Here is our selection of featured articles and resources posted recently:

Resources

• Solving Image Processing Problems with Python - Part 1
• Getting Started with NLP: Simple Topic Modeling in…

Summary: There are some interesting use cases where combining CNNs and RNN/LSTMs seems to make sense and a number of researchers pursuing this.  However, the latest trends in CNNs may make this obsolete.

There are things that just don’t seem to go together.  Take oil and water for instance.  Both valuable, but try putting them together? 

That was my reaction when I first came across the idea of combining CNNs (convolutional neural…

Here is our selection of featured articles posted in the last few days:

• Comparison of the Most Useful Text Processing APIs 
• A Comparative Analysis of Top 6 BI and Data Visualization Tools …

Andrew Ng is a great fan of reading research papers as a long term investment in your own study (On Life, Creativity, And Failure about Andrew Ng). Anyone who has worked in our field (AI, Machine Learning) can attest to that. AI is a complex and a rapidly evolving field. It’s a challenge to stay up to date with the latest technical details.

Based on my experience, in this post, I discuss how you can stay up to date by learning from the community. From a personal perspective, I work in…

Summary:  Our recent series of articles on AI strategies shows the options available for the strategic direction of your AI-first company.  Here are some thoughts on moving from strategy to implementation, including some useful tools to help in planning.

Hope you’ve been following our latest series of articles describing and…

What do experienced data scientists know that beginner data scientists don't know? Here is a quick overview.

1. Automating tasks. Writing code that writes code.
2. Outsourcing tasks to junior members or to consultants.
3. Managing people, hiring the right people, managing managers who report to you.
4. Training colleagues who might not be tech-savvy. Be an adviser for senior managers.
5. Identifying the right tools and assessing the benefits and minuses of…

Guest blog by Seth Dobrin and Daniel Hernandez.

Companies have been sold on the alchemy of data science. They have been promised transformative results. They modeled their expectations after their favorite digital-born companies. They have piled a ton of money into hiring expensive data scientists and ML engineers. They invested heavily in software…

Insights and Advice from Data Science Leaders and Key Influencers. Paperback – July 13, 2018. By Matt Corey.

The Data Scientist’s Book of Quotes includes over 300 insightful and inspiring quotes from the world’s leading Data Science thought leaders and key influencers across the world, including Andrew Ng, Bernard Marr, Vincent Granville, Carla Gentry, Cathy O’Neil and Hilary Mason. The Data Scientist role is one of the most pivotal and disruptive roles in today’s global…

In 2018 Fast Company declared the Data Scientist the best job for the third year in a row, which I wholeheartedly agree with (besides the Director of Fun at the York National Railway Museum), however the role of data scientist, as we know it, will soon have the same fate as the bowling pinsetters, chariot racers, and human alarm clocks.

In 2000-2010 data science was dominated by masters of herculean subjects, with PhDs in linear…

Here is our selection of featured articles posted in the last few days:

Featured Resources and Technical Contributions

• Data Science and Machine Learning: Great List of Resources 
• Free eBooks on Data Visualization…

Here is our selection of featured articles and resources posted in the last few days:

Featured Resources and Technical Contributions

• Comparison of top data science libraries for Python, R and Scala - [Infographic] 
• IBM Community Day: Data…

We discuss a new approach for selecting features from a large set of features, in an unsupervised machine learning framework. In supervised learning such as linear regression or supervised clustering, it is possible to test the predicting power of a set of features (also called independent variables by statisticians, or predictors) using metrics such as goodness of fit with the response (the dependent variable), for instance using the R-squared coefficient. This makes the process of feature…

Here are our selection of featured contributions posted in the last few days:

Featured Resources and Technical Contributions

• AutoEncoders and Parallel Computing with KernelML
• New Book: Mastering Machine Learning…

Python continues to take leading positions in solving data science tasks and challenges. Last year we made a blog post overviewing the Python’s libraries that proved to be the most helpful at that moment. This year, we expanded our list with new libraries and gave a fresh look to the ones we already talked about, focusing on the updates that have been made during the year.…

The impact of a change of scale, for instance using years instead of days as the unit of measurement for one variable in a clustering problem, can be dramatic. It can result in a totally different cluster structure. Frequently, this is not a desirable property, yet it is rarely mentioned in textbooks. I think all clustering software should state in their user guide, that the algorithm is sensitive to scale.

We illustrate the problem here, and propose a scale-invariant methodology for…

New article by Bill Vorhies.

Summary:  There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail.  It’s not as simple as it seems and here we try to take a more nuanced look.  The result is not as threatening as many headlines make it seem.…

Here is our selection of featured articles and resources posted in the last few days.

Featured Resources and Technical Contributions

• Free Book: Applied Stochastic Processes 
• A guide to manipulating, analyzing, and visualizing…

Many data scientists have a passion for mathematics, and many modern math problems can be explored using data science. Below is a selection of interesting articles, many about challenging, deep mathematical problems, by a data scientist who developed math-free algorithms. Some of these articles cover statistical theory and thus belong to…

Mathematical Olympiads are popular among high school students. However, there is nothing similar for college students, except maybe IMC. Even IMC is not popular. It focuses mostly on the same kind of problems as high school Olympiads, and you can not participate if you are over 23 years old. In addition, it is organized by country, as opposed to globally, thus favoring countries with a large population. Topics such as…

The list below is a (non-comprehensive) selection of what I believe should be taught first, in data science classes, based on 30 years of business experience. This is a follow up to my article Why logistic regression should be taught last.

I am not sure whether these topics below are even discussed in data camps or college…