Data Science (1364)

• Data Science For Our Mental Development - Feb 11, 2019.
  
  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.

  Data Science, Development, Emotion

• Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry. - Feb 7, 2019.
  
  The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology.

  Agile, Data Science, Development, Project

• How I used NLP (Spacy) to screen Data Science Resumes - Feb 6, 2019.
  
  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.

  Data Science, Hiring, NLP, Resume

• From Good to Great Data Science, Part 1: Correlations and Confidence - Feb 5, 2019.
  
  With the aid of some hospital data, part one describes how just a little inexperience in statistics could result in two common mistakes.

  Correlation, Data Science, Python, Statistics

• The Essential Data Science Venn Diagram - Feb 4, 2019.
  
  A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.

  Analytics, Data Science, Machine Learning, Statistics, Venn Diagram

• What Is Dimension Reduction In Data Science? - Jan 31, 2019.
  
  An extensive introduction into Dimension Reduction, including a look at some of the different techniques, linear discriminant analysis, principal component analysis, kernel principal component analysis, and more.

  Data Science, Dimensionality Reduction, Linear Discriminant Analysis, Principal component analysis

• The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them - Jan 24, 2019.
  
  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.

  Business Analyst, Data Engineer, Data Science, Data Scientist, Hiring, Jobs

• Data Science Project Flow for Startups - Jan 24, 2019.
  
  The aim of this post, then, is to present the characteristic project flow that I have identified in the working process of both my colleagues and myself in recent years. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness.

  Data Science, Startups, Workflow

• How AI and Data Science is Changing the Utilities Industry - Jan 22, 2019.
  
  Together, artificial intelligence (AI) and data science are causing positive developments for the utilities providers that choose to investigate these things. Here are some examples of technology at work.

  AI, Data Science, Industry, Utilities

• 2018’s Top 7 R Packages for Data Science and AI - Jan 22, 2019.
  
  This is a list of the best packages that changed our lives this year, compiled from my weekly digests.

  Pages: 1 2

  AI, Data Science, R

• Why Applied MSc in Data Engineering? Data Engineers are in greater demand than Data Scientists - Jan 17, 2019.
  
  2 graduate programmes now available at Data ScienceTech Institute in France: Applied MSc in Data Engineering Applied MSc in Data Science & Artificial Intelligence, with enterprise level certifications included in each. There is a 100% conversion to an internship and 90% to a job contract.

  Data Science, Data ScienceTech Institute, France, Online Education

• Ontology and Data Science - Jan 16, 2019.
  
  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.

  Data Science, Ontology

• How to go from Zero to Employment in Data Science - Jan 15, 2019.
  
  We propose the quickest and surest way to go from zero experience to landing a job, either in data science generally, or specifically in a new programming language or a new technology.

  Career, Data Science, Hiring, Skills

• Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated - Jan 14, 2019.
  
  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.

  AI, Analytics, Big Data, Blogs, Data Mining, Data Science, Data Visualization, Machine Learning

• The SIAM Book Series on Data Science - Jan 11, 2019.
  
  SIAM is soliciting manuscripts for its new book series on the mathematical and computational foundations of data science.

  Book, Data Science, SIAM

• The Role of the Data Engineer is Changing - Jan 10, 2019.
  
  The role of the data engineer in a startup data team is changing rapidly. Are you thinking about it the right way?

  Data Engineer, Data Science, dbt, ETL

• A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
  
  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.

  Algorithms, Data Science, Decision Trees, Machine Learning, Python, scikit-learn

• 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
  
  Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.

  AI, Algorithms, Big Data, Data Science, Deep Learning, Machine Learning, MIT, NLP, Reinforcement Learning, U. of Washington, UC Berkeley, Yandex

• Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning - Dec 19, 2018.
  
  Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.

  Data Science, Deep Learning, Machine Learning, Pandas, Python, PyTorch, TensorFlow

• How will automation tools change data science? - Dec 18, 2018.
  
  This article provides an overview of recent trends in machine learning and data science automation tools and addresses how those tools will change data science.

  Automation, Data Science, dotData

• Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019 - Dec 18, 2018.
  
  This is a collection of data science, machine learning, analytics, and AI predictions for next year from a number of top industry organizations. See what the insiders feel is on the horizon for 2019!

  2019 Predictions, AI, Analytics, Data Science, Domino, dotData, Figure Eight, Industry, Knime, Machine Learning, MapR, MathWorks, OpenText, ParallelM, Salesforce, Splice Machine, Splunk

• Introduction to Statistics for Data Science - Dec 17, 2018.
  
  This tutorial helps explain the central limit theorem, covering populations and samples, sampling distribution, intuition, and contains a useful video so you can continue your learning.

  Data Science, Statistics

• Why You Shouldn’t be a Data Science Generalist - Dec 14, 2018.
  
  But it’s hard to avoid becoming a generalist if you don’t know which common problem classes you could specialize in in the fist place. That’s why I put together a list of the five problem classes that are often lumped together under the “data science” heading.

  Career Advice, Data Science, Data Scientist

• Learning Machine Learning vs Learning Data Science - Dec 11, 2018.
  
  We clarify some important and often-overlooked distinctions between Machine Learning and Data Science, covering education, scalable vs non-scalable jobs, career paths, and more.

  Career, Data Science, Education, Machine Learning

• Should you become a data scientist? - Dec 10, 2018.
  
  An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.

  Career, Data Science, Data Scientist, History, Machine Learning, Tips, Trends

• Common mistakes when carrying out machine learning and data science - Dec 6, 2018.
  
  We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!

  Data Preparation, Data Science, Data Visualization, Machine Learning, Missing Values, Mistakes, Multicollinearity

• How to build a data science project from scratch - Dec 5, 2018.
  
  A demonstration using an analysis of Berlin rental prices, covering how to extract data from the web and clean it, gaining deeper insights, engineering of features using external APIs, and more.

  Berlin, Data Preparation, Data Science, Real Estate, Web Scraping

• 6 Step Plan to Starting Your Data Science Career - Dec 5, 2018.
  
  When people want to launch data science careers but haven't made the first move, they're in a scenario that's understandably daunting and full of uncertainty. Here are six steps to get started.

  Career, Data Science

• Kick Start Your Data Career! Tips From the Frontline - Dec 5, 2018.
  
  I am going to provide very interesting and useful tips through this blog series which will help students to kick start their career in Data.

  Career, Data Science, Tips

• Data Science Projects Employers Want To See: How To Show A Business Impact - Dec 4, 2018.
  
  The best way to create better data science projects that employers want to see is to provide a business impact. This article highlights the process using customer churn prediction in R as a case-study.

  Career Advice, Churn, Data Preparation, Data Science, R

• Top KDnuggets tweets, Nov 21-27: Intro to #DataScience for Managers – a mindmap; An Introduction to #AI - Nov 28, 2018.
  
  Also: An Introduction to #AI; Intuitively Understanding Convolutions for #DeepLearning; 10 Free Must-See Courses for Machine Learning and Data Science.

  Convolutional Neural Networks, Data Science, Manager, Top tweets

• My secret sauce to be in top 2% of a Kaggle competition - Nov 26, 2018.
  
  A collection of top tips on ways to explore features and build better machine learning models, including feature engineering, identifying noisy features, leakage detection, model monitoring, and more.

  Competition, Data Science, Kaggle

• Data Science Strategy Safari: Aligning Data Science Strategy to Org Strategy - Nov 26, 2018.
  
  The title of this post is derived by drawing inspiration from Mintzberg’s seminal work. In this post, I am attempting to take you on a safari through the data science strategy formulation process.

  Business, Data Science, Strategy

• Intro to Data Science for Managers - Nov 23, 2018.
  
  This mindmap contains a condensed introduction to the key data science concepts and techniques that have revolutionized the business landscape and became essential for making beneficial data-driven decisions

  Data Science, Manager, Visualization

• 6 Goals Every Wannabe Data Scientist Should Make for 2019 - Nov 22, 2018.
  
  Looking to embark on a new path as a data scientist? That goal may be worthy, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.

  Advice, Career, Data Science

• Cartoon: Thanksgiving, Big Data, and Turkey Data Science. - Nov 22, 2018.
  
  A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.

  Cartoon, Data Science, Thanksgiving

• The Big Data Game Board™ - Nov 19, 2018.
  
  Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!

  Big Data, Data Science, Games

• What is the Best Python IDE for Data Science? - Nov 14, 2018.
  
  Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you.

  Data Science, IDE, Jupyter, Programming, Python

• The 5 Basic Statistics Concepts Data Scientists Need to Know - Nov 13, 2018.
  
  Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!

  Data Science, Data Scientist, Statistics

• Self-Service Analytics and Operationalization – Why You Need Both - Nov 12, 2018.
  
  Get the guidebook / whitepaper for a look at how today's top data-driven companies scale their advanced analytics & machine learning efforts.

  Analytics, Data Science, Dataiku, Deployment, Self-service

• Best Practices for Using Notebooks for Data Science - Nov 8, 2018.
  
  Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.

  Best Practices, Data Science, Jupyter

• 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
  
  Check out a collection of free machine learning and data science courses to kick off your winter learning season.

  Data Science, Deep Learning, fast.ai, Google, Linear Algebra, Machine Learning, MIT, NLP, Reinforcement Learning, Stanford, Yandex

• The Most in Demand Skills for Data Scientists - Nov 2, 2018.
  
  Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. How should data scientists who want to be in demand by employers spend their learning budget?

  Career, Data Science, Data Science Skills, LinkedIn, Python vs R

• Data Science “Paint by the Numbers” with the Hypothesis Development Canvas - Nov 2, 2018.
  
  Now you are ready to take the next step from a Big Data MBA perspective by building off of the Business Model Canvas to flesh out the business use cases – or hypothesis – which is where we can become more effective at leveraging data and analytics to optimize our the business.

  Big Data, Business, Data Science

• How Data Science Is Improving Higher Education - Nov 1, 2018.
  
  Increasingly, colleges and universities, as well as governments, are using data science to improve the ways educational institutions do everything from recruiting to engaging with students to budgeting.

  Data Science, Education

• Graphs Are The Next Frontier In Data Science - Oct 18, 2018.
  
  GraphConnect 2018, Neo4j’s bi-annual conference, was held in New York City in mid-September. Read about what happened, and why graphs are the next big thing in data science.

  Conference, Data Science, Graph Analytics, Neo4j

• Applied Data Science: Solving a Predictive Maintenance Business Problem Part 3 - Oct 16, 2018.
  
  In this post we will expand our analysis to multiple variables and then see how intuitions we develop during the exploration phase, can lead to generating new features for modelling.

  Business Context, Data Science, Predictive Maintenance

• Using Confusion Matrices to Quantify the Cost of Being Wrong - Oct 11, 2018.
  
  The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.

  Confusion Matrix, Data Science, Machine Learning, Metrics, Predictive Modeling

• How To Learn Data Science If You’re Broke - Oct 9, 2018.
  
  A first-hand account on how to learn data science on a budget, with advice covering useful resources, a recommended curriculum, typical concepts, building a portfolio and more.

  Beginners, Career, Data Science, Data Science Education

• Understand Why ODSC is the Most Recommended Conference for Applied Data Science - Oct 4, 2018.
  
  Running 4 days, 40 training sessions, 50 workshops, and over 200 speakers, an ODSC conference offers unparalleled depth and breadth in deep learning, machine learning, and other data science topics. Save 20% offer ends tomorrow. Register now!

  CA, Data Science, ODSC, San Francisco

• 5 Reasons Why You Should Use Cross-Validation in Your Data Science Projects - Oct 2, 2018.
  
  In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with.

  Cross-validation, Data Science, Machine Learning

• Raspberry Pi IoT Projects for Fun and Profit - Sep 27, 2018.
  
  In this post, I will explain how to run an IoT project from the command line, without graphical interface, using Ubuntu Core in a Raspberry Pi 3.

  Pages: 1 2

  Data Science, IoT, Python, Raspberry Pi

• Diversity in Data Science: Overview and Strategy - Sep 24, 2018.
  
  We take a hard look at diversity within the tech industry, root causes, and potential solutions and highlight resources/initiatives that can connect readers with programs aiding their professional development.

  Career, Data Science, Diversity, Hiring, Trends, Women

• 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study - Sep 20, 2018.
  
  Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.

  Data Science, Machine Learning, Neural Networks

• A Winning Game Plan For Building Your Data Science Team - Sep 18, 2018.
  
  We need to understand the responsibilities, capabilities, expectations and competencies of the Data Engineer, Data Scientist and Business Stakeholder.

  Data Engineering, Data Science, Data Science Team

• Ethics + Data Science: opinion by DJ Patil, former US Chief Data Scientist - Sep 14, 2018.
  
  How much has data changed our lives over the past decade? Former US Chief Data Scientist DJ Patil investigates.

  Data Science, DJ Patil, Ethics, Social Good

• The Growing Participation of Women in the Data Science Community - Sep 14, 2018.
  
  We still have a long way to go before the gender representation becomes more equalized, but the field at large indicates hopeful trends about women working in the role or desiring to do so in the future.

  Data Science, STEM, Women

• Data Science Cheat Sheet - Sep 6, 2018.
  
  Check out this new data science cheat sheet, a relatively broad undertaking at a novice depth of understanding, which concisely packs a wide array of diverse data science goodness into a 9 page treatment.

  Cheat Sheet, Data Science

• What on earth is data science? - Sep 4, 2018.
  
  An overview and discussion around data science, covering the history behind the term, data mining, statistical inference, machine learning, data engineering and more.

  Data Mining, Data Science, Decision Making, Statistics

• 5 Resources to Inspire Your Next Data Science Project - Sep 4, 2018.
  
  In this post, my intention is provide some useful tips and resources to springboard you into your next data science project.

  Data Science, Resources

• UX Design Guide for Data Scientists and AI Products - Aug 21, 2018.
  
  Realizing that there is a legitimate knowledge gap between UX Designers and Data Scientists, I have decided to attempt addressing the needs from the Data Scientist’s perspective.

  AI, Data Science, Data Scientist, UI/UX

• Interpreting a data set, beginning to end - Aug 20, 2018.
  
  Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.

  Analytics, Big Data, Data Science, Data Visualization, Machine Learning, SAS, Statistics, t-SNE

• Project Hydrogen, new initiative based on Apache Spark to support AI and Data Science - Aug 16, 2018.
  
  An introduction to Project Hydrogen: how it can assist machine learning and AI frameworks on Apache Spark and what distinguishes it from other open source projects.

  AI, Apache Spark, Data Science, Databricks, Distributed Computing, Production

• Data Scientist guide for getting started with Docker - Aug 14, 2018.
  
  Docker is an increasingly popular way to create and deploy applications through virtualization, but can it be useful for data scientists? This guide should help you quickly get started.

  Data Science, Data Scientist, Docker, Jupyter

• Programming Best Practices For Data Science - Aug 7, 2018.
  
  In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.

  Best Practices, Data Science, Pandas, Programming, Python

• DevOps for Data Scientists: Taming the Unicorn - Jul 27, 2018.
  
  How do we version control the model and add it to an app? How will people interact with our website based on the outcome? How will it scale!?

  Data Science, Data Scientist, DevOps, Unicorn, Version Control

• Data Science For Business: 3 Reasons You Need To Learn The Expected Value Framework - Jul 26, 2018.
  
  This article highlights the importance of learning the expected value framework in data science, covering classification, maximization and testing.

  Business, Business Value, Data Science, H2O

• How to Build a Data Science Portfolio - Jul 25, 2018.
  
  This post will include links to where various data science professionals (data science managers, data scientists, social media icons, or some combination thereof) and others talk about what to have in a portfolio and how to get noticed.

  Advice, Career, Data Science, Portfolio, Resume, Social Media

• Cookiecutter Data Science: How to Organize Your Data Science Project - Jul 24, 2018.
  
  A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

  Data Science, Programming, Project, Python

• Building A Data Science Product in 10 Days - Jul 23, 2018.
  
  At startups, you often have the chance to create products from scratch. In this article, the author will share how to quickly build valuable data science products, using his first project at Instacart as an example.

  Data Science, Instacart, Product

• Explaining the 68-95-99.7 rule for a Normal Distribution - Jul 19, 2018.
  
  This post explains how those numbers were derived in the hope that they can be more interpretable for your future endeavors.

  Data Analysis, Data Science, Normal Distribution, Python, Statistics

• The 4 Levels of Data Usage in Data Science - Jul 9, 2018.
  
  This is an overview of the 4 levels, or "buckets," of data usage in business, starting at monitoring and progressing to automation.

  Automation, Ben Lorica, Business, Data Science, O'Reilly

• Cartoon: How is Data Science Different From Religion? - Jul 8, 2018.
  
  This difference between Data Science and Religion is not what you expect ...

  Cartoon, Data Science, Religion

• 5 of Our Favorite Free Visualization Tools - Jul 5, 2018.
  
  5 key free data visualization tools that can provide flexible and effective data presentation.

  Analytics, D3.js, Data Science, Data Visualization, Free Software, R, Tableau

• Why a Professional Association for Data Scientists is a Bad Idea - Jul 2, 2018.
  
  This post presents the argument against having a professional association for data scientists.

  Certification, Data Science, Data Science Education, SIGKDD, Trends

• Automated Machine Learning vs Automated Data Science - Jul 2, 2018.
  
  Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.

  Automated Data Science, Automated Machine Learning, Data Science, Machine Learning

• Top 20 Python Libraries for Data Science in 2018 - Jun 27, 2018.
  
  Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.

  Pages: 1 2

  Bokeh, Data Science, Keras, Matplotlib, NLTK, numpy, Pandas, Plotly, Python, PyTorch, scikit-learn, SciPy, Seaborn, TensorFlow, XGBoost

• 5 Data Science Projects That Will Get You Hired in 2018 - Jun 26, 2018.
  
  A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.

  Data Preparation, Data Science, Data Visualization, Hiring, Jupyter, Machine Learning

• Data Science Predicting The Future - Jun 19, 2018.
  
  In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.

  Data Science, Forecasting, Machine Learning, Programming Languages, Regression

• Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray - Jun 15, 2018.
  
  While KDnuggets takes no side, we present the informative and respectful back and forth as we believe it has value for our readers. We hope that you agree.

  AI, Computer Science, Data Science, Judea Pearl, Statistics

• Advice For Applying To Data Science Jobs - Jun 13, 2018.
  
  A comprehensive guide to applying for a job in data science, covering the application, interview and offer stage.

  Advice, Career, Data Science, Jobs

• The What, Where and How of Data for Data Science - Jun 12, 2018.
  
  Here we will take data science apart and build it back up to a coherent and manageable concept. Bear with us!

  Big Data, Data Science

• DIY Deep Learning Projects - Jun 8, 2018.
  
  Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.

  Computer Vision, Data Science, Deep Learning, LinkedIn, Neural Networks, OpenCV, Python

• How (dis)similar are my train and test data? - Jun 7, 2018.
  
  This articles examines a scenario where your machine learning model can fail.

  Data Science, Datasets, Feature Selection, Machine Learning, Training Data

• Command Line Tricks For Data Scientists - Jun 7, 2018.
  
  Aspiring to master the command line should be on every developer’s list, especially data scientists. Learning the ins and outs of your terminal will undeniably make you more productive.

  Data Science, Data Science Tools, Data Scientist

• The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R? - Jun 6, 2018.
  
  We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.

  Anaconda, Apache Spark, Data Science, Keras, Machine Learning, Open Source, Poll, Python, R, RapidMiner, Scala, scikit-learn, TensorFlow

• Resources For Women In Data Science and Machine Learning - Jun 4, 2018.
  
  A comprehensive list of resources for Women in Data Science and Machine Learning, including a list of useful tech groups and published lists for finding Women speakers.

  Data Science, Diversity, Meetings, Resources, Women

• The Book of Why - Jun 1, 2018.
  
  Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.

  Bayesian Networks, Causality, Data Science, Judea Pearl, Simpson's Paradox, Statistics

• A Beginner’s Guide to the Data Science Pipeline - May 29, 2018.
  
  On one end was a pipe with an entrance and at the other end an exit. The pipe was also labeled with five distinct letters: "O.S.E.M.N."

  Beginners, Data Science, Pipeline

• 10 More Free Must-Read Books for Machine Learning and Data Science - May 28, 2018.
  
  Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.

  Books, Data Science, ebook, Free ebook, Machine Learning

• Top 20 R Libraries for Data Science in 2018 - May 25, 2018.
  
  We have prepared an infographic of Top 20 R packages for data science, which covers the libraries main features and GitHub activities, as all of the libraries are open-source.

  Data Science, Infographic, R

• Data Science: 4 Reasons Why Most Are Failing to Deliver - May 24, 2018.
  
  Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.

  Data Science, Deployment, Domino, Failure, Production

• Scientific debt – what does it mean for Data Science? - May 23, 2018.
  
  This article analyses scientific debt - what it is and what it means for data science.

  Business, Data Engineering, Data Science, DataCamp, Technical Debt

• The Executive Guide to Data Science and Machine Learning - May 10, 2018.
  
  This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.

  Big Data, Business, Data Science, Machine Learning

• Torus for Docker-First Data Science - May 8, 2018.
  
  To help data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced a toolkit based on the popular cookiecutter project structure.

  Data Science, DevOps, Docker, Machine Learning Engineer, Open Source, Python

• Top Data Science, Machine Learning Courses from Udemy – May 2018 - May 8, 2018.
  
  Learn Machine Learning, Data Science, Python, Azure Machine Learning, and more with Udemy Mother's Day $9.99 sale - get top courses from leading instructors.

  Azure ML, Data Science, Machine Learning, Python, Udemy

• 2018 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2018.
  
  Vote in KDnuggets 19th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?

  Data Mining Software, Data Science, Machine Learning, Poll

• Skewness vs Kurtosis – The Robust Duo - May 4, 2018.
  
  Kurtosis and Skewness are very close relatives of the “data normalized statistical moment” family – Kurtosis being the fourth and Skewness the third moment, and yet they are often used to detect very different phenomena in data. At the same time, it is typically recommendable to analyse the outputs of both together to gather more insight and understand the nature of the data better.

  Data Science, Descriptive Analytics, Statistics

• Boost your data science skills. Learn linear algebra. - May 3, 2018.
  
  The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.

  Data Science, Linear Algebra, Mathematics, numpy, Python

• To Kaggle Or Not - May 2, 2018.
  
  Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.

  Advice, Competition, Data Science, Kaggle

• KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric - May 2, 2018.
  
  Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial

  Blockchain, Convolutional Neural Networks, Data Science, Machine Learning, Metrics, numpy, Python

• Data Science vs Machine Learning vs Data Analytics vs Business Analytics - May 1, 2018.
  
  This article gives a broad overview of data science and the various fields within it, including business analytics, data analytics, business intelligence, advanced analytics, machine learning, and AI.

  AI, Business, Business Analytics, Data Analytics, Data Science, Machine Learning

• The Dirty Little Secret Every Data Scientist Knows (but won’t admit) - Apr 26, 2018.
  
  Most people don’t realize, but the actual “fancy” machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!

  Data Cleaning, Data Preparation, Data Science, Machine Learning

• Data Science Interview Guide - Apr 25, 2018.
  
  Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).

  Pages: 1 2

  Data Science, Interview

• 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - Apr 17, 2018.
  
  It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.

  Book, Data Science, Ian Goodfellow, Machine Learning, Mathematics, Robert Tibshirani, Vladimir Vapnik

• Key Algorithms and Statistical Models for Aspiring Data Scientists - Apr 16, 2018.
  
  This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.

  Algorithms, Data Science, Machine Learning, Online Education, Statistics

• Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor - Apr 6, 2018.
  
  No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.

  Data Science, Descriptive Analytics, Statistics

• A Day in the Life of a Data Scientist: Part 4 - Apr 2, 2018.
  
  Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.

  Advice, Career, Data Science, Data Scientist

• 8 Common Pitfalls That Can Ruin Your Prediction - Mar 21, 2018.
  
  A good prediction can help your work and make it easier. But how can you be sure that your prediction is good? Here are some common pitfalls that you should avoid.

  Advice, Data Science, Outliers, Overfitting, Predictive Analytics

• Top 12 Essential Command Line Tools for Data Scientists - Mar 21, 2018.
  
  This post is a short introductory overview of 12 Unix-like operating system command line tools of value to data science tasks, and the data scientists who perform them.

  Data Exploration, Data Science, Data Science Tools

• Ranking Popular Distributed Computing Packages for Data Science - Mar 20, 2018.
  
  We examined 140 frameworks and distributed programing packages and came up with a list of top 20 distributed computing packages useful for Data Science, based on a combination of Github, Stack Overflow, and Google results.

  Apache Spark, Data Science, Distributed Systems, GitHub, Hadoop

• Descriptive Statistics: The Mighty Dwarf of Data Science - Mar 20, 2018.
  
  No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.

  Data Science, Descriptive Analytics, Statistics

• Multiscale Methods and Machine Learning - Mar 19, 2018.
  
  We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.

  Algorithms, Data Science, Deep Learning, Machine Learning, Statistics

• Your free 70-page guide to a career in data science - Mar 16, 2018.
  
  To help you become a Data Scientist, we put together a guide with answers to: how do you break into the profession? What skills do you need to become a data scientist? Where are best data science jobs?

  Advice, Career, Data Science, Springboard

• A Beginner’s Guide to Data Engineering – Part II - Mar 15, 2018.
  
  In this post, I share more technical details on how to build good data pipelines and highlight ETL best practices. Primarily, I will use Python, Airflow, and SQL for our discussion.

  Pages: 1 2

  AirBnB, Data Engineering, Data Science, ETL, Pipeline, Python, SQL

• 5 Things to Know Before Rushing to Start in Data Science - Mar 13, 2018.
  
  Strong math understanding, computing skills, critical thinking and presentations skills provide a strong foundation for a career in Data Science.

  Advice, Business Analytics, Career, Data Science, Data Science Education

• 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning - Mar 8, 2018.
  
  For the 2018 international women's day, we profile 18 inspiring women who lead the field in AI, Analytics, Big Data , Data science, and Machine Learning areas.

  AI, Big Data, Carla Gentry, Data Science, Fei-Fei Li, Hilary Mason, Jill Dyche, Meta Brown, Monica Rogati, Women

• Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box - Mar 8, 2018.
  
  The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.

  Andrew Ng, Data Science, Data Scientist, Deep Learning, Machine Learning

• Should You Ever Volunteer Your Data Skills for Free? - Mar 6, 2018.
  
  The question has probably come up of whether it’s ever okay to offer your data-related knowledge to people or organizations for free. Does taking that approach ever benefit you?

  Career, Data Science, Social Good

• Time Series for Dummies – The 3 Step Process - Mar 5, 2018.
  
  Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.

  Data Science, Deep Learning, Machine Learning, Predictive Modeling, Stationarity, Time Series

• Data Science in Fashion - Mar 2, 2018.
  
  Fashion industry is an extremely competitive and dynamic market. Trends and styles change with the blink of an eye. Data Science can be used here on historical data to predict the trends which will be “Hot” hence potentially saving a lot of time and money.

  Brands, Data Science, Fashion, Retail, Supply Chain

• How to Survive Your Data Science Interview - Mar 1, 2018.
  
  There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every company

  Advice, Career, Data Science, Interview

• Applied Data Science: Solving a Predictive Maintenance Business Problem Part 2 - Feb 20, 2018.
  
  In this post we will discuss further on how exploratory analysis can be used for getting insights for feature engineering.

  Data Analysis, Data Exploration, Data Science, Feature Engineering

• 5 Things You Need To Know About Data Science - Feb 19, 2018.
  
  Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists

  Algorithms, BI, Data Analytics, Data Mining, Data Science, Data Science Education, Data Scientist, Google Trends, Jobs, Machine Learning

• Histogram 202: Tips and Tricks for Better Data Science - Feb 15, 2018.
  
  We show how to make an ideal histogram, share some tips, and give examples. Let's dive into the world of binning.

  Data Science, Histogram, Statistics

• Data Science at the Command Line: Exploring Data - Feb 14, 2018.
  
  See what's available in the freely-available book "Data Science at the Command Line" by digging into data exploration in the terminal.

  Data Exploration, Data Science, Data Science Tools

• 7 Steps of a Data Science PoC – Get The Guidebook - Feb 12, 2018.
  
  Download a free copy of the white paper The 7 Steps to Driving a Successful Data Science POC for a detailed walk-through of the seven steps to running a successful POC.

  Data Science, Dataiku, Proof-of-concept, White Paper

• Top 15 Scala Libraries for Data Science in 2018 - Feb 9, 2018.
  
  For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.

  Apache Spark, Data Analysis, Data Science, Data Visualization, Machine Learning, NLP, Scala

• Why Data Scientists Must Know About Change Management - Feb 8, 2018.
  
  Change management may be seen as an opposite to data science, but in reality both are related. Without proper implementation, a data science project fails.

  Change Management, Data Science, Implementation

• Deep Feature Synthesis: How Automated Feature Engineering Works - Feb 7, 2018.
  
  Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.

  Automated Machine Learning, Automation, Data Science, Feature Engineering, Machine Learning