Building an insight-Driven Culture with Data Literacy

Data Culture require an iterative process to be achieved. Once it is achieved it can be used to make better decisions. Data culture can be best practiced in a team. The state of data culture determine their level of data science maturity. This presentation basically explain how to identify quality in a Data Science team.

Transcript

1. - AKANDE Imisioluwa Afolabi Building an Insight-Driven Culture with Data Literacy
2. What is data-driven culture? A data-driven culture is an act of using data to make better decisions in a team and having access to the data you need when you need it.
3. Data Literacy
4. What we can do with data? - Transformational Analytics - Learning Analytics - Predictive Analytics
5. Different teams, Different levels of maturity
6. CAPABILITY MATURITY MODEL INTEGRATION
7. How to rate the quality of a Data Science Team 1. Can new hires get set up in the environment to run analysis on their first day? 2. Can data scientists utilize the latest tools/packages without help from IT? 3. Can data scientists use on-demand and scalable compute resources without help from IT/dev ops? 4. Can data scientists find and reproduce past experiments and results, using the original code, data, parameters, and software versions? 5. Can predictive models be deployed to production without custom engineering or infrastructure work?
8. Can new hires get set up in the environment to run analysis on their first day?
9. Popular Python Package managers that can be used to setup Database environment 1. Pip 2 Virtualenv 3. Anaconda 4. Enthought Canopy
10. You never studied programming subject in school AND You cannot write a line of code? Whoop!!!. You feel you are doomed to have an unsuccessful career in data science
11. 1. RapidMiner 2. DataRobot 3. BigML 4. Cloud Machine Learning Engine 5. Paxata 6. Trifacta 7. Narrative Science 8. MLBase 9. WEKA 10. Automatic Statistician Tools/packages with little or no programming experience Read Article here - https://www.analyticsvidhya.com/blog/
12. Can data scientists use on-demand and scalable compute resources without help from IT/dev ops?
13. Scalable compute resources on Cloud 1. Microsoft Azure 2. Amazon web service 3. Google Cloud Platform
14. Can data scientists find and reproduce past experiments and results?
15. Data Science Workflow https://cacm.acm.org/blogs/blog-cacm/
16. https://dataversioncontrol.com/ Data Version Control
17. Can predictive models be deployed to production without custom engineering or infrastructure work?
18. Steps involved in deploying Predictive models to production https://www.predictiveanalyticstoday.com/deployment-predictive-models/
19. Examples of Predictive analysis software - Rapid Miner Studio - MATLAB
20. Questions ?
21. Thanks