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Archive | Machine Learning

Figure 2. Typical work activities vary across different data roles.

Who Does the Machine Learning and Data Science Work?

A survey of over 19,000 data professionals showed that nearly 2/3rds of respondents said they analyze data to influence product/business decisions. Only 1/4 of respondents said they do research to advance the state of the art of machine learning. Different data roles have different work activity profiles with Data Scientists engaging in more different work […]

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Figure 1. Machine Learning Algorithms Used in 2019. Click image to enlarge.

Top Machine Learning Algorithms, Frameworks, Tools and Products Used by Data Scientists

A recent survey by Kaggle revealed that data professionals used a variety of different algorithms, tools, frameworks and products to extract insights. Top algorithms were linear/logistic regression, decision trees/random forests and Gradient Boosting Machines. Top frameworks were Scikit-learn and TensorFlow. Top tools for automation were related to model selection and data augmentation. While half of […]

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Customer Experience and Emerging Technologies: My CXChat Summary on Artificial Intelligence, Machine Learning and the Customer

Below is a list of topics, answers and articles in support of a recent Tweet Chat in which I was the guest. The chat (#CXChat) was on customer experience and emerging technologies. You can read the official summary of this CXChat by Sue Duris here.Ā  I was invited as a guest in a weekly tweet […]

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Using IBM Watson to Answer Two Important Questions about your Customers

Customer experience management (CXM) programs are necessarily a quantitative endeavor, requiring CX professionals to decipher insights from a sea of customer data. In this post, I will illustrate how you can use IBM Watson Studio to analyze one source of customer data, customer survey responses, to answer two important questions about the health of your […]

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Figure 1. Programming Languages used in 2018. Click image to enlarge.

Programming Languages Most Used and Recommended by Data Scientists

The practice of data science requires the use of analytics tools, technologies and programming languages to help data professionals extract insights and value from data. A recent survey of nearly 24,000 data professionals by Kaggle revealed that Python, SQL and R are the most popular programming languages. The most popular, by far, was Python (83% […]

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Top BOB Blog Posts of 2018: Data Science, Machine Learning and the Net Promoter Score

The importance of data science and machine learning continues to grow in business and beyond. I did my part this year to spread interest in data science to more people. All of my top blog posts of 2018 (most reads) are all related to data science, with posts that address the practice of data science, […]

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Customer Genome Project

How Data Integration and Machine Learning Improve Retention Marketing

Retention marketing is about preventing your valuable customers from churning. Reducing customer churn requires you to know two things: 1) which customers are about to churn and 2) which remedies will keep them from churning. In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos […]

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