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5 day workshop handouts
The document outlines a workshop agenda focused on data science, spanning five days with various sessions that cover topics such as problem statements, exploratory data analysis, model building, and stakeholder communication. Participants will set learning objectives, engage in hands-on sessions for data wrangling, and model evaluation, while also having opportunities for mentorship through Q&A. Key takeaways emphasize understanding the data science pipeline and effectively communicating insights to stakeholders.
5 day workshop handouts
- 1. Copyright 2018. Allrights reserved
- 2. Copyright 2018. Allrights reserved Workshop Agenda Summary 2 5 4 3 6 7 Expectation Setting Introduction to Data Science Problem Statement & Exploratory Data Analysis Model Building Stakeholder Communication SESSION AMA on Data Science Learning Path 1 SESSIO N SESSIO N 2 WALKTHROUGH WALKTHROUGH SESSIO N Day 1 Model Evaluation WALKTHROUGH SESSIO N Day 2 Day 3 Day 3 Day 4 Day 4 Day 5 Day 1
- 3. Copyright 2018. Allrights reserved Name What are your learning objectives over the next 2 days: Where would you implement what you have learnt? Goal Setting 3
- 4. Copyright 2018. Allrights reserved Key Takeaways Storyboard You will get a birds’ eye view of the entire landscape of Data Science. Reflect on the session just passed and note down your takeaways. Introduction to Data ScienceSESSION 4 Data Science is a way of thinking about and acting on the world so as to learn more about it and to better achieve our goals.
- 5. Copyright 2018. Allrights reserved Key Takeaways Storyboard You will get an overall idea of the problem you will be solving and get end to end view of the data science pipeline. Reflect on the session just passed and note down your takeaways. Understanding the Problem StatementSESSION 5 Data Science is a way of thinking about and acting on the world so as to learn more about it and to better achieve our goals.
- 6. Copyright 2018. Allrights reserved Storyboard A deep dive into wrangling the data and getting the first level insights into the data. We will look at the complete data analysis pipeline and understand the key business insights that we can communicate at the first level itself. Key Takeaways Exploratory Data Analysis 6 WALKTHROUG H
- 7. Copyright 2018. Allrights reserved Storyboard Apply vanilla models on the data and then try to improve the model performance step-by-step. Key Takeaways Model Building 7 WALKTHROUG H
- 8. Copyright 2018. Allrights reserved Storyboard Metric Design and the tradeoffs we need to take while choosing the right metric and model. Need to ensure that we align to the overall business metric. Key Takeaways Model Evaluation 8 WALKTHROUG H
- 9. Copyright 2018. Allrights reserved Notes/ Questions Storyboard How to communicate to the stakeholders our results? Things to take care while communicating to the stakeholders. Key Takeaways Stakeholder CommunicationSESSION 9
- 10. Copyright 2018. Allrights reserved Notes/ Questions Storyboard Ask the Mentor anything on Data Science. Key Takeaways Data Science AMASESSION 10
- 11. Copyright 2018. Allrights reserved Thank You
Editor's Notes
- #3 Jump into some prototypes - How will you adapt them to your use cases. NLP - Fundamentals , Sentiment Analysis. Customer complaints is a top use case at barclays Big Data Architecture - Step back from individual methods and problems, ask ourselves how do we design individual systems. Getting these things to fit together and how it goes in productionalization.. We will walk through an actual FinTech architecture - delivering a loan for a mobile architecture. Architecture begins with code in ipython notebook, then virtual environment, then version control, container ML Deployments Portability Maintainence Scale Trust