Pattern Curators of the Cognitive Era
Machine learning has a critical dependency on human learning. But not just on Data Scientists, but on legions of people who legions of individuals who prepare training data to guide algorithms.
on Mar 31, 2016 in Curation, Data Curation, IBM Watson, Machine Learning
The Rise of Dark Data and How It Can Be Harnessed
Dark data isn’t just a small portion of big data, but the biggest and fastest growing. It holds massive potential for those who can harness it successfully.
on Mar 31, 2016 in Dark Data, Excel, Surveys
Don’t be afraid to Fail – Start Now with Data Science
An argument for why aspiring data scientists should stop waiting for permission and start doing data science.
on Mar 30, 2016 in Advice, Data Science, Failure, Success
HR Analytics Starter Kit – Intro to R
We review tools to help you start performing HR analytics with a focus on R platform, and providing useful examples for the HR and Workforce analytics using R.
on Mar 28, 2016 in HR, R, Use Cases, Workforce Analytics
Don’t Buy Machine Learning
In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it’s not even there because we plan it for a future phase after building the application first.
on Mar 28, 2016 in Advice, Industry, Machine Learning
How to combat financial fraud by using big data?
Financial fraud methods are becoming more sophisticated and the techniques to combat such attacks also need to evolve. Big data has brought with it novel fraud detection and prevention techniques such as behavioral analysis and real-time detection to give fraud fighting techniques a new perspective.
on Mar 25, 2016 in Alibaba, Banking, Big Data, Fraud, Fraud Detection, Fraud Prevention
Whether Evolution or Revolution, The Internet of Things is Here to Stay
The Internet of Things (IoT) is the next boom you need to know about, and insurance provider AIG has recently released a no-nonsense whitepaper providing an overview of the landscape in the space.
on Mar 25, 2016 in AIG, Internet of Things, IoT
Data Science Tools – Are Proprietary Vendors Still Relevant?
We examine and quantify the dramatic impact of open source tools like R and Python on SAS, IBM, Microsoft, and other proprietary Data Science vendors. We also investigate how open source tools were faring against each other, which are growing, which are falling, and look R versus Python debate.
on Mar 25, 2016 in Data Science Tools, IBM, Microsoft, Open Source, Python, R, SAS
Ethics In Machine Learning: What we learned from Tay chatbot fiasco?
As Microsoft chatbot Tay showed, Machine Learning brings us into a new world where our views on ethics and political correctness will be challenged. ML learns from us. In both good and bad ways, it reflects what we really are.
on Mar 25, 2016 in AI, Bots, Chatbot, Ethics, Microsoft, MLconf, Seattle, Twitter, WA
“Citizen Data Scientist” Revolution
The naysayers are on the wrong side of "citizen" Data Scientist debate. Business users already have self-service BI capabilities and make decisions whether they are statistically sound or not. We can’t stop them from making decisions but should make statistically sound decisions easier. This new approach is called Smart Data Discovery.
on Mar 24, 2016 in BeyondCore, Citizen Data Scientist, Gartner
When Big Data Means Bad Analytics
When analytics delivers disappointing results, it is often because there is not enough analytic expertise, and/or lack of understanding of a business objectives for using Big Data in the first place. To avoid failure, insist on high standards.
on Mar 21, 2016 in Big Data, Data Science Education, Failure, FICO, Hiring
AlphaGo is not the solution to AI
The field will be better off without an bust cycle it is important for people to keep and inform a balanced view of successes and their extent. AlphoGo might be a step forward for the AI community, but it is still no way close to the true AI.
on Mar 21, 2016 in AI, AlphaGo, Deep Learning, DeepMind, Go, John Langford
Analytics Hiring Strong, Staying In One Job Is Weak
With more companies jumping on the data-driven bandwagon, companies have been creating new roles and new data science and analytics teams. It is right time to make your move and land up in your dream job.
on Mar 21, 2016 in Analytics, Burtch Works, Career, Hiring
3 Telecom Developments Which impact IoT Analytics
Highlights and developments to watch from Mobile World Congress 2016 which will impact IoT analytics in future.
on Mar 18, 2016 in Analytics, IoT, Telecom
Big Data Will Rule Your Home
The "connected home" is the next frontier for Big Data, and soon our lives may be significantly impacted by the analytical firepower from the IoT. Would benefits outweigh the risks and how would you then feel if your fridge locks you out because your scales and wearables have sounded the warning signs?
on Mar 18, 2016 in Big Data, Connected Home, IoT, Privacy
Exclusive Interview with Alexander Gray, Skytree CEO: Fast, Automated, Machine Learning Software for Free?
We discuss how Skytree compares with competition, how does it perform relative to expert Data Scientists, how does Skytree Automodel compare to Deep Learning, and more.
on Mar 17, 2016 in Alexander Gray, Automated, Data Science Platform, Deep Learning, Skytree
Data is the New Everything
Data gets a lot of mainstream attention these days, and has been compared to all sorts of different things. This is a lighthearted look at some of the top suggestions from Google autocomplete when searching for the phrase "data is the new" something.
on Mar 17, 2016 in Data, Google, Oil & Gas, Search Engine
The Evolution of the Data Scientist
We trace the evolution of Data Science from ancient mathematics to statistics and early neural networks, to present successes like AlphaGo and self-driving car, and look into the future.
on Mar 16, 2016 in Automated, Data Scientist, Demis Hassabis, Evolution, Mathematics, Statistics
Career Advice to Data Scientists – Go Make More Money
Data Scientist should offer the enterprise more than the ability (and cost) of doing analysis, but behave as an executive with expertise in analysis and help lead the enterprise on decisions, investments, and operations.
on Mar 16, 2016 in Advice, Career, Data Science Skills, Skills
After 150 Years, the ASA Says No to p-values
The ASA has recently taken a position against p-values. Read the overview and opinion of a well-respected statistician to gain additional insight.
on Mar 15, 2016 in ASA, P-value, Statistics
Wind and Weather – Open Text Data Digest
It’s soothing to watch the wind flows cycle and clouds form and dissipate. Now an app called Windyty lets you navigate real-time and predictive views of the weather yourself, controlling the area, altitude, and variables such as temperature, air pressure, humidity, clouds, or precipitation.
on Mar 15, 2016 in Data Visualization, OpenText, Weather
How to tell a great analyst from a good analyst
Good analyst help businesses to stay in the competition, but great analyst sets the business apart from its competition. Learn more about how to be a great analyst by walking that extra mile.
on Mar 15, 2016 in Analyst, Data Science Skills, Quandl
What Should Data Scientists Know About Psychology?
Due to training in the scientific method, data management, statistics/data analysis, subject matter expertise, and communicating results into substantive knowledge psychology researchers must have a solid understanding of data science and vice-versa.
on Mar 14, 2016 in Data Scientist, Methodology, Psychology
The Anchors of Trust in Data Analytics
An exploration of some of the critical questions and challenges emerging around trust in data and analytics. The four anchors of trust that will shape public confidence in D&A in the age of the analytical enterprise are highlighted.
on Mar 14, 2016 in Analytics, Big Data, Data Analytics, KPMG, Trust
When Good Advice Goes Bad
Consider these 4 examples of good statistical advice which, when misused, can go bad.
on Mar 14, 2016 in Andrew Gelman, Bayesian, Overfitting, P-value, Statistics
What is the influence of Big Data in Medicine?
The 360-degree customer view is the idea, that companies can get a complete view of customers by aggregating data from the various touch points that a user. And, big data is helping to materialize this idea, which will revolutionize the healthcare.
on Mar 14, 2016 in Big Data, Customer Analytics, Healthcare
Fraud Bots Mess Up Your Big Data
The bots that cause digital ad fraud also mess up analytics. When they create fake visits, pageviews, ad impressions, clicks, etc. those metrics are not real and should be corrected for.
on Mar 11, 2016 in Bots, Fraud Detection
The Data Science Puzzle, Explained
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.
on Mar 10, 2016 in Artificial Intelligence, Data Mining, Data Science, Deep Learning, Explained, Machine Learning
Practical Career Advice and Best Practices in Analytics
Being an analyst is not only a technical job it also has a peoples side to it. Given that many MBAs, engineers, and even non-quantitative graduates are interested in Analytics careers, we are sharing some advice on best practices for excelling with Analytics in your career.
on Mar 10, 2016 in Analytics Consultant, Career, Data Science Skills
Deep Learning: an Interview with Yoshua Bengio
Yoshua Bengio is a renowned figure in the machine learning and specifically deep learning, here is an interview with Yoshua about his thoughts on media interest in the field, future developments and more.
on Mar 8, 2016 in Deep Learning, RE.WORK, Yoshua Bengio
Fastest Growing Programming Languages and Computing Frameworks
A new model for ranking programming languages and predicting the growth of user adoption. Includes current language rankings and predictions.
on Mar 7, 2016 in Data Science, Javascript, Programming Languages, SQL, Trends
Trump vs Clinton – What are the Odds?
Even with 5% advantage for Clinton, statistical analysis and examining how undecided break towards these candidates, we estimate a 25%-30% chance that Trump would be elected president.
on Mar 7, 2016 in Donald Trump, Elections, Hillary Clinton, Politics
Nurture by Numbers – Big Data and Children
Driven by rising healthcare costs and competitions for top schools, more organisations and individuals are turning to Big Data and Analytics to try and give their children the upper hand.
on Mar 5, 2016 in Big Data, Children, Education, Healthcare, Privacy, Speech Recognition
The Rise Of The Robot
Atlas, the latest robot from Google's Boston Dynamics a pretty resilient chap. He can trudge through uneven snow, be knocked off his feet and get up again. and do work that can take place in any warehouse. We examine what it means for our future.
on Mar 3, 2016 in Google, Humans vs Machines, Robots
The Mirage of a Citizen Data Scientist
The term "citizen data scientist" has been irritating me recently. I explain why I think it both a bad term and a bad idea, and what we need instead.
on Mar 1, 2016 in Citizen Data Scientist, Data Analyst, Data Scientist, Gartner, Overfitting
Dynamic Data Visualization with PHP and MySQL: Election Spending
Learn how to fetch data from MySQL database using PHP and create dynamic charts with that data, using an interesting example of New Hampshire primary election spending.
on Mar 1, 2016 in Data Visualization, FusionCharts, MySQL, PHP
Data Science and Disability
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.
on Mar 1, 2016 in Data Science, Disability, Healthcare
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