Search results for "visualization"

1696 documents found out of 7187 total.

• Everything a Data Scientist Should Know About Data Management">Everything a Data Scientist Should Know About Data Management

  For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.

  https://www.kdnuggets.com/2019/10/data-scientist-data-management.html

• How to Write Web Apps Using Simple Python for Data Scientists

  Convert your Data Science Projects into cool apps easily without knowing any web frameworks.

  https://www.kdnuggets.com/2019/10/write-web-apps-using-simple-python-data-scientists.html

• How to Become a (Good) Data Scientist – Beginner Guide">How to Become a (Good) Data Scientist – Beginner Guide

  A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.

  https://www.kdnuggets.com/2019/10/good-data-scientist-beginner-guide.html

• Top 7 Things I Learned in my Data Science Masters

  Even though I’m still in my studies, here’s a list of the most important things I’ve learned (as of yet).

  https://www.kdnuggets.com/2019/10/top-7-things-learned-data-science-masters.html

• Activation maps for deep learning models in a few lines of code">Activation maps for deep learning models in a few lines of code

  We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code.

  https://www.kdnuggets.com/2019/10/activation-maps-deep-learning-models-lines-code.html

• Multi-Task Learning – ERNIE 2.0: State-of-the-Art NLP Architecture Intuitively Explained

  The tech giant Baidu unveiled its state-of-the-art NLP architecture ERNIE 2.0 earlier this year, which scored significantly higher than XLNet and BERT on all tasks in the GLUE benchmark. This major breakthrough in NLP takes advantage of a new innovation called “Continual Incremental Multi-Task Learning”.

  https://www.kdnuggets.com/2019/10/multi-task-learning-ernie-sota-nlp-architecture.html

• A European Approach to Master’s Degrees in Data Science">A European Approach to Master’s Degrees in Data Science

  Data science education in Europe has been reevaluated and new recommendations are leading the way to the next generation of data science Master's courses to better support and train students.

  https://www.kdnuggets.com/2019/10/european-approach-masters-data-science.html

• Natural Language in Python using spaCy: An Introduction

  This article provides a brief introduction to working with natural language (sometimes called “text analytics”) in Python using spaCy and related libraries.

  https://www.kdnuggets.com/2019/09/natural-language-python-using-spacy-introduction.html

• 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python

  “I want to learn machine learning and artificial intelligence, where do I start?” Here.

  https://www.kdnuggets.com/2019/09/5-beginner-friendly-steps-learn-machine-learning-data-science-python.html

• Which Data Science Skills are core and which are hot/emerging ones?">Which Data Science Skills are core and which are hot/emerging ones?

  We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.

  https://www.kdnuggets.com/2019/09/core-hot-data-science-skills.html

• Explore the world of Bioinformatics with Machine Learning">Explore the world of Bioinformatics with Machine Learning

  The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.

  https://www.kdnuggets.com/2019/09/explore-world-bioinformatics-machine-learning.html

• Can graph machine learning identify hate speech in online social networks?

  Online hate speech is a complex subject. Follow this demonstration using state-of-the-art graph neural network models to detect hateful users based on their activities on the Twitter social network.

  https://www.kdnuggets.com/2019/09/graph-machine-learning-hate-speech-social-networks.html

• 10 Great Python Resources for Aspiring Data Scientists">10 Great Python Resources for Aspiring Data Scientists

  This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey.

  https://www.kdnuggets.com/2019/09/10-great-python-resources-aspiring-data-scientists.html

• I wasn’t getting hired as a Data Scientist. So I sought data on who is.">I wasn’t getting hired as a Data Scientist. So I sought data on who is.

  Instead of focusing on skills thought to be required of data scientists, we can look at what they have actually done before.

  https://www.kdnuggets.com/2019/09/getting-hired-data-scientist-sought-data.html

• Python Libraries for Interpretable Machine Learning">Python Libraries for Interpretable Machine Learning

  In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models.

  https://www.kdnuggets.com/2019/09/python-libraries-interpretable-machine-learning.html

• An Overview of Topics Extraction in Python with Latent Dirichlet Allocation

  A recurring subject in NLP is to understand large corpus of texts through topics extraction. Whether you analyze users’ online reviews, products’ descriptions, or text entered in search bars, understanding key topics will always come in handy.

  https://www.kdnuggets.com/2019/09/overview-topics-extraction-python-latent-dirichlet-allocation.html

• Top 10 Data Science Use Cases in Energy and Utilities

  In this article, we will consider the most vivid data science use cases in the industry of energy and utilities.

  https://www.kdnuggets.com/2019/09/top-10-data-science-use-cases-energy-utilities.html

• Emoji Analytics

  Emoji is becoming a global language understandable by anyone who expresses... emotion. With the pervasiveness of these little Unicode blocks, we can perform analytics on their use throughout social media to gain insight into sentiments around the world.

  https://www.kdnuggets.com/2019/08/emoji-analytics.html

• R Users’ Salaries from the 2019 Stackoverflow Survey

  Let’s take a look on what R users are saying about their salaries. Note that the following results could be biased because of unrepresentative and in some cases small samples.

  https://www.kdnuggets.com/2019/08/r-users-salaries-2019-stackoverflow-survey.html

• Object-oriented programming for data scientists: Build your ML estimator">Object-oriented programming for data scientists: Build your ML estimator

  Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.

  https://www.kdnuggets.com/2019/08/object-oriented-programming-data-scientists-estimator.html

• Artificial Intelligence vs. Machine Learning vs. Deep Learning: What is the Difference?

  Over the past few years, artificial intelligence continues to be one of the hottest topics. And in order to work effectively with it, you need to understand its constituent parts.

  https://www.kdnuggets.com/2019/08/artificial-intelligence-vs-machine-learning-vs-deep-learning-difference.html

• Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch">Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch

  Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.

  https://www.kdnuggets.com/2019/08/numpy-neural-networks-computational-graphs.html

• Detecting stationarity in time series data

  Explore how to determine if your time series data is generated by a stationary process and how to handle the necessary assumptions and potential interpretations of your result.

  https://www.kdnuggets.com/2019/08/stationarity-time-series-data.html

• How to Become More Marketable as a Data Scientist">How to Become More Marketable as a Data Scientist

  As a data scientist, you are in high demand. So, how can you increase your marketability even more? Check out these current trends in skills most desired by employers in 2019.

  https://www.kdnuggets.com/2019/08/marketable-data-scientist.html

• Top KDnuggets tweets, Aug 07-13: Deep Learning Cheat Sheets; 12 NLP Researchers, Practitioners To Follow

  Deep Learning Cheat Sheets; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Knowing Your Neighbours: Machine Learning on Graphs.

  https://www.kdnuggets.com/2019/08/top-tweets-aug07-13.html

• Keras Callbacks Explained In Three Minutes

  A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples.

  https://www.kdnuggets.com/2019/08/keras-callbacks-explained-three-minutes.html

• How a simple mix of object-oriented programming can sharpen your deep learning prototype

  By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.

  https://www.kdnuggets.com/2019/08/simple-mix-object-oriented-programming-sharpen-deep-learning-prototype.html

• Ten more random useful things in R you may not know about

  I had a feeling that R has developed as a language to such a degree that many of us are using it now in completely different ways. This means that there are likely to be numerous tricks, packages, functions, etc that each of us use, but that others are completely unaware of, and would find useful if they knew about them.

  https://www.kdnuggets.com/2019/07/ten-more-random-useful-things-r.html

• Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning">Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning

  Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations.

  https://www.kdnuggets.com/2019/07/best-podcasts-ai-analytics-data-science-machine-learning.html

• Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras">Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras

  Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.

  https://www.kdnuggets.com/2019/07/convolutional-neural-networks-python-tutorial-tensorflow-keras.html

• Top 13 Skills To Become a Rockstar Data Scientist">Top 13 Skills To Become a Rockstar Data Scientist

  Education, coding, SQL, big data platforms, storytelling and more. These are the 13 skills you need to master to become a rockstar data scientist.

  https://www.kdnuggets.com/2019/07/top-13-skills-become-rockstar-data-scientist.html

• Top Certificates and Certifications in Analytics, Data Science, Machine Learning and AI">Top Certificates and Certifications in Analytics, Data Science, Machine Learning and AI

  Here are the top certificates and certifications in Analytics, AI, Data Science, Machine Learning and related areas.

  https://www.kdnuggets.com/2019/07/top-certificates-analytics-data-science-machine-learning-ai.html

• Kaggle Kernels Guide for Beginners: A Step by Step Tutorial

  This is an attempt to hold the hands of a complete beginner and walk them through the world of Kaggle Kernels — for them to get started.

  https://www.kdnuggets.com/2019/07/kaggle-kernels-guide-beginners-tutorial.html

• What’s the Best Data Strategy for Enterprises: Build, buy, partner or acquire?

  Every large organization is investing heavily in building data solutions and tools. They are building data solutions from scratch when they could be taking advantage of readily available tools and solutions. Many organizations are re-inventing the wheel and wasting resources.

  https://www.kdnuggets.com/2019/07/best-data-strategy-enterprises-build-buy-partner-acquire.html

• The Evolution of a ggplot

  A step-by-step tutorial showing how to turn a default ggplot into an appealing and easily understandable data visualization in R.

  https://www.kdnuggets.com/2019/07/evolution-ggplot.html

• How to Make Stunning 3D Plots for Better Storytelling

  3D Plots built in the right way for the right purpose are always stunning. In this article, we’ll see how to make stunning 3D plots with R using ggplot2 and rayshader.

  https://www.kdnuggets.com/2019/07/stunning-3d-plots-better-storytelling.html

• Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference">Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference

  Researchers from MIT recently unveiled a new probabilistic programming language named Gen, a language which allow researchers to write models and algorithms from multiple fields where AI techniques are applied without having to deal with equations or manually write high-performance code.

  https://www.kdnuggets.com/2019/07/introducing-gen-language-progammable-inference.html

• The Death of Big Data and the Emergence of the Multi-Cloud Era">The Death of Big Data and the Emergence of the Multi-Cloud Era

  The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.

  https://www.kdnuggets.com/2019/07/death-big-data-multi-cloud-era.html

• Training a Neural Network to Write Like Lovecraft">Training a Neural Network to Write Like Lovecraft

  In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out.

  https://www.kdnuggets.com/2019/07/training-neural-network-write-like-lovecraft.html

• Annotated Heatmaps of a Correlation Matrix in 5 Simple Steps

  A heatmap is a graphical representation of data in which data values are represented as colors. That is, it uses color in order to communicate a value to the reader. This is a great tool to assist the audience towards the areas that matter the most when you have a large volume of data.

  https://www.kdnuggets.com/2019/07/annotated-heatmaps-correlation-matrix.html

• Top 8 Data Science Use Cases in Construction

  This article considers several of the most efficient and productive data science use cases in the construction industry.

  https://www.kdnuggets.com/2019/07/top-8-data-science-use-cases-construction.html

• How do you check the quality of your regression model in Python?

  Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.

  https://www.kdnuggets.com/2019/07/check-quality-regression-model-python.html

• An Overview of Outlier Detection Methods from PyOD – Part 1

  PyOD is an outlier detection package developed with a comprehensive API to support multiple techniques. This post will showcase Part 1 of an overview of techniques that can be used to analyze anomalies in data.

  https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html

• The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph

  Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it, in the next article we’ll go to the practice on how to do this.

  https://www.kdnuggets.com/2019/06/data-fabric-machine-learning-building-knowledge-graph.html

• Understanding Cloud Data Services">Understanding Cloud Data Services

  Ready to move your systems to a cloud vendor or just learning more about big data services? This overview will help you understand big data system architectures, components, and offerings with an end-to-end taxonomy of what is available from the big three cloud providers.

  https://www.kdnuggets.com/2019/06/understanding-cloud-data-services.html

• 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition">7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition

  Interested in mastering data preparation with Python? Follow these 7 steps which cover the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.

  https://www.kdnuggets.com/2019/06/7-steps-mastering-data-preparation-python.html

• How to Learn Python for Data Science the Right Way">How to Learn Python for Data Science the Right Way

  The biggest mistake you can make while learning Python for data science is to learn Python programming from courses meant for programmers. Avoid this mistake, and learn Python the right way by following this approach.

  https://www.kdnuggets.com/2019/06/python-data-science-right-way.html

• Become a Pro at Pandas, Python’s Data Manipulation Library

  Pandas is one of the most popular Python libraries for cleaning, transforming, manipulating and analyzing data. Learn how to efficiently handle large amounts of data using Pandas.

  https://www.kdnuggets.com/2019/06/pro-pandas-python-library.html

• If you’re a developer transitioning into data science, here are your best resources"> If you’re a developer transitioning into data science, here are your best resources

  This article will provide a background on the data scientist role and why your background might be a good fit for data science, plus tangible stepwise actions that you, as a developer, can take to ramp up on data science.

  https://www.kdnuggets.com/2019/06/developer-transitioning-data-science-best-resources.html

• What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem">What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem

  We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data.

  https://www.kdnuggets.com/2019/06/top-data-science-machine-learning-tools.html

• The Infinity Stones of Data Science">The Infinity Stones of Data Science

  Do you love data science 3000? Don't want to be embarrassed in front of the other analytics wizards? Aspire to be one of Earth's mightiest heroes, like Kevin Bacon? Help make data science a snap with these simple insights.

  https://www.kdnuggets.com/2019/06/infinity-stones-data-science.html

• Using the ‘What-If Tool’ to investigate Machine Learning models

  The machine learning practitioner must be a detective, and this tool from teams at Google enables you to investigate and understand your models.

  https://www.kdnuggets.com/2019/06/using-what-if-tool-investigate-machine-learning-models.html

• PyViz: Simplifying the Data Visualisation Process in Python">PyViz: Simplifying the Data Visualisation Process in Python

  There are python libraries suitable for basic data visualizations but not for complicated ones, and there are libraries suitable only for complex visualizations. Is there a single library that handles both these tasks efficiently? The answer is yes. It's PyViz

  https://www.kdnuggets.com/2019/06/pyviz-data-visualisation-python.html

• NLP and Computer Vision Integrated">NLP and Computer Vision Integrated

  Computer vision and NLP developed as separate fields, and researchers are now combining these tasks to solve long-standing problems across multiple disciplines.

  https://www.kdnuggets.com/2019/06/nlp-computer-vision-integrated.html

• The Whole Data Science World in Your Hands

  Testing MatrixDS capabilities on different languages and tools: Python, R and Julia. If you work with data you have to check this out.

  https://www.kdnuggets.com/2019/06/whole-data-science-world.html

• KDnuggets™ News 19:n21, Jun 5: Transitioning your Career to Data Science; 11 top Data Science, Machine Learning platforms; 7 Steps to Mastering Intermediate ML w. Python

  The results of KDnuggets 20th Annual Software Poll; How to transition to a Data Science career; Mastering Intermediate Machine Learning with Python ; Understanding Natural Language Processing (NLP); Backprop as applied to LSTM, and much more.

  https://www.kdnuggets.com/2019/n21.html

• Who is your Golden Goose?: Cohort Analysis

  Step-by-step tutorial on how to perform customer segmentation using RFM analysis and K-Means clustering in Python.

  https://www.kdnuggets.com/2019/05/golden-goose-cohort-analysis.html

• Animations with Matplotlib

  Animations make even more sense when depicting time series data like stock prices over the years, climate change over the past decade, seasonalities and trends since we can then see how a particular parameter behaves with time.

  https://www.kdnuggets.com/2019/05/animations-with-matplotlib.html

• Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint

  Social media has been gold for studying the way people communicate and behave, in this article I’ll show you the easiest way of analyzing tweets without the Twitter API and scalable for Big Data.

  https://www.kdnuggets.com/2019/05/analyzing-tweets-nlp-spark-optimus-twint.html

• Your Guide to Natural Language Processing (NLP)

  This extensive post covers NLP use cases, basic examples, Tokenization, Stop Words Removal, Stemming, Lemmatization, Topic Modeling, the future of NLP, and more.

  https://www.kdnuggets.com/2019/05/guide-natural-language-processing-nlp.html

• 6 Industries Warming up to Predictive Analytics and Forecasting

  Here are six sectors that are realizing how beneficial predictive analytics could be, embracing the possibilities of valuable insights extracted from such technology.

  https://www.kdnuggets.com/2019/05/6-industries-warming-up-predictive-analytics-forecasting.html

• The Data Fabric for Machine Learning – Part 1">The Data Fabric for Machine Learning – Part 1

  How the new advances in semantics and the data fabric can help us be better at Machine Learning

  https://www.kdnuggets.com/2019/05/data-fabric-machine-learning-part-1.html

• PyCharm for Data Scientists

  This article is a discussion of some of PyCharm's features, and a comparison with Spyder, an another popular IDE for Python. Read on to find the benefits and drawbacks of PyCharm, and an outline of when to prefer it to Spyder and vice versa.

  https://www.kdnuggets.com/2019/05/pycharm-data-scientists.html

• Customer Churn Prediction Using Machine Learning: Main Approaches and Models

  We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.

  https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

• What’s Going to Happen this Year in the Data World

  "If we wish to foresee the future of mathematics, our proper course is to study the history and present condition of the science." Henri Poncairé.

  https://www.kdnuggets.com/2019/05/whats-going-happen-this-year-data-world.html

• Best US/Canada Masters in Analytics, Business Analytics, Data Science

  In the final part of this series, we provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across the US and Canada.

  https://www.kdnuggets.com/2019/05/best-masters-data-science-analytics-us-canada.html

• Data Science vs. Decision Science

  Data science and decision science are related but still separate fields, so at some points, it might be hard to compare them directly. We attempted to show our vision of the commonalities, differences, and specific features of data science and decision science.

  https://www.kdnuggets.com/2019/05/data-science-vs-decision-science.html

• The Third Wave Data Scientist">The Third Wave Data Scientist

  An extensive look at what skills are needed to make up the portfolio of the third wave of data scientists.

  https://www.kdnuggets.com/2019/05/third-wave-data-scientist.html

• The 3 Biggest Mistakes on Learning Data Science">The 3 Biggest Mistakes on Learning Data Science

  Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.

  https://www.kdnuggets.com/2019/05/biggest-mistakes-learning-data-science.html

• Which Deep Learning Framework is Growing Fastest?

  In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity. TensorFlow was the champion of deep learning frameworks and PyTorch was the youngest framework. How has the landscape changed?

  https://www.kdnuggets.com/2019/05/which-deep-learning-framework-growing-fastest.html

• Interview Questions for Data Science – Three Case Interview Examples

  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.

  https://www.kdnuggets.com/2019/04/interview-questions-data-science.html

• Top Data Science and Machine Learning Methods Used in 2018, 2019">Top Data Science and Machine Learning Methods Used in 2018, 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.

  https://www.kdnuggets.com/2019/04/top-data-science-machine-learning-methods-2018-2019.html

• The most desired skill in data science">The most desired skill in data science

  What is the biggest skill gap in data science according to hiring managers looking for hire recent graduates? Hint: it’s not coding.

  https://www.kdnuggets.com/2019/04/most-desired-skill-data-science.html

• Projects to Include in a Data Science Portfolio

  “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.”

  https://www.kdnuggets.com/2019/04/projects-include-data-science-portfolio.html

• Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own

  Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.

  https://www.kdnuggets.com/2019/04/graduating-gans-understanding-generative-adversarial-networks.html

• Machine Learning and Deep Link Graph Analytics: A Powerful Combination

  We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.

  https://www.kdnuggets.com/2019/04/machine-learning-graph-analytics.html

• 2019 Best Masters in Data Science and Analytics – Online

  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.

  https://www.kdnuggets.com/2019/04/best-masters-data-science-analytics-online.html

• The Mueller Report Word Cloud: A brief tutorial in R

  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.

  https://www.kdnuggets.com/2019/04/mueller-report-word-cloud-brief-tutorial-r.html

• How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides">How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides

  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.

  https://www.kdnuggets.com/2019/04/data-science-ultimate-questions-answers-aspiring-data-scientists.html

• Sisense BloX – Go Beyond Dashboards

  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.

  https://www.kdnuggets.com/2019/04/sisense-blox-beyond-dashboards.html

• 3 Big Problems with Big Data and How to Solve Them

  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.

  https://www.kdnuggets.com/2019/04/3-big-problems-big-data.html

• 2019 Best Masters in Data Science and Analytics – Europe Edition">2019 Best Masters in Data Science and Analytics – Europe Edition

  We provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across Europe.

  https://www.kdnuggets.com/2019/04/best-masters-data-science-analytics-europe.html

• Data Science with Optimus Part 2: Setting your DataOps Environment">Data Science with Optimus Part 2: Setting your DataOps Environment

  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.

  https://www.kdnuggets.com/2019/04/data-science-with-optimus-part-2-setting-dataops-environment.html

• Data Science with Optimus Part 1: Intro

  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.

  https://www.kdnuggets.com/2019/04/data-science-with-optimus-part-1-intro.html

• Why Data Scientists Need To Work In Groups">Why Data Scientists Need To Work In Groups

  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.

  https://www.kdnuggets.com/2019/04/why-data-scientists-need-work-groups.html

• Spatio-Temporal Statistics: A Primer

  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.

  https://www.kdnuggets.com/2019/04/spatio-temporal-statistics-primer.html

• Top 8 Data Science Use Cases in Gaming

  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.

  https://www.kdnuggets.com/2019/04/top-8-data-science-use-cases-gaming.html

• Interpolation in Autoencoders via an Adversarial Regularizer

  Adversarially Constrained Autoencoder Interpolation (ACAI; Berthelot et al., 2018) is a regularization procedure that uses an adversarial strategy to create high-quality interpolations of the learned representations in autoencoders.

  https://www.kdnuggets.com/2019/03/interpolation-autoencoders-adversarial-regularizer.html

• How to Choose the Right Chart Type

  This article presents an infographic for choosing which chart type is most useful in a given scenario. The infographic and chart types are then explored for greater clarity.

  https://www.kdnuggets.com/2019/03/how-choose-right-chart-type.html

• The Four Levels of Analytics Maturity

  We outline our four-step model to categorize how successfully a company uses analytics by its ability to show the analytics, uncover underlying trends, and take action based on them.

  https://www.kdnuggets.com/2019/03/four-levels-analytics-maturity.html

• Checklist for Debugging Neural Networks

  Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.

  https://www.kdnuggets.com/2019/03/checklist-debugging-neural-networks.html

• Top 8 Data Science Use Cases in Manufacturing

  Data science is said to change the manufacturing industry dramatically. Let's take under consideration several data science use cases in manufacturing that have already become common and brought benefits to the manufacturers.

  https://www.kdnuggets.com/2019/03/top-8-data-science-use-cases-manufacturing.html

• Artificial Neural Networks Optimization using Genetic Algorithm with Python">Artificial Neural Networks Optimization using Genetic Algorithm with Python

  This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.

  https://www.kdnuggets.com/2019/03/artificial-neural-networks-optimization-genetic-algorithm-python.html

• Another 10 Free Must-Read Books for Machine Learning and Data Science">Another 10 Free Must-Read Books for Machine Learning and Data Science

  Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.

  https://www.kdnuggets.com/2019/03/another-10-free-must-read-books-for-machine-learning-and-data-science.html

• Deconstructing BERT, Part 2: Visualizing the Inner Workings of Attention

  In this post, the author shows how BERT can mimic a Bag-of-Words model. The visualization tool from Part 1 is extended to probe deeper into the mind of BERT, to expose the neurons that give BERT its shape-shifting superpowers.

  https://www.kdnuggets.com/2019/03/deconstructing-bert-part-2-visualizing-inner-workings-attention.html

• On Building Effective Data Science Teams

  We take a look at the qualities that make a successful data team in order to help business leaders and executives create better AI strategies.

  https://www.kdnuggets.com/2019/03/building-effective-data-science-teams.html

• Comparing MobileNet Models in TensorFlow

  MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.

  https://www.kdnuggets.com/2019/03/comparing-mobilenet-models-tensorflow.html

• Top 7 Data Science Use Cases in Travel

  To satisfy all the needs of the growing number of consumers and process enormous data chunks, data science algorithms are vital. Let’s consider several of widespread and efficient data science use cases in the travel industry.

  https://www.kdnuggets.com/2019/02/top-7-data-science-use-cases-travel.html

• Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters

  Google’s BERT algorithm has emerged as a sort of “one model to rule them all.” BERT builds on two key ideas that have been responsible for many of the recent advances in NLP: (1) the transformer architecture and (2) unsupervised pre-training.

  https://www.kdnuggets.com/2019/02/deconstructing-bert-distilling-patterns-100-million-parameters.html

• Simple Yet Practical Data Cleaning Codes

  Real world data is messy and needs to be cleaned before it can be used for analysis. Industry experts say the data preprocessing step can easily take 70% to 80% of a data scientist's time on a project.

  https://www.kdnuggets.com/2019/02/simple-yet-practical-data-cleaning-codes.html

• Python Data Science for Beginners">Python Data Science for Beginners

  Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.

  https://www.kdnuggets.com/2019/02/python-data-science-beginners.html

• How to Setup a Python Environment for Machine Learning">How to Setup a Python Environment for Machine Learning

  In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.

  https://www.kdnuggets.com/2019/02/setup-python-environment-machine-learning.html

• Top 10 Data Science Use Cases in Telecom

  In this article, we attempt to present the most relevant and efficient data science use cases in the field of telecommunication.

  https://www.kdnuggets.com/2019/02/top-10-data-science-use-cases-telecom.html

• An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning">An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning

  If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.

  https://www.kdnuggets.com/2019/02/introduction-scikit-learn-gold-standard-python-machine-learning.html

• Data Science For Our Mental Development

  In this blog, I aim to generalize how AI can help us with mental development in the future as well as discuss some of the present-day solutions.

  https://www.kdnuggets.com/2019/02/data-science-mental-development.html

• Neural Networks – an Intuition

  Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.

  https://www.kdnuggets.com/2019/02/neural-networks-intuition.html

• Top 10 Technology Trends of 2019">Top 10 Technology Trends of 2019

  This article outlines 10 top trending technologies for 2019, a list which covers diverse topics such as security, IoT, reinforcement learning, energy sustainability, smart cities, and much more.

  https://www.kdnuggets.com/2019/02/top-10-technology-trends-2019.html

• How I used NLP (Spacy) to screen Data Science Resumes

  A real life example of when using NLP can help filter down a list of candidates for a job opening, with full source code and methodology.

  https://www.kdnuggets.com/2019/02/nlp-spacy-data-science-resumes.html

• Trending Deep Learning Github Repositories

  Check these pair of resources for trending and top GitHub deep learning repositories for some new ideas on what to be looking out for.

  https://www.kdnuggets.com/2019/02/trending-top-deep-learning-github-repositories.html

• 7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition">7 Steps to Mastering Basic Machine Learning with Python — 2019 Edition

  With a new year upon us, I thought it would be a good time to revisit the concept and put together a new learning path for mastering machine learning with Python. With these 7 steps you can master basic machine learning with Python!

  https://www.kdnuggets.com/2019/01/7-steps-mastering-basic-machine-learning-python.html

• Airbnb Rental Listings Dataset Mining

  An Exploratory Analysis of Airbnb’s Data to understand the rental landscape in New York City.

  https://www.kdnuggets.com/2019/01/airbnb-rental-listings-dataset-mining.html

• The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them

  Because big data touches almost every industry across the board, those who aren’t already working in data and analytics will soon be utilizing the technology for its undeniable business benefits. Whichever way you slice it, the future of work is through data.

  https://www.kdnuggets.com/2019/01/top-jobs-data-science.html

• 2018’s Top 7 R Packages for Data Science and AI

  This is a list of the best packages that changed our lives this year, compiled from my weekly digests.

  https://www.kdnuggets.com/2019/01/vazquez-2018-top-7-r-packages.html

• Automated Machine Learning in Python

  An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.

  https://www.kdnuggets.com/2019/01/automated-machine-learning-python.html

• Word Embeddings & Self-Supervised Learning, Explained

  There are many algorithms to learn word embeddings. Here, we consider only one of them: word2vec, and only one version of word2vec called skip-gram, which works well in practice.

  https://www.kdnuggets.com/2019/01/burkov-self-supervised-learning-word-embeddings.html

• Ontology and Data Science">Ontology and Data Science

  In simple words, one can say that ontology is the study of what there is. But there is another part to that definition that will help us in the following sections, and that is ontology is usually also taken to encompass problems about the most general features and relations of the entities which do exist.

  https://www.kdnuggets.com/2019/01/ontology-data-science.html

• The 6 Most Useful Machine Learning Projects of 2018

  Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.

  https://www.kdnuggets.com/2019/01/6-most-useful-machine-learning-projects-2018.html

• Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated

  Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  https://www.kdnuggets.com/2019/01/active-blogs-ai-analytics-data-science.html

• How to solve 90% of NLP problems: a step-by-step guide">How to solve 90% of NLP problems: a step-by-step guide

  Read this insightful, step-by-step article on how to use machine learning to understand and leverage text.

  https://www.kdnuggets.com/2019/01/solve-90-nlp-problems-step-by-step-guide.html

• Practical Apache Spark in 10 Minutes

  Check out this series of articles on Apache Spark. Each part is a 10 minute tutorial on a particular Apache Spark topic. Read on to get up to speed using Spark.

  https://www.kdnuggets.com/2019/01/practical-apache-spark-10-minutes.html

• Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data

  This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more.

  https://www.kdnuggets.com/2019/01/principles-database-management-practical-guide.html

• 4 Myths of Big Data and 4 Ways to Improve with Deep Data

  There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.

  https://www.kdnuggets.com/2019/01/4-myths-big-data-deep-data.html

• Comparison of the Text Distance Metrics

  There are many different approaches of how to compare two texts (strings of characters). Each has its own advantages and disadvantages and is good only for a range of specific use cases.

  https://www.kdnuggets.com/2019/01/comparison-text-distance-metrics.html

• Papers with Code: A Fantastic GitHub Resource for Machine Learning">Papers with Code: A Fantastic GitHub Resource for Machine Learning

  Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out.

  https://www.kdnuggets.com/2018/12/papers-with-code-fantastic-github-resource-machine-learning.html

• Comparison of the Top Speech Processing APIs

  There are two main tasks in speech processing. First one is to transform speech to text. The second is to convert the text into human speech. We will describe the general aspects of each API and then compare their main features in the table.

  https://www.kdnuggets.com/2018/12/activewizards-comparison-speech-processing-apis.html

• Supervised Learning: Model Popularity from Past to Present

  An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?

  https://www.kdnuggets.com/2018/12/supervised-learning-model-popularity-from-past-present.html

• Synthetic Data Generation: A must-have skill for new data scientists

  A brief rundown of methods/packages/ideas to generate synthetic data for self-driven data science projects and deep diving into machine learning methods.

  https://www.kdnuggets.com/2018/12/synthetic-data-generation-must-have-skill.html

• KDnuggets Site Map

  About KDnuggets Awards and Honors for KDnuggets Companies, offering Bioinformatics products and solutions Data Science and Analytics products Consulting and Training Data Warehousing and OLAP Read more »

  https://www.kdnuggets.com/about/site-map.html

• A Guide to Decision Trees for Machine Learning and Data Science">A Guide to Decision Trees for Machine Learning and Data Science

  What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.

  https://www.kdnuggets.com/2018/12/guide-decision-trees-machine-learning-data-science.html

• Feature Engineering for Machine Learning: 10 Examples

  A brief introduction to feature engineering, covering coordinate transformation, continuous data, categorical features, missing values, normalization, and more.

  https://www.kdnuggets.com/2018/12/feature-engineering-explained.html