Seeking Simplicity’s Sweet Spot
Albert Einstein, you may have heard, was a clever man. He scribbled equations on blackboards, thought big thoughts, and all of that. But, allegedly, he also said Everything should be made as simple as possible, but not simpler. These words have resonated with me recently, as I’ve heard pitches from one company after another, all […]
Visualisation – the key that unlocks data’s value?
As the Big Data hype machine continues its relentless attempt to gobble everything in its path, new business units and entire new domains buying into the promise find themselves faced with unanticipated data volume and complexity. They see the potential for data-based decision making, but still face (short-term?) challenges in actually managing, analysing or interpreting […]
Thrive For Precision Not Accuracy
Jake Porway who was a data scientist at the New York Times R&D labs has a great perspective on why multi-disciplinary teams are important to avoid bias and bring in different perspective in data analysis. He discusses a story where data gathered by Über in Oakland suggested that prostitution arrests increased in Oakland on Wednesdays […]
A Data Scientist’s View On Skills, Tools, And Attitude
I recently came across this interview (thanks Dharini for the link!) with Nick Chamandy, a statistician a.k.a a data scientist at Google. I would encourage you to read it; it does have some great points. I found the following snippets interesting: Recruiting data scientists: When posting job opportunities, we are cognizant that people from different […]
Commoditizing Data Science
My ongoing conversations with several people continue to reaffirm my belief that Data Science is still perceived to be a sacred discipline and data scientists are perceived to be highly skilled statisticians who walk around wearing white lab coats. The best data scientists are not the ones who know the most about data but they […]
Objectively Inconsistent
During his recent visit to the office of 37 Signals, Jeff Bezos said, “to be consistently objective, one has to be objectively inconsistent.” I find this perspective very refreshing that is applicable to all things and all disciplines in life beyond just product design. As a product designer you need to have a series of […]
Data Scientists Should Be Design Thinkers
World Airline RoutesEvery company is looking for that cool data scientist who will come equipped with all the knowledge of data, domain expertise, and algorithms to turn around their business. The inconvenient truth is there are no such data scientists…
Data Is More Important Than Algorithms
Netflix Similarity Map In 2006 Netflix offered to pay a million dollar, popularly known as the Netflix Prize, to whoever could help Netflix improve their recommendation system by at least 10%. A year later Korbel team won the Progress Prize by improving Netflix’s recommendation system by 8.43%. They also gave the source code to Netflix […]
4 Big Data Myths – Part II
This is the second and the last part of this two-post series blog post on Big Data myths. If you haven’t read the first part, check it out here. Myth # 2: Big Data is an old wine in new bottle I hear people say, “Oh, that Big Data, we used to call it BI.” One […]
Hubris and the Data Scientist
ReadWriteWeb‘s Joe Brockmeier captures a recurring issue from last week’s O’Reilly Strata conference, asking “Can Big Data replace domain expertise?” According to Brockmeier, the audience (of data scientists) apparently narrowly agreed that their arsenal of tools and algorithms trumped the knowledge and experience of the meteorologists, financiers, and retailers to whose domains data scientists are increasingly […]
