Bob Hayes
CustomerThink
Seattle, WA, United States
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Past articles by Bob:
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 activities than other data… → Read More
Most Popular Integrated Development Environments (IDEs) Used by Data Scientists
Results of a worldwide survey of data professionals, the top used Integrated Development Environments (IDEs) are: Jupyter (73% have used), Visual Studio (31%), RStudio (30%), PyCharm (29%) and Notepad++ (22%). Integrated Development Environments (IDEs) helps programmers consolidate different aspects of software development. An IDE typically consists of: 1) source code editor, 2) build automation… → Read More
Usage of Programming Languages by Data Scientists: Python Grows while R Weakens
The practice of data science, including work in machine learning and artificial intelligence, requires the use of analytics tools, technologies and programming languages. A recent survey of nearly 20,000 data professionals by Kaggle revealed that Python, SQL and R continue to be the most popular programming languages. The most popular, by far, was Python (87% used). Additionally, 8 out of 10… → Read More
Top Cloud Computing Products and Services Used by Data Scientists
A recent survey revealed that 69% of data pros have used at least one cloud computing product in the last 5 years while 62% of data pros have used at least one cloud computing service in the last 5 years. The most popular cloud computing products include AWS Elastic Compute, Google Cloud Engine and AWS Lambda. The most popular cloud computing services include Amazon Web Services, Google Cloud… → Read More
Most Popular Machine Learning Frameworks and Products Used by Data Professionals
A recent survey revealed that 84% of data pros have used at least one ML framework in the last 5 years while 51% of data pros have used at least one ML product in the last 5 years. The most popular ML frameworks include Scikit-Learn, Tensorflow and Keras. The most popular ML products include SAS, Cloudera and Azure. Figure 1. Machine Learning Frameworks used in last 5 years. Click image to… → Read More
Usage-Driven Groupings of Data Science and Machine Learning Programming Languages
Analysis of usage patterns of 16 data science programming languages by over 18,000 data professionals showed that programming languages can be grouped into a smaller set (specifically, 5 groupings). That is, some programming languages tend to be used together apart from other programming languages. A few of the different groupings of languages reflect specific types of applications or specific… → Read More
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% used). Additionally, 3 out of 4 data… → Read More
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 and 2) employing machine learning predictive… → Read More
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 and 2) employing machine learning predictive… → Read More
Data Science and the Quest for Truth
I was interviewed by IBM to share my thoughts on a topic related to data science about which I’m passionate: How do we know what we know and, once we know it, how do we know it’s the truth? IBM turned that interview into a comic strip (see below) and I summarize my points in this post. You would think that, because we have access to so much information in this digital age, getting to the truth… → Read More
Customer Success Executives Need to Answer These Three 3 Critical Questions
In today’s subscription-based economy, customers are no longer trapped in long-term contracts; instead, they are able to jump to competitors easily when they become dissatisfied with their current vendor. Consequently, many subscription-based and SaaS companies are turning to the practice of Customer Success to retain their customers. Customer Success is the function in a company that manages… → Read More
4 Reasons Why Customer Retention Matters to Your Customer Acquisition Efforts
Business growth depends on acquiring new customers and keeping them around for a long time. Yet businesses are over 2x more likely to focus on acquisition efforts than they are retention efforts. In today’s post, I want to discuss why businesses need to increase their focus on customer retention efforts and why the are imperative to your customer acquisition efforts. Here are four reasons why… → Read More
4 Reasons Why Customer Retention Matters to Your Customer Acquisition Efforts
Business growth depends on acquiring new customers and keeping them around for a long time. Yet businesses are over 2x more likely to focus on acquisition efforts than they are retention efforts. In today’s post, I want to discuss why businesses need to increase their focus on customer retention efforts and why the are imperative to your customer acquisition efforts. Here are four reasons why… → Read More
Top 5 Paying Data Science and Machine Learning Jobs in the US
A recent survey by Kaggle revealed that the annual median compensation (salary + bonus) of data professionals in the US was $118K. US data professionals who self-identified as machine learning engineers, software developers and data scientists had the highest annual compensation at $128K, $120K and $120K, respectively. Data professionals who self-identified as data scientists reported the… → Read More
Top 5 Paying Data Science and Machine Learning Jobs in the US
A recent survey by Kaggle revealed that the annual median compensation (salary + bonus) of data professionals in the US was $118K. US data professionals who self-identified as machine learning engineers, software developers and data scientists had the highest annual compensation at $128K, $120K and $120K, respectively. Data professionals who self-identified as data scientists reported the… → Read More
Top 10 Challenges to Practicing Data Science at Work
A recent survey of over 16,000 data professionals showed that the most common challenges to data science included dirty data (36%), lack of data science talent (30%) and lack of management support (27%). Also, data professionals reported experiencing around three challenges in the previous year. A principal component analysis of the 20 challenges studied showed that challenges can be grouped… → Read More
Top 10 Platforms and Resources to Learn Data Science Skills
A recent survey of over 16,000 data professionals showed that the most used platforms/resources included Kaggle, Online courses and Stack Overflow Q&A. Additionally, the most useful platforms/resources included Personal Projects, Online courses and Stack Overflow Q&A. On average, data pros used around three (3) different platforms/resources to learn data science skills. There are many… → Read More
Machine Learning Engineers and Data Scientists Report Highest Job Satisfaction Among Data Professionals
Results from the Kaggle State of Data Science and Machine Learning survey of data professionals revealed that job satisfaction varies widely across job titles. Data professionals who reported the highest level of job satisfaction were: 1) Machine Learning Engineers, 2) Data Scientists and 3) Predictive Modeler. Data professionals who reported the lowest level of job satisfaction were: 1)… → Read More
Top Machine Learning and Data Science Methods Used at Work
The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. A recent survey by Kaggle revealed that data professionals used data visualization, logistic regression, cross-validation and decision trees more than other data science methods in 2017. Looking ahead to 2018, data professionals are most interested in… → Read More
Which Data Science Tools are Used Together?
Analysis of usage of 48 data science tools by over 10,000 data professionals showed that data science tools could be grouped into a smaller set (specifically, 14 tool groupings). That is, some data science tools tend to be used together apart from other data science tools. Implications for vendors and data professionals are discussed. Data professionals rely on data science tools, technologies… → Read More