Author: Data Science Central

Feed: Featured Blog Posts - Data Science Central. Author: VAMSI NELLUTLA. It is very easy to assume that Data Scientists are highly technical people. Ones who invest all their intellect into interpreting data, concluding smart insights, and offering strategies accordingly. However, this assumption is quite quick to make and presents an incomplete image of any Data Scientist. Due to such assumptions, many people, even recruiters, only focus on the hard skills of Data Scientists. In reality, a good Data Scientist also possesses the right combination of hard and soft skills just like any other professions. Furthermore, we live in an ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Sandipan Dey. In this article a few popular image processing problems along with their solutions are going to be discussed. Python image processing libraries are going to be used to solve these problems. Image Transformations and Warping 0. Display RGB image color channels in 3D A gray-scale image can be thought of a 2-D function f(x,y) of the pixel locations (x,y), that maps each pixel into its corresponding gray level (an integer in [0,255], e.g.,). For an RGB image there are 3 such functions, f_R(x,y), f_G(x.y), f_B(x.y). matplotlib’s 3-D plot functions can be used to plot each function. The following ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Fahad Zaidi. This article will provide a high level understanding of effective ways to set up a data science function in 3 types of organisations: (1) Startups, (2) Medium Size Organisations, and (3) Large Organisations. Most companies have realised that they need Data Science capabilities in order to stay competitive in their respective markets. In some cases, the products and insights generated from a Data Science (DS) team within an organisation is increasingly becoming their main differentiator. Yet very few companies are getting this right. The disconnect between what is needed by organisation ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vibhor Nigam. This post is a token of appreciation for the amazing open source community of Data Science, to which I owe a lot of what I have learned. For last few months, I have been working on a side project of mine to develop machine learning application on streaming data. It was a great learning experience with numerous challenges and lots of learning, some of which I have tried to share in here. This post is focused on how to deploy machine learning models on streaming data and covers all ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Aileen Scott. While businesses around the globe continue to evolve their approaches to data exchange and storage, the rise of blockchain enterprises that specialize in helping other businesses grow into cutting edge organizations have been breaking new grounds and create lasting change through modern technology. We have seen this shift in acceptance in the public eye over the last few years, with major cryptocurrencies becoming accepted by governments like Germany as an acceptable mode of exchange in the country. The way traditional financial systems interact with one another are set to ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Nishtha Singh. Technology changes on the daily basis transforming the rules like never before. Something that looked modern and fresh yesterday can appear dated seemingly overnight, and trends once dismissed as irrevocably passé can unexpectedly cycle back in vogue. Over the past few years, AI, Big Data, and such disruptive technologies have succeeded in transforming many industries already but a notable and a positive change noticeable is in the travel sector. Maybe because travel industries comprises of an enormous amount of data through online searches, analysis of consumer buying behavior but ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Aymone Kouame. What is NLP? Natural Language Processing (NLP) can be simply defined as teaching an algorithm to read and analyze human (natural) languages just like the human brain does, but a lot faster than a human could, more accurately and on very large amounts of data. It is a great skill to have if you are an aspiring data scientist or data analyst because has endless valuable applications in all industries. Examples of NLP applications include information discovery and retrieval for customer service, virtual assistance, content generation, medical diagnosis assistance, ... Read More

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. Featured Resources and Technical Contributions  Solving Image Processing Problems with Python - Part 1 Getting Started with NLP: Simple Topic Modeling in R - Part 1 Deploying Machine Learning Models on Spark Structured Streaming  General Myths to avoid in Data Science and Machine Learning  Neural Networks from a Bayesian Perspective  Comparison of the Top Cloud APIs for Computer Vision  A Little College Sports Analysis, but ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Deena Zaidi. While a reliable method to accurately identify suicidal patients is missing from medical literature, researchers are continually striving for AI-solutions to predict and prevent suicidal attempts. Suicide is cited as the 10th leading cause of death and is one of three leading causes on the rise, according to the Centers for Disease Control and Prevention (CDC). AI tools are diligently being deployed to address the rising suicide risk. Machine Learning for Identifying Suicidal Thoughts In a recent research, researchers from Vanderbilt University applied machine learning (ML) to overcome the limitations of traditional methods that ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Pratik Kanada. A list of machine learning frameworks has come into the picture for the development and deployment of the AI apps. These frameworks will ditch the entire flow of development, testing, optimization, and final production. Developers are quite bewildered which framework to pick and which to ditch. Some of the frameworks would focus on the easy usability while others may put emphasis on the production deployment and parameter optimization. Every framework will have highs and lows of their own. They would have their own areas of excellence and downfalls making ... Read More