2017 May Opinions, Interviews
All (112) | Courses, Education (11) | Meetings (16) | News, Features (20) | Opinions, Interviews (22) | Software (5) | Tutorials, Overviews (32) | Webcasts & Webinars (6)
- Challenges in Machine Learning for Trust - May 29, 2017.
With an explosive growth in the number of transactions, detecting fraud cannot be done manually and Machine Learning-based methods are required. We examine what are the main challenges for using Machine Learning for Trust.
- How A Data Scientist Can Improve Productivity - May 25, 2017.
Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.
- Will Data Science Eliminate Data Science? - May 25, 2017.
There are elements of what we do which are AI complete. Eventually, Artificial General Intelligence will eliminate the data scientist, but it’s not around the corner.
- Natural Language Generation overview – is NLG is worth a thousand pictures ? - May 23, 2017.
NLG tools automate the analysis and enhance traditional BI platforms by explaining in plain English the significance of visualizations and findings – here is an overview of the market.
- Why Java is the Language of Choice for the Internet of Things (IoT) - May 23, 2017.
What has caused this Java revival and why is Java so useful in the Internet of Things? Better yet, what is the Internet of Things?
- The Path To Learning Artificial Intelligence - May 19, 2017.
Learn how to easily build real-world AI for booming tech, business, pioneering careers and game-level fun.
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Getting Into Data Science: What You Need to Know - May 18, 2017.
Ready to embark on an exciting and in-demand career? Here’s what you need to know about what a data scientist does—and how you can become competitive in this in-demand field. - Simplifying Data Pipelines in Hadoop: Overcoming the Growing Pains - May 18, 2017.
Moving to Hadoop is not without its challenges—there are so many options, from tools to approaches, that can have a significant impact on the future success of a business’ strategy. Data management and data pipelining can be particularly difficult.
- Teaching the Data Science Process - May 17, 2017.
Understanding the process requires not only wide technical background in machine learning but also basic notions of businesses administration; here I will share my experience on teaching the data science process.
- Data science through the lens of research design - May 16, 2017.
Data science projects may often fail due to a lack of clear definition of the business goal and not because data scientists technical abilities. We examine the connection between data science and research design to help address this issue.
- Data Version Control: iterative machine learning - May 11, 2017.
ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.
- The Quant Crunch: The demand for data science skills - May 10, 2017.
This report, created by analyzing millions of job postings using advanced technology, divides Data Science and Analytics roles into 6 broad categories, and answers many questions, including cities, industries, job roles with most growth.
- A Data Analyst guide to A/B testing - May 9, 2017.
A/B testing is key to improving results in any marketing campaign. We examine the issues involved in its 3 main components: message variants, user group selection, and choosing the winning version.
- The Power of Data and Collaboration to Improve Traffic Safety - May 8, 2017.
Datakind, in collaboration with Microsoft, completed significant data-driven projects to improve traffic safety and help save lives in New York City, Seattle, and New Orleans.
- Data Science & Machine Learning Platforms for the Enterprise - May 8, 2017.
A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.
- Machine Learning overtaking Big Data? - May 4, 2017.
Is Machine Learning is overtaking Big Data?! We also examine trends for several more related and popular buzzwords, and see how BD, ML. Artificial Intelligence, Data Science, and Deep Learning rank.
- 42 Essential Quotes by Data Science Thought Leaders - May 4, 2017.
42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.
- How to Fail with Artificial Intelligence: 9 creative ways to make your AI startup fail - May 4, 2017.
This post summarizes nine creative ways to condemn almost any AI startup to bankruptcy. I focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.
- Did you know cavemen were already dealing with “Big Data” issues? - May 3, 2017.
We know Big Data & Analytics are new & cutting edge technologies; but actually, human started using data & analytics techniques 5000 years ago. Let’s take a look.
- Pros and Pitfalls of Observational Research - May 3, 2017.
Why the connection between beer brand and region? Climate? Tradition? Or simply distribution? Some combination of the three, plus other factors?
- Deep Learning – Past, Present, and Future - May 2, 2017.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
- What Do Frameworks Offer Data Scientists that Programming Languages Lack? - May 2, 2017.
While programming languages will never be completely obsolete, a growing number of programmers (and data scientists) prefer working with frameworks and view them as the more modern and cutting-edge option for a number of reasons.
Getting Into Data Science: What You Need to Know
Apr
