- 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
- The Data Science Machine, or ‘How To Engineer Feature Engineering’ - Oct 22, 2015.
MIT researchers have developed what they refer to as the Data Science Machine, which combines feature engineering and an end-to-end data science pipeline into a system that beats nearly 70% of humans in competitions. Is this game-changing?
Automated, Data Science, Feature Engineering, Feature Extraction, MIT
- What Types of Questions Can Data Science Answer - Sep 29, 2015.
Data science has enabled us to solve complex and diverse problems by using machine learning and statistic algorithms. Here we have enumerated the common applications of supervised, unsupervised and reinforcement learning techniques
Pages: 1 2
Data Science, Use Cases
- 15 Mathematics MOOCs for Data Science - Sep 23, 2015.
The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.
Pages: 1 2
Applied Statistics, Coursera, Data Science, edX, Mathematics, MOOC, R, Udemy
- Top 10 Quora Data Science Writers and Their Best Advice - Sep 17, 2015.
Top Quora data science writers give their advice on pursuing a career in the field, approaching interviews, and selecting appropriate technologies.
Data Science, Quora, scikit-learn, Top 10
- The 123 Most Influential People in Data Science - Sep 15, 2015.
We used LittleBird algorithm to build a true Data Science influencer network by measuring how often influencers retweet other influencers. Top influencers include @hmason, @kdnuggets, @kaggle, @peteskomoroch, @mrogati, and @KirkDBorne.
About KDnuggets, Alex Salkever, Big Data Influencers, Data Science, Hilary Mason, Influencers, Kaggle, Kirk D. Borne, Silk.co
- A Great way to learn Data Science by simply doing it - Sep 11, 2015.
There are tons of great online resources out there we can pick up and learn them to become a master in data science. Here is a comprehensive list of data science course providers along with links to the data science courses.
Data Science, Data Science Education
- Data Science Data Architecture - Sep 10, 2015.
Data scientists are kind of a rare breed, who juggles between data science, business and IT. But, they do understand less IT than an IT person and understands less business than a business person. Which demands a specific workflow and data architecture.
Pages: 1 2
Big Data Architecture, Data Management, Data Science, Olav Laudy
- Salaries by Roles in Data Science and Business Intelligence - Sep 9, 2015.
Data Scientist is the hottest role. What's next? We present national average salaries, job title progression in career, job trends and skills for popular job titles in Data Science & Business Intelligence. Check out the salaries of related roles.
Business Intelligence, Data Science, Data Science Skills, Data Scientist, Salary, Trends
- 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more - Sep 4, 2015.
Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science.
Book, Brendan Martin, Data Mining, Data Science, Free ebook, Machine Learning, Python, R, SQL
- Paradoxes of Data Science - Aug 21, 2015.
There are many paradoxes, ironies and disconnects in today’s world of data science: pain points, things ignored, shoved under the rug, denied or paid lip.
Data Science, Data Science Skills, Myths, Thomas Ball
- 50+ Data Science and Machine Learning Cheat Sheets - Jul 14, 2015.
Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms.
Cheat Sheet, Data Science, Django, Hadoop, Machine Learning, Python, R
- Deep Learning and the Triumph of Empiricism - Jul 7, 2015.
Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?
Big Data, Data Science, Deep Learning, Mathematics, Statistics, Zachary Lipton
- Data Science and Big Data: Two very Different Beasts - Jul 6, 2015.
Creating artifact from the ore requires the tools, craftmanship and science. Same is the case of big data and data science, here we present the distinguishing factors between the ore and the artifact.
Big Data, Data Science, Sean McClure
- Using Ensembles in Kaggle Data Science Competitions – Part 2 - Jun 26, 2015.
Aspiring to be a Top Kaggler? Learn more methods like Stacking & Blending. In the previous post we discussed about ensembling models by ways of weighing, averaging and ranks. There is much more to explore in Part-2!
Competition, Data blending, Data Science, Kaggle, Netflix
- Top 20 R Machine Learning and Data Science packages - Jun 24, 2015.
We list out the top 20 popular Machine Learning R packages by analysing the most downloaded R packages from Jan-May 2015.
CRAN, Data Science, Machine Learning, R, R Packages, Top list
- Interview: Joseph Babcock, Netflix on Genie, Lipstick, and Other In-house Developed Tools - Jun 16, 2015.
We discuss role of analytics in content acquisition, data architecture at Netflix, organizational structure, and open-source tools from Netflix.
Data Science, ETL, In-house, Interview, Joseph Babcock, Netflix, Open Source, Tools
- Insights from Data Science Handbook - May 28, 2015.
Here you can find perspective of lead data scientists on the definitions ranging from data science, metrics selection while solving a problem, work ethics, the art of storytelling and why data science is important in todays world.
Data Science, Data Science Fellows, Data Science Jargon, DJ Patil, Handbook, Hilary Mason
- I’ve Been Replaced by an Analytics Robot - May 20, 2015.
A veteran statistician reflects on the journey from a statistician of the past to data scientist of today, how the work he used to do became automated, and what future can data scientists can expect.
Automation, Data Science, Future, History, Robots
- The Inconvenient Truth About Data Science - May 5, 2015.
Data is never clean, you will spend most of your time cleaning and preparing data, 95% of tasks do not require deep learning, and more inconvenient wisdom.
Advice, Data Cleaning, Data Science
- More Free Data Mining, Data Science Books and Resources - Mar 25, 2015.
More free resources and online books by leading authors about data mining, data science, machine learning, predictive analytics and statistics.
Book, Data Mining, Data Science, Free ebook, Machine Learning
- History of Data Science Infographic in 5 strands - Feb 17, 2015.
History of Data Science infographic presents key events in Data Science across 5 strands: Computer Science, Data Technology, Visualization, Mathematics/OR, and Statistics.
About Gregory Piatetsky, Data Science, History
- Automatic Statistician and the Profoundly Desired Automation for Data Science - Feb 17, 2015.
The Automatic Statistician project by Univ. of Cambridge and MIT is pushing ahead the frontiers of automation for the selection and evaluation of machine learning models. In general, what does automation mean to Data Science?
Automation, Cambridge, Data Cleaning, Data Science, Machine Learning, MIT, Modeling, Statistician
- Data Science’s Most Used, Confused, and Abused Jargon - Feb 10, 2015.
As data science has spread through the mainstream, so too has a dense vocabulary of ill-defined jargon. In a split-personality post, we offer several perspectives on many of data science's most confused terms.
Big Data Privacy, Data Science, Deep Learning, Zachary Lipton
- Predictions: 2015 Analytics and Data Science Hiring Market - Jan 13, 2015.
Thanks to Big Data, analytics have become inescapable. Forget the C-Suite if you’re not a Data Geek, recruiting for startups gets harder, analytics salary bands get a lift, and more 2015 predictions.
Apache Spark, Burtch Works, Data Science, Hiring, MOOC, Predictions for 2015, Salary, Startups
- Fundamental methods of Data Science: Classification, Regression And Similarity Matching - Jan 12, 2015.
Data classification, regression, and similarity matching underpin many of the fundamental algorithms in data science to solve business problems like consumer response prediction and product recommendation.
Classification, Data Science, Regression, Similarity
- Hot or Not: Data Science Trends in 2015 - Dec 24, 2014.
CrowdFlower infographic predicts the hot trends for data science in 2015 and which trends will fade away.
CrowdFlower, Data Democratization, Data Science, Infographic, Predictions for 2015, Social Good, Trends
- DrivenData: Data Science Competitions for Social Good - Nov 4, 2014.
DrivenData plans to bring cutting-edge practices in data science and crowdsourcing to some of the world's biggest social challenges and the organizations taking them on.
Competition, Crowdsourcing, Data Science, DrivenData, Nonprofit, Social Good
- Most Viewed Data Mining Talks at Videolectures - Sep 9, 2014.
Watch the top 25 most viewed popular data mining lectures on VideoLectures.NET to learn about topics ranging general big-data tutorials to monetizing data mining startups.
Big Data, Data Mining, Data Mining Training, Data Science, Tutorials, Videolectures
- Dataiku Data Science Studio - Aug 26, 2014.
Data Science Studio (DSS) from Dataiku is a complete Data Science software tool for developers and analysts,
which significantly shortens the time-consuming load-clean-train-test-deploy cycles of building predictive applications.
A community edition and a free trial available.
Data Mining Software, Data Preparation, Data Science, Dataiku, Florian Douetteau, Prediction
- Four main languages for Analytics, Data Mining, Data Science - Aug 18, 2014.
New KDnuggets Poll shows the growing dominance of four main languages for Analytics, Data Mining, and Data Science: R, SAS, Python, and SQL - used by 91% of data scientists - and decline in popularity of other languages, except for Julia and Scala.
Analytics Languages, Data Mining, Data Science, Julia, Poll, Python, R, SAS, Scala, SQL
- Data for Good: data-driven projects for social good - Jul 26, 2014.
Data for Good is an exciting new non-profit seeking to highlight the various data science projects and resources that can ultimately contribute to the social good.
Data Science, Government, Open Data, Social Good, Social Participation
- NYU Data Science Program – Things to Know - Jun 13, 2014.
Inside summary of NYU Data Science program launched last year, what it is, and what makes it special.
Data Science, Deep Learning, New York-NY, NYU, Ran Bi, Yann LeCun
- Data Science Last Mile - Jun 6, 2014.
This post discusses the Data Science "Last Mile", the final work to take the discovered insights and deliver them a highly usable format or integrate into a specific application.
Alpine, Data Science, Joel Horwitz, Predictive Analytics
- Guide to Data Science Cheat Sheets - May 12, 2014.
Selection of the most useful Data Science cheat sheets, covering SQL, Python (including NumPy, SciPy and Pandas), R (including Regression, Time Series, Data Mining), MATLAB, and more.
Cheat Sheet, Data Science, Python, R, SQL
- Is Data Scientist the right career path for you? Candid advice - Mar 28, 2014.
Candid advice from an industry veteran reveals the true picture behind the much-talked-about Data Scientist "glamour" and helps people have the right expectations for a Data Science career.
Advice, Career, Data Science, Data Scientist, Hadoop, Paco Nathan, Recommendation, Visualization
- Alpine Data Science Periodic Table - Feb 19, 2014.
One of the most clever giveaways at the recent Strata Conference in Santa Clara was a Periodic Table of Data Science from Alpine.
Alpine, Data Science, Periodic-Table, Strata 2014
- Split on Data Science Skills: Individual vs Team Approach - Jan 21, 2014.
The results of latest KDnuggets poll show an almost equal split between those who favor individual and those who favor the team approach. See the counterintuitive regional differences and interesting comments.
Data Science, Poll, Skills, Team
