Data Science (1337)

• Data Science and the Imposter Syndrome - Sep 15, 2017.
  
  You are not the only one who wonders how much longer they can get away with pretending to be a data scientist. You are not the only one who has nightmares about being laughed out of your next interview.

  Bias, Data Science, Data Scientist

• Python vs R – Who Is Really Ahead in Data Science, Machine Learning? - Sep 12, 2017.
  
  We examine Google Trends, job trends, and more and note that while Python has only a small advantage among current Data Science and Machine Learning related jobs, this advantage is likely to increase in the future.

  Data Science, Google Trends, Jobs, Kaggle, Machine Learning, Python, Python vs R, R

• Putting the “Science” Back in Data Science - Sep 6, 2017.
  
  The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.

  Business, Data Science, Machine Learning, Process, Rubens Zimbres

• Search Millions of Documents for Thousands of Keywords in a Flash - Sep 1, 2017.
  
  We present a python library called FlashText that can search or replace keywords / synonyms in documents in O(n) – linear time.

  Algorithms, Data Science, GitHub, NLP, Python, Search, Search Engine, Text Mining

• 277 Data Science Key Terms, Explained - Sep 1, 2017.
  
  This is a collection of 277 data science key terms, explained with a no-nonsense, concise approach. Read on to find terminology related to Big Data, machine learning, natural language processing, descriptive statistics, and much more.

  Data Science, Explained, Key Terms

• Vital Statistics You Never Learned… Because They’re Never Taught - Aug 29, 2017.
  
  Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.

  Bayesian, Data Science, Machine Learning, Statistics

• 42 Steps to Mastering Data Science - Aug 25, 2017.
  
  This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.

  Data Preparation, Data Science, Deep Learning, Machine Learning, NoSQL, Python, SQL

• Data Science Primer: Basic Concepts for Beginners - Aug 11, 2017.
  
  This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types of patterns we can mine from data.

  Bias, Data Mining, Data Science, Distribution, Ensemble Methods, Statistics

• The Key to Data Monetization - Jul 31, 2017.
  
  While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.

  Analytics, Data Monetization, Data Science, Monetizing

• Top Quora Data Science Writers and Their Best Advice, Updated - Jul 24, 2017.
  
  Get some insight into tips and tricks, the future of the field, career advice, code snippets, and more from the top data science writers on Quora.

  Data Science, Quora, Top list

• Road Lane Line Detection using Computer Vision models - Jul 19, 2017.
  
  A tutorial on how to implement a computer vision data pipeline for road lane detection used by self-driving cars.
   
   

  Pages: 1 2

  AI, Computer Vision, Data Science, Machine Learning, Python, Self-Driving Car

• How GDPR Affects Data Science - Jul 17, 2017.
  
  Coming European GDPR directive affects data science practice mainly in 3 areas: limits on data processing and consumer profiling, a “right to an explanation” for automated decision-making, and accountability for bias and discrimination in automated decisions.

  Bias, Data Science, Europe, GDPR, Privacy, Thomas Dinsmore

• How to Build a Data Science Pipeline - Jul 14, 2017.
  
  Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.

  Data Science, Pipeline, Production

• What Advice Would You Give Your Younger Data Scientist Self? - Jul 5, 2017.
  
  I was asked this question recently via LinkedIn message: "What advice would you give your younger data scientist self?" The best piece of advice I honestly think I can give is this: Forget about "data science."

  Advice, Career, Data Science, Data Scientist

• Top 15 Python Libraries for Data Science in 2017 - Jun 13, 2017.
  
  Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.

  Pages: 1 2

  Data Mining, Data Science, Deep Learning, Machine Learning, Natural Language Processing, Python, Visualization

• Your Checklist to Get Data Science Implemented in Production - Jun 7, 2017.
  
  For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results. Here are the key things to keep in mind when you're working on your design-to-production pipeline.

  Checklist, Data Science, Dataiku, Production

• How HR Managers Use Data Science to Manage Talent for Their Companies - Jun 7, 2017.
  
  Data sciences can also be used by HR manager to create several estimates like the investment on talent pool, cost per hire, cost on training, and cost per employee. It provides better techniques for optimization, forecasting, and reporting.

  Big Data, Data Science, Decision Management, HR

• 7 Steps to Mastering Data Preparation with Python - Jun 2, 2017.
  
  Follow these 7 steps for mastering data preparation, covering the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.

  Pages: 1 2

  7 Steps, Data Preparation, Data Preprocessing, Data Science, Data Wrangling, Machine Learning, Pandas, Python

• Data Science for Newbies: An Introductory Tutorial Series for Software Engineers - May 31, 2017.
  
  This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.

  Apache Spark, Data Science, Jupyter, Machine Learning, Pandas, Python, Reddit, Scala, SQL

• Qualitative Research Methods for Data Science? - May 30, 2017.
  
  Why on Earth would a data scientist need to know about qualitative research? There are plenty of reasons. Here are a few.

  Data Science, Qualitative Analytics, Qualitative Research

• Will Data Science Eliminate Data Science? - May 25, 2017.
  
  There are elements of what we do which are AI complete. Eventually, Artificial General Intelligence will eliminate the data scientist, but it’s not around the corner.

  Automation, Data Science, Data Scientist

• Teaching the Data Science Process - May 17, 2017.
  
  Understanding the process requires not only wide technical background in machine learning but also basic notions of businesses administration; here I will share my experience on teaching the data science process.

  Data Science, Methodology, Process, Teaching

• 42 Essential Quotes by Data Science Thought Leaders - May 4, 2017.
  
  42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.

  Pages: 1 2

  Career, Data Science, Data Science Skills, Data Scientist, DJ Patil, Hilary Mason, Kirk D. Borne

• Do We Need Balanced Sampling? - May 4, 2017.
  
  Resampling is a solution which is very popular in dealing with class imbalance. Our research on churn prediction shows that balanced sampling is unnecessary.

  Customer Analytics, Data Mining, Data Science

• Did you know cavemen were already dealing with “Big Data” issues? - May 3, 2017.
  
  We know Big Data & Analytics are new & cutting edge technologies; but actually, human started using data & analytics techniques 5000 years ago. Let’s take a look.

  Big Data, Big Data Analytics, Data Analysis, Data Science, History

• What Do Frameworks Offer Data Scientists that Programming Languages Lack? - May 2, 2017.
  
  While programming languages will never be completely obsolete, a growing number of programmers (and data scientists) prefer working with frameworks and view them as the more modern and cutting-edge option for a number of reasons.

  Big Data, Data Science, Programming Languages

• The 2017 Data Scientist Report is now available - May 1, 2017.
  
  For the third year in a row, CrowdFlower surveyed data scientists (nearly 200 this year) from all manner of organizations, which they have compiled into one free report which you can be downloaded now. This year, lots of insights into the word of AI are included.

  CrowdFlower, Data Science, Report

• Models: From the Lab to the Factory - Apr 27, 2017.
  
  In this post, we’ll go over techniques to avoid these scenarios through the process of model management and deployment.

  Data Science, Modeling, SVDS

• Data Science for the Layman (No Math Added) - Apr 20, 2017.
  
  Written for the layman, this book is a practical yet gentle introduction to data science. Discover key concepts behind more than 10 classic algorithms, explained with real-world examples and intuitive visuals.

  Book, Data Science, Machine Learning, Tutorial

• Machine Learning Finds “Fake News” with 88% Accuracy - Apr 12, 2017.
  
  In this post, the author assembles a dataset of fake and real news and employs a Naive Bayes classifier in order to create a model to classify an article as fake or real based on its words and phrases.

  Data Science, Fake News, Machine Learning, Naive Bayes, Politics, Text Analytics

• Anonymization and the Future of Data Science - Apr 11, 2017.
  
  This post walks the reader through a real-world example of a "linkage" attack to demonstrate the limits of data anonymization. New privacy regulation, most notably the GDPR, are making it increasingly difficult to maintain a balance between privacy and utility.

  Big Data Privacy, Data Science, Law, Privacy

• 10 Free Must-Read Books for Machine Learning and Data Science - Apr 10, 2017.
  
  Spring. Rejuvenation. Rebirth. Everything’s blooming. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right.

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

• How to stay out of analytic rabbit holes: avoiding investigation loops and their traps - Apr 6, 2017.
  
  Data scientists tend to think that their main job is to answer complex questions and gain in-depth insights, bu in reality it is all about solving problems – and the only way to solve a problem is to act on it.

  Data Science, Methodology, Skills

• What Is Data Science, and What Does a Data Scientist Do? - Mar 23, 2017.
  
  This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.

  Career, Data Science, Data Scientist

• What Top Firms Ask: 100+ Data Science Interview Questions - Mar 22, 2017.
  
  Check this out: A topic wise collection of 100+ data science interview questions from top companies.

  Algorithms, Data Science, Google, Hadoop, Interview Questions, Machine Learning, Microsoft, Statistics, Uber

• The Most Underutilized Function in SQL - Mar 20, 2017.
  
  Find out why md5() is an SQL function that's used surprisingly often, and find out how -- and why -- you can use it yourself.

  Data Science, SQL

• 50 Companies Leading The AI Revolution, Detailed - Mar 16, 2017.
  
  We detail 50 companies leading the Artificial Intelligence revolution in AD Sales, CRM, Autotech, Business Intelligence and analytics, Commerce, Conversational AI/Bots, Core AI, Cyber-Security, Fintech, Healthcare, IoT, Vision, and other areas.

  AI, Business Analytics, Cybersecurity, Data Science, Healthcare, IoT, Machine Learning

• 7 Types of Data Scientist Job Profiles - Mar 15, 2017.
  
  There is no one profile for the Data Scientist, but I tried to make a few generic job profiles that can somewhat fit job descriptions of different companies. I think there is way too much variety, but I had to narrow down on a set of profiles. Check out the list.

  Career, Data Science, Data Scientist

• 17 More Must-Know Data Science Interview Questions and Answers, Part 3 - Mar 15, 2017.
  
  The third and final part of 17 new must-know Data Science interview questions and answers covers A/B testing, data visualization, Twitter influence evaluation, and Big Data quality.
   
   

  Pages: 1 2

  3Vs of Big Data, A/B Testing, Big Data, Data Quality, Data Science, Data Visualization, Influencers, Interview Questions, Statistics, Twitter

• 6 Business Concepts you need to become a Data Science Unicorn - Mar 13, 2017.
  
  Are you a data science professional and want to advance your career as Data Science Unicorn? Here we provide important business concepts and guidelines required for a data science techie to become a Unicorn.

  Bernard Marr, Business Intelligence, Business Strategy, Data Science, Unicorn, Youtube

• Neuroscience for Data Scientists: Understanding Human Behaviour - Mar 8, 2017.
  
  Neuroscience is very complex and advanced study of brain and people often misuse this term. Here we try to explain neuroscience terminologies and use of data science for such studies.

  Consumer Analytics, Data Science, Neuroscience

• How to Get a Data Science Job: A Ridiculously Specific Guide - Mar 7, 2017.
  
  Job hunting is challenging and sometimes frustrating task and we all experience it in our career. Here we provide a very specific and practical guide to get your dream job in Data Science world.

  Advice, Data Science, Data Science Skills, Glassdoor, Hiring

• Every Intro to Data Science Course on the Internet, Ranked - Mar 2, 2017.
  
  For this guide, I spent 10+ hours trying to identify every online intro to data science course offered as of January 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings.

  Coursera, Data Science, MOOC, Online Education, Ranking, Udacity, Udemy

• The Data Science Project Playbook - Mar 1, 2017.
  
  Keep your development team from getting mired in high-complexity, low-return projects by following this practical playbook.

  Data Science, Data Science Team

• 17 More Must-Know Data Science Interview Questions and Answers, Part 2 - Feb 22, 2017.
  
  The second part of 17 new must-know Data Science Interview questions and answers covers overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms.

  Algorithms, Data Science, Ensemble Methods, Feature Engineering, Feature Selection, High-dimensional, Interview Questions, Overfitting, Unsupervised Learning

• Creativity is Crucial in Data Science - Feb 20, 2017.
  
  Creativity and Innovation are integral to Data Science and going forward in the world of AI, those are the things that will give edge to the humans over the machines.

  Analytics Innovation, Creativity, Data Science, Innovation

• Apache Arrow and Apache Parquet: Why We Needed Different Projects for Columnar Data, On Disk and In-Memory - Feb 16, 2017.
  
  Apache Parquet and Apache Arrow both focus on improving performance and efficiency of data analytics. These two projects optimize performance for on disk and in-memory processing

  Apache, Apache Arrow, Apache Spark, Data Science, Dremio, In-Memory Computing, Machine Learning, Python

• 17 More Must-Know Data Science Interview Questions and Answers - Feb 15, 2017.
  
  17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
   
   

  Pages: 1 2

  Anomaly Detection, Bias, Classification, Data Science, Donald Trump, Interview Questions, Outliers, Overfitting, Variance

• Career Advice for Analytics & Data Science Professionals - Feb 13, 2017.
  
  In our experience working with many quantitative professionals over the years, the two main areas that contribute to long-term career growth are networking and continuous learning. Here is specific advice on how to do this and tips for Continuous Learning.

  Advice, Analytics, Burtch Works, Career, Data Science, Hiring

• The Data Science of NYC Taxi Trips: An Analysis & Visualization - Feb 10, 2017.
  
  This post outlines using Google BigQuery for an analysis of NYC Taxi Trips in the cloud, presenting the analysis and visualization in Tableau Public for readers to interact with.

  Data Science, Data Visualization, New York City, NY, Tableau, Taxi

• Getting Real World Results From Agile Data Science Teams - Feb 10, 2017.
  
  In this post, I’ll look at the practical ingredients of managing agile data science. By using agile data science methods, we help data teams do fast and directed work, and manage the inherent uncertainty of data science and application development.

  Agile, Data Science, Data Science Team, SVDS

• 5 Career Paths in Big Data and Data Science, Explained - Feb 6, 2017.
  
  Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.

  Big Data, Career, Data Analyst, Data Engineering, Data Infrastructure, Data Science, Explained, Machine Learning

• Identifying Variables That Might Be Better Predictors - Feb 2, 2017.
  
  This blog serves to expand on the approach that the data science team uses to identify (and quantify) which variables and metrics are better predictors of performance.

  Data Science, Feature Selection, Prediction, Predictive Analytics

• Fixing Deployment and Iteration Problems in CRISP-DM - Feb 1, 2017.
  
  Many analytic models are not deployed effectively into production while others are not maintained or updated. Applying decision modeling and decision management technology within CRISP-DM addresses this.

  Analytics, CRISP-DM, Data Mining, Data Science, Decision Modeling, IIA, Methodology

• Why the Data Scientist and Data Engineer Need to Understand Virtualization in the Cloud - Jan 25, 2017.
  
  This article covers the value of understanding the virtualization constructs for the data scientist and data engineer as they deploy their analysis onto all kinds of cloud platforms. Virtualization is a key enabling layer of software for these data workers to be aware of and to achieve optimal results from.

  Pages: 1 2

  Cloud, Data Engineer, Data Engineering, Data Science, Data Scientist, Virtualization

• Bringing Business Clarity To CRISP-DM - Jan 24, 2017.
  
  Many analytic projects fail to understand the business problem they are trying to solve. Correctly applying decision modeling in the Business Understanding phase of CRISP-DM brings clarity to the business problem.

  CRISP-DM, Data Mining, Data Science, Decision Modeling, Methodology, Predictive Analytics

• The Data Science Puzzle, Revisited - Jan 20, 2017.
  
  The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.

  AI, Big Data, Data Mining, Data Science, Deep Learning, Machine Learning

• 90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated) - Jan 17, 2017.
  
  Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  Pages: 1 2

  Big Data, Blogs, Data Mining, Data Science, Machine Learning

• The Most Popular Language For Machine Learning and Data Science Is … - Jan 11, 2017.
  
  When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.

  Data Science, Machine Learning, Programming Languages, Python, R, Scala

• A Tasty approach to data science - Jan 7, 2017.
  
  Data scientists at Foodpairing help brands cut down on the fuzzy front end of product development. The so-called Consumer Flavor Intelligence combines internet data and food science to create timely flavor line extensions.

  Coffee, Consumer Analytics, Data Science, Food

• Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall - Jan 5, 2017.
  
  Data science and predictive analytics can provide huge value, but they can mislead and backfire if not used with fail-safe measures. The author gives examples of such problems and provides guidelines to avoid them.

  Advice, Data Science, Model Performance, Overfitting, Predictive Analytics, Statistical Modeling

• Data Science Basics: Power Laws and Distributions - Dec 21, 2016.
  
  Power laws and other relationships between observable phenomena may not seem like they are of any interest to data science, at least not to newcomers to the field, but this post provides an overview and suggests how they may be.

  Beginners, Data Science, Distribution, Zipf's Law

• The 5 Basic Types of Data Science Interview Questions - Dec 16, 2016.
  
  Data science interviews are notoriously complex, but most of what they throw at you will fall into one of these categories.

  Data Science, Interview Questions, Springboard

• 50+ Data Science, Machine Learning Cheat Sheets, updated - Dec 14, 2016.
  
  Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.

  Cheat Sheet, Data Science, Django, Hadoop, Java, Machine Learning, MATLAB, Python, R

• Data Science Basics: What Types of Patterns Can Be Mined From Data? - Dec 14, 2016.
  
  Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.

  Beginners, Classification, Data Science, Frequent Pattern Mining, Outliers, Regression

• Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017 - Dec 13, 2016.
  
  Key themes included the polling failures in 2016 US Elections, Deep Learning, IoT, greater focus on value and ROI, and increasing adoption of predictive analytics by the "masses" of industry.
   
  

  Pages: 1 2

  2017 Predictions, Data Science, John Elder, Kirk D. Borne, Predictive Analytics, Tom Davenport

• Top Reasons Why Big Data, Data Science, Analytics Initiatives Fail - Dec 1, 2016.
  
  We examine the main reasons for failure in Big Data, Data Science, and Analytics projects which include lack of clear mandate, resistance to change, and not asking the right questions, and what can be done to address these problems.

  Big Data, Data Science, Failure, Project Fail

• 10 Tips to Improve your Data Science Interview - Nov 29, 2016.
  
  Interviewing is a skill. Here are 10 tips and resources to improve your Data Science interviews.

  Career, Data Science, Interview Questions, Skills

• Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning - Nov 23, 2016.
  
  Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.

  Big Data, Data Science, Facebook, Machine Learning

• Predictive Science vs Data Science - Nov 22, 2016.
  
  Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.

  Algorithms, Applied Statistics, Data Science, Prediction

• Data Avengers… Assemble! - Nov 19, 2016.
  
  The Avengers are perfectly capable of defending the Earth from our worst enemies. But are they up to the task of taking care of our data? Read this terribly punny "opinion" piece to find out.

  Comic, Data Science, Data Science Team

• Questions To Ask When Moving Machine Learning From Practice to Production - Nov 18, 2016.
  
  An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.

  Data Science, Deep Learning, Deployment, Machine Learning, Production

• Combining Different Methods to Create Advanced Time Series Prediction - Nov 16, 2016.
  
  The results from combining methods for time series prediction have been quite promising. However, the degree of error for long-term predictions is still quite high. Sounds like a challenge, so some new experiments are forthcoming!

  ARIMA, Data Science, Machine Learning, Prediction, Time Series

• Data Science and Big Data, Explained - Nov 14, 2016.
  
  This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.

  Beginners, Big Data, Data Science, Explained

• Top 10 Amazon Books in Data Mining, 2016 Edition - Nov 11, 2016.
  
  Given the ongoing explosion in interest for all things Data Mining, Data Science, Analytics, Big Data, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Data Mining category.

  Amazon, Books, Data Mining, Data Science

• Top KDnuggets tweets, Nov 2-8: 35 #OpenSource tools for Internet of Things; An Introduction to Ensemble Learners - Nov 9, 2016.
  
  21 Must-Know #DataScience Interview Questions with Answers; Big Data Science: Expectation vs. Reality; Big #DataScience: Expectation vs. Reality; The 10 Algorithms #MachineLearning Engineers Need to Know.

  Data Science, IoT, Top tweets

• How to Rank 10% in Your First Kaggle Competition - Nov 9, 2016.
  
  This post presents a pathway to achieving success in Kaggle competitions as a beginner. The path generalizes beyond competitions, however. Read on for insight into succeeding while approaching any data science project.

  Pages: 1 2 3 4

  Beginners, Competition, Data Science, Kaggle, Machine Learning, Python

• Practical Data Science: Building Minimum Viable Models - Nov 8, 2016.
  
  Data Science for startups based on data: Minimum Valuable Model, a new concept to avoid a full scale 95% accurate data science model. Want to know more about MVM? Have a look at this interesting article.

  Big Data, Data Science, Startups

• Data Science Basics: An Introduction to Ensemble Learners - Nov 8, 2016.
  
  New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.

  Beginners, Boosting, Data Science, Ensemble Methods

• Data Science 101: How to get good at R - Nov 1, 2016.
  
  Everybody talks about R programming, how to learn, how to be good at it. But in this article, Ari Lamstein tells us his story about why and how he started with R along with how to publish, market and monetise R projects.

  Ari Lamstein, Beginners, Data Science, Monetizing, Programming, R

• Learn Data Science in 8 (Easy) Steps - Oct 27, 2016.
  
  Want to learn data science? Check out these 8 (easy) steps to set out in the right direction!

  Pages: 1 2

  Big Data, Data Science, DataCamp, Machine Learning

• Big Data Science: Expectation vs. Reality - Oct 27, 2016.
  
  The path to success and happiness of the data science team working with big data project is not always clear from the beginning. It depends on maturity of underlying platform, their cross skills and devops process around their day-to-day operations.

  Big Data, Big Data Engineer, Data Science, Data Science Team, DevOps

• 5 EBooks to Read Before Getting into A Machine Learning Career - Oct 21, 2016.
  
  A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.

  Bayesian, Data Science, Deep Learning, Free ebook, Machine Learning, Reinforcement Learning

• Jupyter Notebook Best Practices for Data Science - Oct 20, 2016.
  
  Check out this overview of Jupyter notebook best practices as pertains to data science. Novice or expert, you may find something of use here.

  Data Science, Jupyter, Python, SVDS

• Top 10 Data Science Videos on Youtube - Oct 17, 2016.
  
  Learning and the future are the key topics in the recent Youtube videos on Data Science. The main questions revolve around: “how to become a Data Scientist”, “what is a data scientist”, and “where data science is going”. But why there is so little explanation of data science to the masses?

  Pages: 1 2

  Data Science, Data Scientist, DJ Patil, Online Education, R, Videolectures, Youtube

• EDISON Data Science Framework to define the Data Science Profession - Oct 14, 2016.
  
  EDISON Data Science Framework provides conceptual, instructional and policy components required to establish the Data Science profession.

  Certification, Data Science, Data Science Certificate, Data Science Education, Data Scientist

• Top 12 Interesting Careers to Explore in Big Data - Oct 12, 2016.
  
  From data driven strategies to decision making, the true worth of Big Data has been realized, and has led to opening up of amazing career choices. Check out these 12 interesting careers to explore in Big Data.

  Analyst, Big Data, Big Data Engineer, Business Analytics, Data Science, Data Scientist, Machine Learning Scientist, Simplilearn, Statistician

• KDnuggets™ News 16:n36, Oct 12: Battle of the Data Science Venn Diagrams; 9 Bizarre and Surprising Insights; ROI in Big Data Analytics - Oct 12, 2016.
  
  Battle of the Data Science Venn Diagrams; Top September Stories in KDnuggets; Open Images Dataset; Still Searching for ROI in Big Data Analytics?

  Big Data ROI, Data Science, Ethics, Venn Diagram

• Battle of the Data Science Venn Diagrams - Oct 6, 2016.
  
  First came Drew Conway's data science Venn diagram. Then came all the rest. Read this comparative overview of data science Venn diagrams for both the insight into the profession and the humor that comes along for free.

  Pages: 1 2

  Data Science, Drew Conway, Venn Diagram

• Top Data Scientist Claudia Perlich on Biggest Issues in Data Science - Sep 29, 2016.
  
  Find out what top data scientist Claudia Perlich believes are - and are not - the biggest issues in data science today, and why spending 80% of their time with data preparation is not a problem.

  Claudia Perlich, Data Science

• Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science - Sep 26, 2016.
  
  The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.

  Data Science, Deep Learning, IoT, Privacy, Robots

• Top 16 Active Big Data, Data Science Leaders on LinkedIn - Sep 23, 2016.
  
  Who are the most active Big Data, Data Science Influencers and Leaders on LinkedIn? We analyze the data and bring you the list of key people to follow.

  About Gregory Piatetsky, Bernard Marr, Big Data, Big Data Influencers, Carla Gentry, Data Science, DJ Patil, Influencers, LinkedIn, Tom Davenport

• The (Not So) New Data Scientist Venn Diagram - Sep 12, 2016.
  
  This post outlines a (relatively) new(er) Data Science-related Venn diagram, giving an update to Conway's classic, and providing further fuel for flame wars and heated disagreement.

  Data Science, Data Scientist, Drew Conway, Venn Diagram, Yanir Seroussi

• Introducing Dask for Parallel Programming: An Interview with Project Lead Developer - Sep 7, 2016.
  
  Introducing Dask, a flexible parallel computing library for analytics. Learn more about this project built with interactive data science in mind in an interview with its lead developer.

  Analytics, Continuum Analytics, Dask, Data Science, Distributed Computing, Parallelism, Python, Scientific Computing

• A Tutorial on the Expectation Maximization (EM) Algorithm - Aug 25, 2016.
  
  This is a short tutorial on the Expectation Maximization algorithm and how it can be used on estimating parameters for multi-variate data.

  Clustering, Data Science, Data Science Education, Predictive Analytics, Statistics

• How to Become a (Type A) Data Scientist - Aug 23, 2016.
  
  This post outlines the difference between a Type A and Type B data scientist, and prescribes a learning path on becoming a Type A.

  Advice, Data Science, Data Scientist, Internet of Things, IoT

• How to Become a Data Scientist – Part 1 - Aug 22, 2016.
  
  Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it!

  Pages: 1 2 3 4

  Career, Data Science, Data Science Skills, Data Scientist, Skills

• Cartoon: Facebook data science experiments and Cats - Aug 8, 2016.
  
  In honor of International Cat Day, we revisit KDnuggets cartoon that looks at the Facebook data science experiment on emotion manipulation and the importance of happy kittens.

  Cartoon, Cats, Data Science, Facebook

• The Core of Data Science - Aug 1, 2016.
  
  This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.

  Bayesian, Data Science, Data Science Team, Ontology

• Dataiku DSS 3.1 – Now with 5 ML Backends & Scala! - Aug 1, 2016.
  
  Introducing Dataiku DSS 3.1, with new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface.

  Data Science, Dataiku, Machine Learning, Scala

• Data Science of Visiting Famous Movie Locations in San Francisco - Jul 30, 2016.
  
  Using the Google Places API and IMDb API, we selected movie locations in The Golden City which every movie fan should visit while they are in town, and optimize sightseeing by solving the travelling salesman problem.

  CA, Data Science, Google, IMDb, Python, San Francisco

• Theoretical Data Discovery: Using Physics to Understand Data Science - Jul 29, 2016.
  
  Data science may be a relatively recent buzzword, but the collection of tools and techniques to which it refers come from a broad range of disciplines. Physics has a wealth of concepts to learn from, as evidenced in this piece.

  Data Science, Physics, Quantum Computing

• Data Science Statistics 101 - Jul 28, 2016.
  
  Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.

  Beginners, Data Science, Statistics

• Data Science for Beginners 1: The 5 questions data science answers - Jul 26, 2016.
  
  A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.

  Beginners, Data Science, Microsoft, Question answering

• Building a Data Science Portfolio: Machine Learning Project Part 1 - Jul 20, 2016.
  
  Dataquest's founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!

  Pages: 1 2

  Advice, Career, Data Science, Data Scientist, Dataquest, Machine Learning, Portfolio, Project, Python

• 10 Algorithm Categories for AI, Big Data, and Data Science - Jul 14, 2016.
  
  With a focus on leveraging algorithms and balancing human and AI capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.

  AI, Algorithms, Big Data, Data Science

• Storytelling: The Power to Influence in Data Science - Jul 6, 2016.
  
  Data scientists need to share results, which is different than talking shop with other data scientists. Read about influencing people and telling stories as a data scientist.

  Communication, Data Science, Storytelling

• 3 Key Ethics Principles for Big Data and Data Science - Jul 6, 2016.
  
  If ethics in general are important, should ethics training be a crucial element of the data science field?

  Big Data, Data Science, Ethics, Hui Xiong

• 7 Steps to Mastering SQL for Data Science - Jun 16, 2016.
  
  Follow these 7 steps to go from SQL data science newbie to seasoned practitioner quickly. No nonsense, just the necessities.

  Pages: 1 2

  7 Steps, Data Science, Database, Relational Databases, SQL

• Top 10 IPython Notebook Tutorials for Data Science and Machine Learning - Apr 22, 2016.
  
  A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.

  Data Science, Deep Learning, GitHub, IPython, Machine Learning, Python, Sebastian Raschka, TensorFlow

• 12 Inspiring Women In Data Science, Big Data - Apr 15, 2016.
  
  It’s been well documented that women don’t come close to parity in STEM fields with their counterparts. Could the rise of big data and data science offer women a clearer path to success in technology? Here’s a list of 12 inspiring women who work in big data and data

  Big Data, Data Science, InformationWeek, Women

• CrowdFlower 2016 Data Science Report - Apr 11, 2016.
  
  A new data science report with survey results related to the success and challenges of data scientists, and where data science is going as a discipline.

  CrowdFlower, Data Science, Report

• Basics of GPU Computing for Data Scientists - Apr 7, 2016.
  
  With the rise of neural network in data science, the demand for computationally extensive machines lead to GPUs. Learn how you can get started with GPUs & algorithms which could leverage them.

  Algorithms, CUDA, Data Science, GPU, NVIDIA

• 100 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning - Mar 29, 2016.
  
  Stay on top of your data science skills game! Here’s a list of about 100 most active and interesting blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  Pages: 1 2

  Big Data, Blogs, Data Science, Deep Learning, Hadoop, Machine Learning

• Lift Analysis – A Data Scientist’s Secret Weapon - Mar 22, 2016.
  
  Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.

  Data Science, Lift charts, Metrics

• The Data Science Game – Student Competition - Mar 17, 2016.
  
  The Data Science Game returns this year, with university students competing for dominance. Details for this iteration and further information is provided here.

  Competition, Data Science, France, Kaggle, Paris, Student Competition

• The Data Science Puzzle, Explained - Mar 10, 2016.
  
  The puzzle of data science is examined through the relationship between several key concepts in the data science realm. As we will see, far from being concrete concepts etched in stone, divergent opinions are inevitable; this is but another opinion to consider.

  Pages: 1 2

  Artificial Intelligence, Data Mining, Data Science, Deep Learning, Explained, Machine Learning

• The Data Science Process, Rediscovered - Mar 9, 2016.
  
  The Data Science Process is a relatively new framework for doing data science. It is compared to previous similar frameworks, and a discussion on process innovation versus repetition is then undertaken.

  Data Science

• Fastest Growing Programming Languages and Computing Frameworks - Mar 7, 2016.
  
  A new model for ranking programming languages and predicting the growth of user adoption. Includes current language rankings and predictions.

  Data Science, Javascript, Programming Languages, SQL, Trends

• The Data Science Process - Mar 4, 2016.
  
  What does a day in the data science life look like? Here is a very helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem.

  CRISP-DM, Data Science, Methodology, Springboard

• scikit-feature: Open-Source Feature Selection Repository in Python - Mar 3, 2016.
  
  scikit-feature is an open-source feature selection repository in python, with around 40 popular algorithms in feature selection research. It is developed by Data Mining and Machine Learning Lab at Arizona State University.

  Data Mining, Data Science, Feature Extraction, Feature Selection, Machine Learning, Python

• Data Science and Disability - Mar 1, 2016.
  
  Data Science and Artificial Intelligence has come to the forefront of technology in the last few years. Learn, how practitioners are taking a more philanthropic outlook on life, supporting people suffering with both physical and mental disabilities.

  Data Science, Disability, Healthcare

• 21 Must-Know Data Science Interview Questions and Answers, part 2 - Feb 20, 2016.
  
  Second part of the answers to 20 Questions to Detect Fake Data Scientists, including controlling overfitting, experimental design, tall and wide data, understanding the validity of statistics in the media, and more.

  Pages: 1 2 3

  Anomaly Detection, Data Science, Data Visualization, Overfitting, Recommender Systems

• Data Science Skills for 2016 - Feb 12, 2016.
  
  As demand for the hottest job is getting hotter in new year, the skill set required for them is getting larger. Here, we are discussing the skills which will be in high demand for data scientist which include data visualization, Apache Spark, R, python and many more.

  Apache Spark, CrowdFlower, Data Science, Python, Skills, SQL

• 21 Must-Know Data Science Interview Questions and Answers - Feb 11, 2016.
  
  KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.

  Pages: 1 2 3

  Bootstrap sampling, Data Science, Interview Questions, Kirk D. Borne, Precision, Recall, Regularization, Yann LeCun

• Top 10 TED Talks for the Data Scientists - Feb 9, 2016.
  
  TEDTalks have been a great platform for sharing ideas and inspirations. Here, we have sifted ten interesting talks for the data scientist from statistics, social media and economics domains.

  Data Science, Hans Rosling, Social Networks, Statistics, TED

• 5 Criteria To Determine If Your Data Is Ready For Serious Data Science - Dec 21, 2015.
  
  If your data is a large, relevant, accurate, connected, and you also have a sharp question, you ready to do some serious data science. If you’re weak on 1-2 points, don’t worry. But if most criteria are not true, you need to do more preparation.

  Data Preparation, Data Science, How to start

• 50 Useful Machine Learning & Prediction APIs - Dec 7, 2015.
  
  We present a list of 50 APIs selected from areas like machine learning, prediction, text analytics & classification, face recognition, language translation etc. Start consuming APIs!

  Pages: 1 2

  API, Data Science, Face Recognition, IBM Watson, Image Recognition, Machine Learning, NLP, Sentiment Analysis

• The hardest parts of data science - Nov 24, 2015.
  
  The hardest part of data science is not building an accurate model or obtaining good, clean data, but defining feasible problems and coming up with reasonable ways of measuring solutions.

  Data Science, Kaggle, Yanir Seroussi

• How Data Science increased the profitability of the e-commerce industry? - Nov 3, 2015.
  
  Data Science helps businesses provide a richer understanding of the customers by capturing and integrating the information on customers web behaviour, their life events, what led to the purchase of a product or service, how customers interact with different channels, and more.

  Pages: 1 2

  Data Science, DeZyre, Ecommerce, Recommendations