Data Science Roles and Their Skill Sets An excellent way to learn about a subject is to ask people involved in that subject. Specifically, to learn about data science and machine learning, it is good to ask data professionals who work in those areas. A few years ago, I conducted a study by asking data […]
A worldwide survey of data professionals showed that adoption of machine learning methods in their company is 45%. Twenty-one percent of survey respondents said their employer is exploring ML methods. ML adoption rates varied by country with Israel (63%), Netherlands (57%) and the United States (56%) showing the highest and Egypt (31%), Morocco (24%) and […]
Data professionals prefer analytics tools over business intelligence tools when getting insights from their data. Nearly half of data professionals surveyed in Kaggle’s 2020 Data Science and Machine Learning Survey said they do not use BI tools. The top tool used by data professionals to analyze data are local development environments (48%), followed by basic […]
Data professionals of all stripes, including data scientists, machine learning engineers and others, use different types of tools in their jobs. A recent survey of over 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 (86% used). Additionally, […]
Analysis of usage of 5 primary tools used to analyze data showed that the top tool used by data professionals to analyze data are local development environments (54%), followed by basic statistical software (20%), cloud-based data software and APIs (8%), advanced statistical software (6%) and business intelligence software (6%). Tool usage differed across different job […]
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 tools […]
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% […]
Data science projects require data professionals to devote their energy toward different activities toward project completion. Results of a recent study of over 23,000 data professionals found that data scientists spend about 40% of gathering and cleaning data, 20% of their time building and selecting models and 11% of their time finding insights and communicating […]
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