- Building a solid data team - Dec 8, 2021.
How do you put together a solid data science team when it comes to developing data-driven products? A variety of roles are available to consider, so which ones do you need and which are most crucial?
- Two Simple Things You Need to Steal from Agile for Data and Analytics Work - Nov 16, 2021.
Peer Review and Definition of Done: small changes, BIG impact.
- Agile Data Labeling: What it is and why you need it - Aug 16, 2021.
The notion of Agile in software development has made waves across industries with its revolution for productivity. Can the same benefits be applied to the often arduous task of annotating data sets for machine learning?
- Can Data Science Be Agile? Implementing Best Agile Practices to Your Data Science Process - Jan 18, 2021.
Agile is not reserved for software developers only -- that's a myth. While these effective strategies are not commonly used by data scientists today and some aspects of data science make Agile a bit tricky, the methodology offers plenty of benefits to data science projects that can increase the effectiveness of your process and bring more success to your outcomes.
- Data Science as a Product – Why Is It So Hard? - Dec 30, 2020.
Developing machine learning models as products that deliver business value remains a new field with uncharted paths toward success. Applying well-established software development approaches, such as agile, is not straightforward, but may still offer a solid foundation to guide success.
- Is Your Machine Learning Model Likely to Fail? - Nov 27, 2020.
Read about these 5 missteps to avoid in your planning process.
- A Holistic Framework for Managing Data Analytics Projects - May 22, 2020.
Agile project management for Data Science development continues to be an effective framework that enables flexibility and productivity in a field that can experience continuous changes in data and evolving stakeholder expectations. Learn more about the leading approaches for developing Data Science models, and apply them to your next project.
- Top 5 must-have Data Science skills for 2020 - Jan 8, 2020.
The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market.
- Why software engineering processes and tools don’t work for machine learning - Dec 5, 2019.
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.
- A Doomed Marriage of Machine Learning and Agile - Nov 28, 2019.
Sebastian Thrun, the founder of Udacity, ruined my machine learning project and wedding.
- How to Make an Agile Team Work for Big Data Analytics - Oct 31, 2019.
Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value for your Big Data products.
- KDnuggets™ News 19:n12, Mar 27: My Best Tips for Agile Data Science Research; R vs Python for Data Visualization - Mar 27, 2019.
Tips for Agile Data Science Research, R (ggplot2) vs Python (Seaborn) Visualization, the problems with self-serve analytics, an approach to AI Blackbox explanation problem, a checklist for debugging neural nets, and more.
- My Best Tips for Agile Data Science Research - Mar 21, 2019.
This post demonstrates how to bring maximum value in minimal time using agile methods in data science research.
- KDnuggets™ News 19:n07, Feb 13: The Best and Worst Data Visualizations of 2018; Gartner 2019 Magic Quadrant for Data Science Platforms - Feb 13, 2019.
Also: Data-science? Agile? Cycles?; How I used NLP (Spacy) to screen Data Science Resumes; Neural Networks - an Intuition; A Quick Guide to Feature Engineering; Understanding Gradient Boosting Machines
- Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry. - Feb 7, 2019.
The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology.
- Virtual Training Events Without Leaving Your Desk - May 30, 2018.
Check out our lineup of upcoming virtual seminars, online learning courses, and customized training in your office. Space is limited, so reserve your seat early and score the best savings!
- Getting Real World Results From Agile Data Science Teams - Feb 10, 2017.
In this post, I’ll look at the practical ingredients of managing agile data science. By using agile data science methods, we help data teams do fast and directed work, and manage the inherent uncertainty of data science and application development.
- Turbo Charge Agile Processes with Deep Learning - Feb 7, 2017.
The key to leveraging Deep Learning, or more broadly AI, in the workplace is to understand where it fits within an agile development environment.
- Navigating the World of Big Data Analytics - Dec 8, 2016.
Fulcrum Agile Analytics Lab- helps our partners test new technologies, new methodologies and new data sets quickly in an environment that can scale up and down and that meets all of their security and compliance requirements. Read to learn more and schedule a consultation.
- Interview: Rachel Hawley, SAS on the Quest for Agile Analytics - Feb 3, 2015.
We discuss Agile Analytics, moving from traditional Analytics to Agile, challenges in operationalizing Analytics, SAS Enterprise Decision Management and SAS In-Memory Statistics.