Social listening has a nasty habit of being completely soft—all about the words without the context of volume and velocity of conversation—or completely quantitative with… Read More »Getting the Complete Picture from Your Brand’s Social Listening
Analytics is still in a phase in many organizations where selling it internally to the stakeholders is the biggest challenge.Creating analytics is a cost- resource… Read More »Lean Analytics: The building blocks
What’s the real value of data science? Hailed as the sexiest job of the 21st century just a few years ago, there are rumors that… Read More »Bridging the Gap Between Data Science and DevOps
These articles were controversial in the sense that they highlighted the differences between data science and other disciplines, at a time when many believed that… Read More »15 Most Controversial Data Science Articles
During the last few years, the hottest word on everyone’s lip has been “productivity.” In the rapidly evolving Internet world, getting something done fast always… Read More »Boost Your Productivity With Amazon Web Services
“Hey, Andy, check out this data I have. What’s the best chart to show it?” I get asked this question a lot. My answer is… Read More »Fit the Chart to the Story, Not the Data
A simple way to find great articles and resources on popular subjects such as data science, machine learning, deep learning, Python, R , data sets,… Read More »R, Python, Machine Learning, Dataviz: Most Popular Resources
Summary: Which is the most critical element in data exploration, statistics or data visualization? The answer is a little like the lyric ‘love and marriage,… Read More »When Data Viz Trumps Statistics
If you are not an expert on Analytics then you must be curious about how one can use analytics for better optimization, forecasting and reporting. Or how… Read More »Caring about Analytics for HR
Guest blog post by Harry Powell, Head of Advanced Data Analytics at Barclays. I was at a meetup in Oxford recently and one of the speakers,… Read More »Big Data and Data Science. Some reflections on compensation levels
