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• Learn About Data Science & the Future of Investing from Hedge Fund Leaders at Rev 2 - Apr 30, 2019.
  
  Rev 2 features interactive sessions, Q&A with industry luminaries, poster sessions for interesting modeling techniques and accomplishments, and stimulating conversations about how to make data science an enterprise-grade capability.

  Data Science, Domino, Hedge fund, Investment, New York City, NY

• Interview Questions for Data Science – Three Case Interview Examples - Apr 30, 2019.
  
  Part two in this series of useful posts for aspiring data scientists focuses on case interviews and how you can best go about answering them.

  Career, Data Science, Interview Questions, Kaiser Fung

• Top Data Science and Machine Learning Methods Used in 2018, 2019 - Apr 29, 2019.
  
  Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline.

  Algorithms, Clustering, Data Science, Deep Learning, Machine Learning, Poll, Regression

• Pandas DataFrame Indexing - Apr 29, 2019.
  
  The goal of this post is identify a single strategy for pulling data from a DataFrame using the Pandas Python library that is straightforward to interpret and produces reliable results.

  Data Science, Pandas, Python

• The most desired skill in data science - Apr 26, 2019.
  
  What is the biggest skill gap in data science according to hiring managers looking for hire recent graduates? Hint: it’s not coding.

  Data Science, Data Science Skills, Kaiser Fung, Self-Driving Car

• Projects to Include in a Data Science Portfolio - Apr 26, 2019.
  
  “Don’t pick just random projects to work on and add it to your resume or portfolio. Solve a problem that relates to the companies that you’re interested in.”

  Career Advice, Data Science, Dataquest, Portfolio

• Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own - Apr 25, 2019.
  
  Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.

  Pages: 1 2

  Deep Learning, GANs, Generative Adversarial Network, Generative Models, MNIST, Neural Networks, Python

• Generative Adversarial Networks – Key Milestones and State of the Art - Apr 24, 2019.
  
  We provide an overview of Generative Adversarial Networks (GANs), discuss challenges in GANs learning, and examine two promising GANs: the RadialGAN, designed for numbers, and the StyleGAN, which does style transfer for images.

  GANs, Generative Adversarial Network, NVIDIA

• Machine Learning and Deep Link Graph Analytics: A Powerful Combination - Apr 23, 2019.
  
  We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.

  Fraud Detection, Graph Analytics, Graph Databases, Machine Learning, XAI

• 2019 Best Masters in Data Science and Analytics – Online - Apr 23, 2019.
  
  We provide an updated comprehensive and objective survey of online Masters in Analytics and Data Science, including rankings, tuition, and duration of the education program.

  Data Analytics, Data Science, Education, Master of Science, MS in Analytics, MS in Data Science, Online Education

• Was it Worth Studying a Data Science Masters? - Apr 23, 2019.
  
  As I started to apply for Data Science roles it quickly became apparent that I was lacking two key skills: applying Machine Learning and coding

  Advice, Career, Data Science, Data Scientist, UK

• Easy Way to Scrape Data from Website By Yourself - Apr 22, 2019.
  
  Introducing Octoparse, a simple cloud-based website data scrapper that will let you extract any web data in real-time and coding is not needed.

  Cloud Computing, Octoparse, Web Scraping

• The Mueller Report Word Cloud: A brief tutorial in R - Apr 22, 2019.
  
  Word clouds are simple visual summaries of the mostly frequently used words in a text, presenting essentially the same information as a histogram but are somewhat less precise and vastly more eye-catching. Get a quick sense of the themes in the recently released Mueller Report and its 448 pages of legal content.

  Donald Trump, Politics, R, Word Cloud

• How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides - Apr 22, 2019.
  
  To learn ALL the skills sets in data science is next to impossible as the scope is way too wide. There’ll always be some skills (technical/non-technical) that data scientists don’t know or haven’t learned as different businesses require different skill sets.

  Advice, Career, Data Science, Data Science Education, Data Scientist, Online Education

• The Rise of Generative Adversarial Networks - Apr 19, 2019.
  
  A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.

  Art, Deepfakes, GANs, Generative Adversarial Network, Ian Goodfellow

• Data Visualization in Python: Matplotlib vs Seaborn - Apr 19, 2019.
  
  Seaborn and Matplotlib are two of Python's most powerful visualization libraries. Seaborn uses fewer syntax and has stunning default themes and Matplotlib is more easily customizable through accessing the classes.

  Advice, Data Visualization, Matplotlib, Python, Seaborn

• Sisense BloX – Go Beyond Dashboards - Apr 18, 2019.
  
  Introducing Sisense BloX, the tool that allows you to integrate your business platforms inside your dashboards using prebuilt templates. Users stay within the dashboard environment and go from understanding insights to taking action—in one click.

  Analytics, Dashboard, Sisense

• 3 Big Problems with Big Data and How to Solve Them - Apr 18, 2019.
  
  We discuss some of the negatives of using big data, including false equivalences and bias, vulnerability to security breaches, protecting against unauthorized access and the lack of international standards for data privacy regulations.

  Advice, Bias, Big Data, Privacy, Security

• Distributed Artificial Intelligence: A primer on Multi-Agent Systems, Agent-Based Modeling, and Swarm Intelligence - Apr 18, 2019.
  
  Distributed Artificial Intelligence (DAI) is a class of technologies and methods that span from swarm intelligence to multi-agent technologies. It is one of the subsets of AI where simulation has greater importance that point-prediction.

  AI, Distributed Systems, Modeling, Swarm Intelligence

• How Optimization Works - Apr 18, 2019.
  
  Optimization problems are naturally described in terms of costs - money, time, resources - rather than benefits. In math it's convenient to make all your problems look the same before you work out a solution, so that you can just solve it the one time.

  Data Science, Data Scientist, Gradient Descent, Optimization, Prescriptive Analytics

• Best Data Visualization Techniques for small and large data - Apr 17, 2019.
  
  Data visualization is used in many areas to model complex events and visualize phenomena that cannot be observed directly, such as weather patterns, medical conditions or mathematical relationships. Here we review basic data visualization tools and techniques.

  Big Data, Charts, Data Visualization, Histogram, Sciforce

• Building a Flask API to Automatically Extract Named Entities Using SpaCy - Apr 17, 2019.
  
  This article discusses how to use the Named Entity Recognition module in spaCy to identify people, organizations, or locations in text, then deploy a Python API with Flask.

  API, Flask, NLP, Python

• How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms - Apr 16, 2019.
  
  We outline three different clustering algorithms - k-means clustering, hierarchical clustering and Graph Community Detection - providing an explanation on when to use each, how they work and a worked example.

  Algorithms, Clustering, Explained

• 2019 Best Masters in Data Science and Analytics – Europe Edition - Apr 16, 2019.
  
  We provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across Europe.

  Pages: 1 2

  Data Analytics, Data Science, Education, Europe, Master of Science, MS in Analytics, MS in Data Science

• Data Science with Optimus Part 2: Setting your DataOps Environment - Apr 16, 2019.
  
  Breaking down data science with Python, Spark and Optimus. Today: Data Operations for Data Science. Here we’ll learn to set-up Git, Travis CI and DVC for our project.

  Apache Spark, Data Operations, Data Science, Python, Workflow

• An introduction to explainable AI, and why we need it - Apr 15, 2019.
  
  We introduce explainable AI, why it is needed, and present the Reversed Time Attention Model, Local Interpretable Model-Agnostic Explanation and Layer-wise Relevance Propagation.

  AI, Explainable AI, LIME, Machine Learning, XAI

• Data Science with Optimus Part 1: Intro - Apr 15, 2019.
  
  With Optimus you can clean your data, prepare it, analyze it, create profilers and plots, and perform machine learning and deep learning, all in a distributed fashion, because on the back-end we have Spark, TensorFlow, Sparkling Water and Keras. It’s super easy to use.

  Apache Spark, Data Science, Python, Workflow

• How can quantum computing be useful for Machine Learning - Apr 12, 2019.
  
  We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.

  Machine Learning, Quantum Computing, SVM

• Why Data Scientists Need To Work In Groups - Apr 12, 2019.
  
  If you read this article you will see that the job of data scientist is NOT listed. The rest of this article will explore why it is true that data scientists need to work in groups.

  Career Advice, Data Scientist

• Beyond Siri, Google Assistant, and Alexa – what you need to know about AI Conversational Applications - Apr 10, 2019.
  
  We discuss industry trends in Artificial Intelligence with Vijay Ramakrishnan, a machine learning engineer and expert in conversational applications.

  AI, Alexa, Google, Siri, Virtual Assistant

• S2DS, a 5-week data science bootcamp helping analytical PhDs transition from academia to industry - Apr 9, 2019.
  
  Introducing Europe’s largest data science training programme. Five weeks of intensive, project-based training turning exceptional analytical PhDs and MScs into Data Scientists.

  Bootcamp, Data Science, Pivigo

• All you need to know about text preprocessing for NLP and Machine Learning - Apr 9, 2019.
  
  We present a comprehensive introduction to text preprocessing, covering the different techniques including stemming, lemmatization, noise removal, normalization, with examples and explanations into when you should use each of them.

  Data Preprocessing, Machine Learning, NLP, Python, Text Analysis, Text Mining

• Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? - Apr 9, 2019.
  
  Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.

  Algorithms, Data Science, Machine Learning, Poll

• Advice for New Data Scientists - Apr 8, 2019.
  
  We provide advice for junior data scientists as they begin their career, with tips and commentary from a tech lead at Airbnb.

  Advice, Beginners, Data Scientist

• Spatio-Temporal Statistics: A Primer - Apr 5, 2019.
  
  Marketing scientist Kevin Gray asks University of Missouri Professor Chris Wikle about Spatio-Temporal Statistics and how it can be used in science and business.

  Interview, Spatio-Temporal, Statistics

• Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 5, 2019.
  
  Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.

  AI, Data Science, Deep Learning, Keras, Machine Learning, NLP, Reinforcement Learning, TensorFlow, U. of Washington, UC Berkeley, Unsupervised Learning

• Training a Champion: Building Deep Neural Nets for Big Data Analytics - Apr 4, 2019.
  
  Introducing Sisense Hunch, the new way of handling Big Data sets that uses AQP technology to construct Deep Neural Networks (DNNs) which are trained to learn the relationships between queries and their results in these huge datasets.

  Big Data Analytics, Deep Learning, Neural Networks, Sisense, SQL

• Building a Recommender System - Apr 4, 2019.
  
  A beginners guide to building a recommendation system, with a step-by-step guide on how to create a content-based filtering system to recommend movies for a user to watch.

  Movies, Python, Recommendation Engine, Recommender Systems

• Predict Age and Gender Using Convolutional Neural Network and OpenCV - Apr 4, 2019.
  
  Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.

  Computer Vision, Convolutional Neural Networks, OpenCV, Python

• Getting started with NLP using the PyTorch framework - Apr 3, 2019.
  
  We discuss the classes that PyTorch provides for helping with Natural Language Processing (NLP) and how they can be used for related tasks using recurrent layers.

  Neural Networks, NLP, PyTorch, Recurrent Neural Networks

• How to DIY Your Data Science Education - Apr 3, 2019.
  
  Some people find the path of formal education works well for them, but this may not work for everyone, in every situation. Here are eight ways that you can take a DIY approach to your data science education.

  Books, Data Science, Data Science Education, MOOC, Podcast, Programming Languages, Youtube

• Top 8 Data Science Use Cases in Gaming - Apr 3, 2019.
  
  The understanding of the data value for optimization and improvement of gaming makes specialists search for new ways to apply data science and its benefits in the gaming business. Therefore, various specific data science use cases appear. Here is our list of the most efficient and widely applied data science use cases in gaming.

  Data Science, Gaming, Use Cases

• 7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them - Apr 2, 2019.
  
  Without the right visualization tools, raw data is of little use. Data visualization helps present the data in an interactive visual format. Here are the qualities to look for in a data visualization tool.

  Big Data, Data Visualization, Domo, Plotly, Power BI, QlikView, Sisense, Tableau

• Which Face is Real? - Apr 2, 2019.
  
  Which Face Is Real? was developed based on Generative Adversarial Networks as a web application in which users can select which image they believe is a true person and which was synthetically generated. The person in the synthetically generated photo does not exist.

  Deep Learning, GANs, Generative Adversarial Network, Neural Networks, NVIDIA, Python

• Top 10 Coding Mistakes Made by Data Scientists - Apr 2, 2019.
  
  Here is a list of 10 common mistakes that a senior data scientist — who is ranked in the top 1% on Stackoverflow for python coding and who works with a lot of (junior) data scientists — frequently sees.

  Data Science, Data Scientist, Mistakes, Programming