Finally we’ll use our extracted location data to visualize sales within the selected city.Dataset: Iowa Liquor Sales This blog post explores and analyzes the ... Read More