Opinions, Interviews, Reports
We welcome guest blogs and opinions - see submission guidelines.
Latest:
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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. -
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.
March:
- Explainable AI or Halting Faulty Models ahead of Disaster
- The Four Levels of Analytics Maturity
- Data Science for Decision Makers: A Discussion with Dr Stelios Kampakis
- The AI Black Box Explanation Problem
- The Problem With Self-Serve Analytics
- How to Capture Data to Make Business Impact
- Top 8 Data Science Use Cases in Manufacturing
- My Best Tips for Agile Data Science Research
- Overcoming distrust on the path to productive analytics
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How To Work In Data Science, AI, Big Data
There are many facets to working in Data Science. Your role will depend greatly on the industry you pick and the area of Data Science you want to pursue. A Data Science career is very dynamic and requires a team effort to succeed. -
My favorite mind-blowing Machine Learning/AI breakthroughs
We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.
- Cartoon: AI and March Madness
- AI: Arms Race 2.0
- The 7 Myths of Data Anonymisation
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The Pareto Principle for Data Scientists
In this article, I’ll share a few ways in which we, as data scientists, can use the power of the Pareto Principle to guide our day-to-day activities.
- Beating the Bookies with Machine Learning
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19 Inspiring Women in AI, Big Data, Data Science, Machine Learning
For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.
- Designing Ethical Algorithms
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis
- Breaking neural networks with adversarial attacks
- Beyond news contents: the role of social context for fake news detection
- 3 Reasons Why AutoML Won’t Replace Data Scientists Yet
- Neural Networks seem to follow a puzzlingly simple strategy to classify images
- The Difference Between Data Scientists and Data Engineers
- On Building Effective Data Science Teams
- OpenAI’s GPT-2: the model, the hype, and the controversy
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What no one will tell you about data science job applications
For every person who has a question relating to a data science job application, and asks it, there are ten people who have the same question, but don’t ask it. If you’re one of those ten, then this post is for you.
- Most impactful AI trends of 2018: The rise of ML Engineering