Feb
2017 Mar Opinions, Interviews
All (117) | Courses, Education (9) | Meetings (17) | News, Features (21) | Opinions, Interviews (27) | Software (4) | Tutorials, Overviews (35) | Webcasts & Webinars (4)
- Put Your Best Face Forward: The New Frontier of Communication - Mar 30, 2017.
Our events are people-focused, bringing brands, influencers, and talent into one space with one goal: to solve all the problems worth solving. We plan conferences that are fun and relaxed on the front end and organized and optimized on the back end.
- What makes a great data scientist? - Mar 30, 2017.
Here are 3 key traits that differentiate between a data scientist and a great data scientist, starting with – great data scientist is obsessed with solving problems, not new tools.
- Key Takeaways from Strata + Hadoop World 2017 San Jose, Day 2 - Mar 29, 2017.
The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.
- It’s Getting Hot In Here: Data Science vs Fake News - Mar 29, 2017.
While some opponents still hold the misconception that the 'science is not yet in' on the culprit, the scientific community has long reached a consensus to the drivers behind the increase in global temperatures.
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Standardization and Specialization in Analytics, Data Science, and BI - Mar 28, 2017.
We see beginnings of both standardization and specialization, with graduate analytics curriculum that covers math, statistics, CS, IT systems, and communications. We also see specializations in data science and BI, and verticals like marketing and healthcare analytics. - From Big Data Platforms to Platform-less Machine Learning - Mar 27, 2017.
The rise in serverless architectures along with marketplaces from cloud providers creates a significant momentum to democratize big data analytics. Machine learning or AI services are much more valuable, tangible and easier to understand for businesses than clumsy big data platforms.
- Key Takeaways from Strata + Hadoop World 2017 San Jose, Day 1 - Mar 24, 2017.
The focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand.
- How to think like a data scientist to become one - Mar 23, 2017.
The author went from securities analyst to Head of Data Science at Amazon. He describes what he learned in his journey and gives 4 useful rules based on his experience.
- 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.
- Why A/B Testers Have The Best Jobs In Tech - Mar 22, 2017.
Learning about what these people do made it clear that when you are deeply involved in A/B testing at scale, there is a tremendous rush from doing so many different things that matter.
- What Happened Last Night in Sweden: Data Science vs Fake News - Mar 22, 2017.
During a rally in February, President Trump had these disparaging words about Sweden’s humane immigration policy... but nothing of note actually happened the previous night in Sweden.
- Data Scientists Might Have It Made For 2017 - Mar 21, 2017.
Companies all over the world have placed a lot of value on getting more insights from big data analytics. That’s not without good reason.
- Climate Change Denial and CO2 Emissions – What is the Connection? - Mar 17, 2017.
We examine the connection between Climate Change Denial and CO2 emissions and find a strong correlation - countries with higher CO2 emissions/capita also have higher percentage of climate skeptics.
- Proxy Indicators: beware of spurious claims - Mar 16, 2017.
Beware of online and market research studies which can lead to false or spurious claims. We examine several notable examples including Google Street View and Argentina inflation.
- 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.
- Text Analytics: A Primer - Mar 14, 2017.
Marketing scientist Kevin Gray asks Professor Bing Liu to give us a quick snapshot of text analytics in this informative interview.
- Interview: UN/WDC “Data For Climate Action” Challenge – What Data Scientists Need to Know - Mar 13, 2017.
We ask UN Global Pulse Director about the 'Data For Climate Action' Challenge, the best sources of climate data, examples of using data for climate mitigation and climate adaptation, and resources for convincing climate change skeptics.
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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. - Google Got a Lot of Data About You - Mar 9, 2017.
This article will dive into six types of data that most big tech companies, and especially Google, gather about consumers.
- 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.
- Big Data Desperately Needs Transparency - Mar 6, 2017.
If Big Data is to realize its potential, people need to understand what it is capable of, what information is out there and where every piece of data comes from. Without such transparency and understanding, it will be difficult to persuade people to rely on the findings.
- Software Engineering vs Machine Learning Concepts - Mar 6, 2017.
Not all core concepts from software engineering translate into the machine learning universe. Here are some differences I've noticed.
- Gartner Data Science Platforms – A Deeper Look - Mar 3, 2017.
Thomas Dinsmore critical examination of Gartner 2017 MQ of Data Science Platforms, including vendors who out, in, have big changes, Hadoop and Spark integration, open source software, and what Data Scientists actually use.
- Greed, Fear, Game Theory and Deep Learning - Mar 3, 2017.
The most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. The core problem of a multi-agent approach is how to control its behavior.
- Predictions for Data Science in 2017 - Mar 2, 2017.
Our predictions include: 2017 will be the year of Deep Learning (DL) technology, Artificial General Intelligence is still far away, Software and Hardware Progress will accelerate, and AI will have unexpected socio-political implications.
- Hadoop Is Falling – Why? - Mar 1, 2017.
Three years ago, looking beyond Hadoop was insanity, and there was little else that could come close. Recently, adoption of Hadoop has slowed down considerably. We examine why.
- 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.
Standardization and Specialization in Analytics, Data Science, and BI
6 Business Concepts you need to become a Data Science Unicorn