Entries by tag: data-newsletter-5

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Scaling Data Teams  09 Oct 2017    data-science data-newsletter-5
Needs of data teams are mostly around data access and sharing; Columnar databases are often more efficient for analytics; MS Excel is useful at many scales; Stakeholder communication is important to make your work more relevant; Use metrics to get to know how data products are being used. Read More ›

Matplotlib, Pylab, Pyplot, etc: What's the difference between these and when to use each?  26 Sep 2017    python data-visualization data-newsletter-5
Do you often get confused with terms like maptlotlib, pyplot, pylab, figures, axes, gcf, gca, etc and wonder what they mean? Matplotlib is the toolkit, PyPlot is an interactive way to use Matplotlib and PyLab is the same thing as PyPlot but with some extra shortcuts. Read More ›

5 Tips for moving your Data Science Operation to the next Level  25 Sep 2017    data-newsletter-5 data-science best-practices
Principles for disciplined data science include: Discoverability, Automation, Collaboration, Empowerment and Deployment. Read More ›

Highlights of the Talk with Dr. Konstan on Recommender Systems  23 Sep 2017    recommender-systems data-newsletter-5
Some highlights of the Podcast Episode with Dr. Joseph Konstan on interesting topics related to Recommender Systems. Discussed topics include serendipity, serpentining, diversity and temporal effects. Read More ›

Data Provenance: Quick Summary + Reasons Why  07 Sep 2017    data-newsletter-5 data-science
Data Provenance (also called Data Lineage) is version control for data. It refers to keeping track of modifications to datasets you use and train models on. This is crucial in data science projects if you need to ensure data quality and reproducibility. Read More ›