2019 Apr Opinions
All (109) | Courses, Education (11) | Meetings (15) | News (13) | Opinions (30) | Top Stories, Tweets (10) | Tutorials, Overviews (27) | Webcasts & Webinars (3)
- Interview Questions for Data Science – Three Case Interview Examples - Apr 30, 2019.
Part two in this series of useful posts for aspiring data scientists focuses on case interviews and how you can best go about answering them.
- Strata SF day 1 Highlights: from Edge to AI, scoring AI projects, cyberconflict, cryptography - Apr 29, 2019.
Journey from “Edge to AI”, scoring your AI projects, cyberconflict, role of cryptography in AI and more insights from a leading conference.
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Top Data Science and Machine Learning Methods Used in 2018, 2019 - Apr 29, 2019.
Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline. -
The most desired skill in data science - Apr 26, 2019.
What is the biggest skill gap in data science according to hiring managers looking for hire recent graduates? Hint: it’s not coding. - Projects to Include in a Data Science Portfolio - Apr 26, 2019.
“Don’t pick just random projects to work on and add it to your resume or portfolio. Solve a problem that relates to the companies that you’re interested in.”
- The problem with data science job postings - Apr 25, 2019.
We provide a useful set of rules you can follow to make sure you’re applying to the right roles and explain why confusing job descriptions with impossible requirements are the new normal.
- Machine Learning and Deep Link Graph Analytics: A Powerful Combination - Apr 23, 2019.
We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.
- Was it Worth Studying a Data Science Masters? - Apr 23, 2019.
As I started to apply for Data Science roles it quickly became apparent that I was lacking two key skills: applying Machine Learning and coding
- AI Supporting The Earth - Apr 22, 2019.
To celebrate Earth Day 2019, we explain how Intel is committed to advancing uses of AI that positively impact our world by providing social good organizations with technologies and expertise to accelerate their work.
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How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides - Apr 22, 2019.
To learn ALL the skills sets in data science is next to impossible as the scope is way too wide. There’ll always be some skills (technical/non-technical) that data scientists don’t know or haven’t learned as different businesses require different skill sets. - The Rise of Generative Adversarial Networks - Apr 19, 2019.
A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.
- 3 Big Problems with Big Data and How to Solve Them - Apr 18, 2019.
We discuss some of the negatives of using big data, including false equivalences and bias, vulnerability to security breaches, protecting against unauthorized access and the lack of international standards for data privacy regulations.
- Distributed Artificial Intelligence: A primer on Multi-Agent Systems, Agent-Based Modeling, and Swarm Intelligence - Apr 18, 2019.
Distributed Artificial Intelligence (DAI) is a class of technologies and methods that span from swarm intelligence to multi-agent technologies. It is one of the subsets of AI where simulation has greater importance that point-prediction.
- How can quantum computing be useful for Machine Learning - Apr 12, 2019.
We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.
- Make Your Own Job in Data Science: A High-Risk, High-Reward Approach - Apr 12, 2019.
This article discusses an alternative approach to finding data science jobs that’s also worth considering, although it has some inherent risks: make your own.
- Why Data Scientists Need To Work In Groups - Apr 12, 2019.
If you read this article you will see that the job of data scientist is NOT listed. The rest of this article will explore why it is true that data scientists need to work in groups.
- How to build a technology narrative for early career data and analytics talent acquisition - Apr 11, 2019.
We provide advice for companies in industries still going through a digital transformation on how they can start to understand the problem that Data and Analytics professionals can help solve.
- Beyond Siri, Google Assistant, and Alexa – what you need to know about AI Conversational Applications - Apr 10, 2019.
We discuss industry trends in Artificial Intelligence with Vijay Ramakrishnan, a machine learning engineer and expert in conversational applications.
- Compilation of Advice for New and Aspiring Data Scientists - Apr 10, 2019.
Check out this compilation of advice for the new and upcoming data scientist, condensing 30+ pieces of advice into 6 minutes.
- Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? - Apr 9, 2019.
Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.
- How to Recognize a Good Data Scientist Job From a Bad One - Apr 9, 2019.
Here are six characteristics which set good data scientist jobs apart form the bad ones.
- Advice for New Data Scientists - Apr 8, 2019.
We provide advice for junior data scientists as they begin their career, with tips and commentary from a tech lead at Airbnb.
- What is missing when AI makes a decision? - Apr 5, 2019.
We explain the need for caution when it comes to using AI in real-life situations and outline the importance of asking the right question to deliver the right impact.
- Spatio-Temporal Statistics: A Primer - Apr 5, 2019.
Marketing scientist Kevin Gray asks University of Missouri Professor Chris Wikle about Spatio-Temporal Statistics and how it can be used in science and business.
- Training a Champion: Building Deep Neural Nets for Big Data Analytics - Apr 4, 2019.
Introducing Sisense Hunch, the new way of handling Big Data sets that uses AQP technology to construct Deep Neural Networks (DNNs) which are trained to learn the relationships between queries and their results in these huge datasets.
- How to DIY Your Data Science Education - Apr 3, 2019.
Some people find the path of formal education works well for them, but this may not work for everyone, in every situation. Here are eight ways that you can take a DIY approach to your data science education.
- 7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them - Apr 2, 2019.
Without the right visualization tools, raw data is of little use. Data visualization helps present the data in an interactive visual format. Here are the qualities to look for in a data visualization tool.
- Top 10 Coding Mistakes Made by Data Scientists - Apr 2, 2019.
Here is a list of 10 common mistakes that a senior data scientist — who is ranked in the top 1% on Stackoverflow for python coding and who works with a lot of (junior) data scientists — frequently sees.
- XAI – A Data Scientist’s Mouthpiece - Apr 1, 2019.
We outline the usefulness of Explainable AI, which allows you to explain the results of a multidimensional model - including having a multimodal decision boundary - to a business user.
- What Does GPT-2 Think About the AI Arms Race? - Apr 1, 2019.
It may be April first, but that doesn't mean you will necessarily be fooled by GPT-2's views on the AI arms race. Why not have a read for fun and to see what the language generation model is capable of.
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