Jan
2017 Feb Opinions, Interviews
http likes 30All (103) | Courses, Education (9) | Meetings (13) | News, Features (19) | Opinions, Interviews (27) | Software (5) | Tutorials, Overviews (24) | Webcasts & Webinars (6)
- The Top 5 KPIs to Consider When Measuring Your Campaign - Feb 28, 2017.
When it comes to measuring marketing campaign performance or analysing customers in any business, below top 5 Key Performance Indicators (KPIs) needs to be used to strategically drive the business.
- The Human Data Scientist: Safeguarding Your Career in the World of Automation - Feb 28, 2017.
"Data scientist" continues to be recognized as a top career, but does this mean unending spoils for the data scientist? With large scale mass automation on the horizon for numerous professions, what can we do to safeguard our positions?
- Machine 4.0: Making your Factory, Production and Maintenance Data Work - Feb 24, 2017.
To leverage the potential of Big Data the manufacturing firms should intelligently integrate and connect their data sources on a unified platform and use machine learning to extract insights, analyze them, and derive results.
- Machine Learning-driven Firewall - Feb 23, 2017.
Cyber Security is always a hot topic in IT industry and machine learning is making security systems more stronger. Here, a particular use case of machine learning in cyber security is explained in detail.
- The Origins of Big Data - Feb 21, 2017.
Big Data has truly come of age in 2013 when OED introduced the term “Big Data” for the first time. But when was the term Big Data first used and Why? Here are the results of our investigation.
- Causation or Correlation: Explaining Hill Criteria using xkcd - Feb 20, 2017.
This is an attempt to explain Hill’s criteria using xkcd comics, both because it seemed fun, and also to motivate causal inference instructures to have some variety in which xkcd comic they include in lectures.
- Creativity is Crucial in Data Science - Feb 20, 2017.
Creativity and Innovation are integral to Data Science and going forward in the world of AI, those are the things that will give edge to the humans over the machines.
- Data for Democracy: The First Two Months of D4D - Feb 20, 2017.
Let’s hear about how Data Science is used for democracy and well being of human societies by Data for Democracy organisation.
- Deep Learning, Artificial Intuition and the Quest for AGI - Feb 20, 2017.
Deep Learning systems exhibit behavior that appears biological despite not being based on biological material. It so happens that humanity has luckily stumbled upon Artificial Intuition in the form of Deep Learning.
- Reducing Science-related Stress - Feb 17, 2017.
The author presents a list of things learned through hard experience to help him with his own imposter syndrome, and help him to feel less stressed out about science.
- Why Go Long on Artificial Intelligence? - Feb 17, 2017.
We are now at the right place and time for AI to be the set of technology advancements that can help us solve challenges where answers reside in data. While we have already seen a few AI bull and bear markets since the 50’s, this time it’s different. If I and others are right, the implications are immensely valuable for all.
- Surprising Popularity: A Solution to the Crowd Wisdom Problem - Feb 15, 2017.
This is an overview of a recent proposed method for solving the crowd wisdom problem: select the answer that is more popular than people predict. Research shows that this principle yields the best answer under reasonable assumptions about voter behavior.
- Career Advice for Analytics & Data Science Professionals - Feb 13, 2017.
In our experience working with many quantitative professionals over the years, the two main areas that contribute to long-term career growth are networking and continuous learning. Here is specific advice on how to do this and tips for Continuous Learning.
- KDnuggets Top Blogger: An Interview with Brandon Rohrer, Top Data Scientist - Feb 13, 2017.
Read an interview with Top KDnuggets Blogger Brandon Rohrer, and get his thoughts on data science, newcomers to the field, and his ambitious pet project.
- Getting Real World Results From Agile Data Science Teams - Feb 10, 2017.
In this post, I’ll look at the practical ingredients of managing agile data science. By using agile data science methods, we help data teams do fast and directed work, and manage the inherent uncertainty of data science and application development.
- AI is not at all like Mobile/Cloud/SaaS - Feb 10, 2017.
AI is a hard problem and will take much longer to solve in any scope. The sudden uptick in interest may revert back to normal, but the cycle of work will be longer, much more diverse, and interesting than Mobile/Cloud/SaaS.
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So What is Big Data? - Feb 9, 2017.
We examine what experts say about Big Data – is it like teenage sex? Is it more than just a large and complex collection of data? And how many Vs are there? - What Americans Really Think About Trump’s Immigration Ban and Why - Feb 9, 2017.
What do Americans really think of the President's immigration ban? Text analysis of what people say in their own words reveals more than multiple-choice surveys.
- Overcoming the Last Hurdle in the Quest for the “Holy Grail” of Marketing - Feb 8, 2017.
A consumer’s complete digital footprint is a messy, fuzzy, dynamic picture. But data science is helping make digital identity as stable as physical identity – the last hurdle in the quest for the "holy grail" of marketing.
- Making Python Speak SQL with pandasql - Feb 8, 2017.
Want to wrangle Pandas data like you would SQL using Python? This post serves as an introduction to pandasql, and details how to get it up and running inside of Rodeo.
- Turbo Charge Agile Processes with Deep Learning - Feb 7, 2017.
The key to leveraging Deep Learning, or more broadly AI, in the workplace is to understand where it fits within an agile development environment.
- 5 Decisive Technology Trends which will Make or Break the Manufacturing Momentum in 2017 - Feb 7, 2017.
Manufacturing contributes to 16% of the global GDP and the Internet of Things (IoT ) is on track to connect >28 billion things. What happens when these massive forces collide? We review 5 game-changing technology catalysts.
- How to get your first job in Data Science? - Feb 6, 2017.
We provide guidelines for the most important questions, including the key data scientist skills and tools, how to get them, how to learn and practice, and where to send your application.
- 3 practical thoughts on why deep learning performs so well - Feb 3, 2017.
Why does Deep Learning perform better than other machine learning methods? We offer 3 reasons: integration of integration of feature extraction within the training process, collection of very large data sets, and technology development.
- An ode to the analytics grease monkeys - Feb 2, 2017.
Analytics is not one time job. It needs to be automated, deployed and improved for future business analytics requirements. Here an IBM expert discusses about development & deployment of analytics assets and capabilities of it.
- Fixing Deployment and Iteration Problems in CRISP-DM - Feb 1, 2017.
Many analytic models are not deployed effectively into production while others are not maintained or updated. Applying decision modeling and decision management technology within CRISP-DM addresses this.
- Is Deep Learning the Silver Bullet? - Feb 1, 2017.
With nearly every every smart young computer scientist planning to work on deep learning, are there really still artificial intelligence researchers working on other techniques? Is deep learning the AI silver bullet?
Five types of thinking for a high performing data scientist