Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.
Ce cours fait partie de la Spécialisation Science des données exécutive
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Identify strengths and weaknesses in experimental designs
Learn novel solutions for managing data pulls
Describe common pitfalls in communicating data analyses
Understand a typical day in the life of a data analysis manager
Compétences que vous acquerrez
- Statistics
- Data Science
- Data Analysis
- Data Management
Votre entreprise pourrait-elle bénéficier de la formation des employés à des compétences recherchées ?
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Programme de cours : ce que vous apprendrez dans ce cours
Introduction, the perfect data science experience
Avis
- 5 stars60,77 %
- 4 stars27,76 %
- 3 stars8,28 %
- 2 stars2,06 %
- 1 star1,11 %
Points forts
Meilleurs avis pour DATA SCIENCE IN REAL LIFE
Brian Caffo does a terrific job teaching some of more advanced material, I very much appreciate his jokes and humor, as well as his helpful explanations of the material.
Examples used in this course are related to Lifescience and candidates like me find it difficult to correlate. It would be beneficial to use some common life examples.
Again a course with depth in content but the presentation of the course could improve , it seems a bit patchy and pre-reads would help.
One more thing to add. The link to Study.com works. But Study.com wouldn't let me watch the whole video anyway; it requires registering and subscribing (after trial period is over). Not useful.
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