Topics: Opinions, Interviews, Reports
We welcome guest blogs and opinions - see submission guidelines.
Latest:
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Research into 1,001 Data Scientist LinkedIn Profiles, the latest - Mar 31, 2020.
What makes a data scientist today? Consider this review of data collected from three years worth of data scientist LinkedIn profiles to gain insight into how this important new career path is shaping up. -
Coronavirus Trends – what can we learn - Mar 31, 2020.
We examine the coronavirus trends, and look at death rates from Covid-19, including absolute numbers, adjusted for population, and daily change rates. The daily change rates are declining for almost all countries, including Italy and Spain, but remaining alarmingly high for US and especially New York State. -
KDnuggets Topics: bringing together the latest and the most popular - Mar 30, 2020.
To help our readers better navigate rich KDnuggets content, we introduce topic pages for most popular topics. Each topic page brings together most recent posts on that topic as well as most popular (badge-winning) previous posts. -
Advice for a Successful Data Science Career - Mar 30, 2020.
This blog is meant to show that most everyone has had to expend quite a bit of effort to get where they are. They have to work hard, sometimes experience failure, show discipline, be persistent, be dedicated to their goals, and sometimes sacrifice or take risks. -
Predicting the President: Two Ways Election Forecasts Are Misunderstood - Mar 27, 2020.
With election cycles always seeming to be in season, predictions on outcomes remain intriguing content for the voting citizens. Misinterpretation of election forecasts also runs rampant, and can impact perceptions of candidates and those who post these predictions. A better fundamental understanding of probability can help improve our collective notion of futurism, and how we monitor elections. -
Deep Learning Breakthrough: a sub-linear deep learning algorithm that does not need a GPU? - Mar 26, 2020.
Deep Learning sits at the forefront of many important advances underway in machine learning. With backpropagation being a primary training method, its computational inefficiencies require sophisticated hardware, such as GPUs. Learn about this recent breakthrough algorithmic advancement with improvements to the backpropgation calculations on a CPU that outperforms large neural network training with a GPU. -
Making sense of ensemble learning techniques - Mar 26, 2020.
This article breaks down ensemble learning and how it can be used for problem solving. -
Alternative Data, Text Analytics, and Sentiment Analysis in Trading and Investing - Mar 25, 2020.
Different types of data beyond your typical dollars and cents have been used in the finance industry for many years. By leveraging machine learning, sentiment data is expected to play an increasingly dominant role in the investment industry, and this article highlights some special challenges of its use in trading models. -
Want to Build an AI Model for Your Business? Read this - Mar 25, 2020.
The best approach for AI production is similar to what venture capitalists (VC’s) do when they evaluate and invest in startups. -
Why BERT Fails in Commercial Environments - Mar 24, 2020.
The deployment of large transformer-based models in dynamic commercial environments often yields poor results. This is because commercial environments are usually dynamic, and contain continuous domain shifts between inference and training data. -
Coronavirus Data and Poll Analysis – yes, there is hope, if we act now - Mar 23, 2020.
We examine the growth of coronavirus daily cases in most affected countries, and show evidence that social distancing works in reducing the rate of spread. We also analyze KDnuggets Poll results - the scale of change to online and how Data Science work is likely to increase or drop in different regions. Stay Healthy and practice social distancing! -
Nine lessons learned during my first year as a Data Scientist - Mar 20, 2020.
What is it like to be a Data Scientist? There can be many hats to wear, and so many problems to solve that are fed with data, churned by data science, and guided by business results. Find out about lessons learned from one Data Scientist about how best to work and perform in the role. -
What is the most effective policy response to the new coronavirus pandemic? - Mar 19, 2020.
Where Test/Trace/Quarantine are working, the number of cases/day have declined empirically. Furthermore, this appears to be a radically superior strategy where it can be deployed. I’ll review the evidence, discuss the other strategies and their consequences, and then discuss what can be done. -
Five Interesting Data Engineering Projects - Mar 17, 2020.
As the role of the data engineer continues to grow in the field of data science, so are the many tools being developed to support wrangling all that data. Five of these tools are reviewed here (along with a few bonus tools) that you should pay attention to for your data pipeline work. -
Scaling Your Data Strategy - Mar 17, 2020.
This article presents a particular vision for a cohesive data strategy for addressing large-scale problems with data-driven solutions, based on prior professional experiences. -
Forecasting Stories: Is it seasonality or not? - Mar 17, 2020.
Kicking off with a series of forecasting stories, starting with seasonality and its business applications. This first article speaks of course corrections that were based on weather and calendar driven seasonality. -
When Will AutoML replace Data Scientists? Poll Results and Analysis - Mar 16, 2020.
Will AI always be 5-10 years away? The majority of respondents to this poll think that AutoML will reach expert level in 5-10 years. Interestingly, it is about the same as 5 years ago. We examine the trends by AutoML experience, industry, and region. -
Skynet Is Real: The History and Future of Factories With No Workers - Mar 16, 2020.
Let’s see whether robots will become "grave diggers" of the proletariat, what do we lack to get total automation, and what compromises exist. -
Building a Mature Machine Learning Team - Mar 13, 2020.
After spending a lot of time thinking about the paths that software companies take toward ML maturity, this framework was created to follow as you adopt ML and then mature as an organization. The framework covers every aspect of building a team including product, process, technical, and organizational readiness, as well as recognizes the importance of cross-functional expertise and process improvements for bringing AI-driven products to market. -
The Most Useful Machine Learning Tools of 2020 - Mar 13, 2020.
This articles outlines 5 sets of tools every lazy full-stack data scientist should use. -
Software Interfaces for Machine Learning Deployment - Mar 11, 2020.
While building a machine learning model might be the fun part, it won't do much for anyone else unless it can be deployed into a production environment. How to implement machine learning deployments is a special challenge with differences from traditional software engineering, and this post examines a fundamental first step -- how to create software interfaces so you can develop deployments that are automated and repeatable. -
Covid-19, your community, and you — a data science perspective - Mar 11, 2020.
Let's talk about covid-19; the reality, the numbers, and the data science. -
New Poll: Coronavirus impact on AI/Data Science/Machine Learning community - Mar 10, 2020.
Has coronavirus impacted your conference or other travel plans, and do you anticipate it causing further professional or educational disruption in the near future? Take part in the new KDnuggets poll and have your say. -
Resources for Women in AI, Data Science, and Machine Learning - Mar 8, 2020.
For the international women's day, we feature resources to help more women enter and succeed in AI, Big Data, Data Science, and Machine Learning fields. -
How Bad Data is Affecting Your Organization’s Operational Efficiency - Mar 5, 2020.
Despite recognizing the importance of data quality, many companies still fail to implement a data quality framework that could protect them from making costly mistakes. Poor data does not just cause revenue loss – it’s the reason your company could lose employees, customers and reputation! -
A simple and interpretable performance measure for a binary classifier - Mar 4, 2020.
Binary classification tasks are the bread and butter of machine learning. However, the standard statistic for its performance is a mathematical tool that is difficult to interpret -- the ROC-AUC. Here, a performance measure is introduced that simply considers the probability of making a correct binary classification. -
How do we Better Solve Analytics Problems? - Mar 4, 2020.
Problem definition and solution development are key ingredients of being a consultant. Structuring the problem definition phase is critical to project success but may seem like a creative process. -
Image Recognition For Building Your Perfect Store - Mar 3, 2020.
In this blog, we outline what a perfect store strategy is, and how to achieve it. -
20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2) - Mar 2, 2020.
We explain important AI, ML, Data Science terms you should know in 2020, including Double Descent, Ethics in AI, Explainability (Explainable AI), Full Stack Data Science, Geospatial, GPT-2, NLG (Natural Language Generation), PyTorch, Reinforcement Learning, and Transformer Architecture.
March:
- Can Edge Analytics Become a Game Changer?
- New Poll: When Will AutoML Replace Data Scientists (if ever)?
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Learning from 3 big Data Science career mistakes
Thinking of data science as merely a technical profession, like programming, may take you away from your goals. We explain big mistakes to avoid, including not understanding the 2 cultures of statistics, and not understanding the shift to industrial focus. -
Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms
The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.
- 7 Data Trends for 2020 (and one non-trend)
- Prepare for a Long Battle against Deepfakes
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The Death of Data Scientists – will AutoML replace them?
Soon after tech giants Google and Microsoft introduced their AutoML services to the world, the popularity and interest in these services skyrocketed. We first review AutoML, compare the platforms available, and then test them out against real data scientists to answer the question: will AutoML replace us? - In Loving Memory of Strictly-Typed Schemas
- Hand labeling is the past. The future is #NoLabel AI
- Scaling the Wall Between Data Scientist and Data Engineer
- What Does it Mean to Deploy a Machine Learning Model?
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Why Did I Reject a Data Scientist Job?
Snagging that job as a Data Scientist might not be exactly what you were expecting. Consider this advice on carefully considering job titles with what the position might really be like day-to-day.
- AI and Machine Learning In Our Every Day Life
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The Data Science Puzzle — 2020 Edition
The data science puzzle is once again re-examined through the relationship between several key concepts of the landscape, incorporating updates and observations since last time. Check out the results here. -
The Future of Machine Learning Will Include a Lot Less Engineering
Despite getting less attention, the systems-level design and engineering challenges in ML are still very important — creating something useful requires more than building good models, it requires building good systems.
- Top 5 Data Science Trends for 2020
- Why are Machine Learning Projects so Hard to Manage?
