2016 Oct Opinions, Interviews
All (98) | Courses, Education (7) | Meetings (11) | News, Features (20) | Opinions, Interviews (35) | Software (5) | Tutorials, Overviews (17) | Webcasts & Webinars (3)
- Education’s Response to the Big Data Skills Demand - Oct 31, 2016.
What are universities and colleges doing to make Big Data skills easier to obtain, and how are they speeding up the educational process to get these people into the workforce faster?
- KDnuggets Top Blogger: An Interview with Adit Deshpande, Deep Learning Aficionado - Oct 31, 2016.
Read an interview with KDnuggets Top Blogger Adit Deshpande, a deep learning aficionado and masterful blogger, who also just happens to be a second year undergraduate student.
- Is Your Code Good Enough to Call Yourself a Data Scientist? - Oct 28, 2016.
Is your code good enough to be calling yourself a Data Scientist? Figure out how to determine the answer to this question... and gain some suggestions on ensuring that the answer is "yes!"
- Using Machine Learning to Detect Malicious URLs - Oct 28, 2016.
This is a write-up of an experiment employing a machine learning model to identify malicious URLs. The author provides a link to the code for you to try yourself.
- Big Data Science: Expectation vs. Reality - Oct 27, 2016.
The path to success and happiness of the data science team working with big data project is not always clear from the beginning. It depends on maturity of underlying platform, their cross skills and devops process around their day-to-day operations.
- Mind of a Data Scientist – Part 2 - Oct 26, 2016.
First part of this series was about formulation of the business problem and engineering the data points. This is the last part of the series and it tells us about exploratory data analysis and feature engineering.
- When Will We Bow To Our Machine Overlords? - Oct 25, 2016.
"I, for one, welcome our new computer overlords." Far from being our overlords, machines are our societal companion, our partner that supports our success and supports the functioning of our society. But the support the machine gives us is rudimentary at best.
- Mind of a Data Scientist – Part 1 - Oct 25, 2016.
By now, many people are aware of which technical skills are required for a Data Scientist, but do you know what mindset or thinking is required to be a good data scientist? Let’s read this two parts series by an industry expert.
- Inside Industry 4.0: What’s Driving The Fourth Industrial Revolution? - Oct 24, 2016.
In the history of mankind and past three major industrial revolutions, horizontal innovations like wheel, steam engine, electricity and integrated chips have always been the crux of it and they changed the world dramatically. Well, fourth one is on its way! Want to know what’s driving it? Have a read at this crisp article.
- Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part 1 - Oct 24, 2016.
CDOs are the new hot role to rock. Read about the CDO Toolkit, which integrates the disciplines of economics and analytics to help the CDO to ascertain the economic value of the organization’s data and data sources.
- KDnuggets Top Blogger: An Interview with Bill Schmarzo, the Dean of Big Data - Oct 24, 2016.
Read an interview with the Dean of Big Data Bill Schmarzo, one of KDnuggets' Top Bloggers for September, and gain some insight on the topics data science, IoT, Big Data... and jeans!
- Valuable Data Products: Answers to Career Questions and More - Oct 21, 2016.
Collecting high quality data from various resources and turning it into data products is one of the ways to monetize data in today’s digital economy. Lets take a deeper look into it.
- KDnuggets Top Blogger: An Interview with Ajit Jaokar, on IoT and Data Science - Oct 21, 2016.
Ajit Jaokar, a leading expert in the field, shares his views on evolution of IoT, Data Science, Smart Cities, the promise and dangers of AI, and encouraging young people.
- Top LinkedIn Groups for Analytics, Big Data, Data Mining, and Data Science in 2016 - Oct 20, 2016.
Big Data and Analytics became the largest group. Overall engagement rates decline, but liking a post is 6.5 times more common than commenting. Machine Learning & Data Science, KDnuggets, and Data Scientists have the highest engagement levels.
- Evolution of the Data Scientist Through the Decade: What’s Changed - Oct 20, 2016.
Evolution is the truth of mankind and it’s inevitable. We all are evolutionizing everyday biologically as well as technologically and so do our roles and responsibilities. Here is the summary of evolution of Data Scientist role and it’s hiring trends in industry throughout the decade.
- Top Data Scientist Daniel Tunkelang on Data Science Project Scope… and Reducing It - Oct 19, 2016.
Respected Data Scientist Daniel Tunkelang shares some insight into problems lying at the crossroads of software engineering and data science, and prescribes one major solution: reduce scope!
- European Machine Intelligence Landscape - Oct 18, 2016.
This post outlines the European machine intelligence landscape, which, until recently, has been under-appreciated in its contribution to the innovation and commercialisation of machine intelligence technologies.
- LinkedIn Knowledge Graph – KDnuggets Interview - Oct 18, 2016.
We interview LinkedIn about their recently published LinkedIn Knowledge Graph which connects their many millions of members, jobs, companies, and more.
- Rexer Analytics Data Science Survey Highlights - Oct 14, 2016.
Regression, Decision Trees, and Cluster analysis remain the most commonly used algorithms in the field, R continues to ascend, job satisfaction remains high, but customer understanding still needs improvement.
- Strata Hadoop 2016: Fast Data and Robots - Oct 14, 2016.
Did you miss Strata Hadoop World conference this year?? No worries! Want to know “how exciting it was”? Lets hear it from an expert in her own words.
- Data Preparation Tips, Tricks, and Tools: An Interview with the Insiders - Oct 14, 2016.
Data preparation and preprocessing tasks constitute a high percentage of any data-centric operation. In order to provide some insight, we have asked a pair of experts to answer a few questions on the subject.
- EDISON Data Science Framework to define the Data Science Profession - Oct 14, 2016.
EDISON Data Science Framework provides conceptual, instructional and policy components required to establish the Data Science profession.
- How to Get Stuff Done at a Data Startup - Oct 13, 2016.
This post is a followup to how to structure data science teams, with a focus on how we get stuff done. The same principles we follow can be applied at your data startup or data science team.
- Top 12 Interesting Careers to Explore in Big Data - Oct 12, 2016.
From data driven strategies to decision making, the true worth of Big Data has been realized, and has led to opening up of amazing career choices. Check out these 12 interesting careers to explore in Big Data.
- CAP Certification Program: What’s on the Exam? - Oct 12, 2016.
We look behind the curtain at the CAP Certification program designed to measure analytics professional’s knowledge across seven unique areas of the analytics process,
- Humans & Machines Ethics Framework: Assessing Machine Learning Influence - Oct 11, 2016.
Humans & Machines Ethics Canvas’ main goal is to be a guide for critical thinking throughout the ethical decision-making process. It acts as a value system and an ethics framework to assess the influence of machine learning and software development while developing a system for individuals, teams, and organisations.
- Here’s How IT Departments are Using Big Data - Oct 10, 2016.
The use cases for big data are clear when it comes to areas like marketing, healthcare, and retail, but IT’s use of big data is a little less clear. Recently, however, some IT departments are finding ways to use big data to improve their individual operations along with that of the entire organization.
- Do Multipliers Trump Big Data Analytics? - Oct 10, 2016.
Multipliers and Big Data analytics are tightly integrated. Multipliers feed into and improve the accuracy of our analytics, while analytics feed into and improve the accuracy of our multipliers. They should be used together at all levels of the organization.
- Still Searching for ROI in Big Data Analytics? You’re Not Alone! - Oct 7, 2016.
Are businesses getting the ROI they desire given the hype around big data analytics? With all the promises of big data analytics, why are more than half the companies still in the red with respect to analytics investments?
- The Coronation of Predictive Analytics: A Four-Year Retrospective - Oct 6, 2016.
The highlights from The Burtch Works Study: Salaries of Predictive Analytics Professionals 2016, which examines updated compensation and demographic data on over 1,200 analytics professionals across the US.
- Battle of the Data Science Venn Diagrams - Oct 6, 2016.
First came Drew Conway's data science Venn diagram. Then came all the rest. Read this comparative overview of data science Venn diagrams for both the insight into the profession and the humor that comes along for free.
- 9 Bizarre and Surprising Insights from Data Science - Oct 5, 2016.
The petabytes of information currently available to analysts amounts to a boundless playing field of possible truths.
- Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team - Oct 4, 2016.
This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.
- Hiring Foreign Data Scientists in the US – A Primer on Visas - Oct 3, 2016.
To alleviate the shortage of Data Scientists, many companies look to hire overseas and need to navigate the complex and expensive visa process. Here we compare two common visa categories for data scientists across 6 criteria employers care about (eligibility, legal fees, filing fees, quota, length of process, and chances of approval).
- How to Structure Your Team When Building a Data Startup - Oct 1, 2016.
Data Startup in mind? Need to structure different teams? Here are guidelines for structuring Data Team, Crawl Development Team, Data Infrastructure Team, and more.