Data Science
Thursday, October 21, 2021
Data Science Made Real
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Workshop Overview:
Join us to learn some best practices to apply data science technology to transform your business. Currently, 80% of advanced predictive analytics solutions using AI and Machine Learning technologies fail. Reason: - lack of knowledge and methodology to the diverse team of Business Analysts, Product Managers and Data Analysts. - Failure to collaboratively develop solutions that effectively address business needs. The knowledge you would gain will help you gain practical ways to address these challenges.
Intended Audience:
You are a product or project manager to apply data science/AI capabilities in your business solution You are an executive to leverage AI capabilities in transforming your business You are a business analyst to ensure right data science solution is developed
Topics Covered:
Analytics project lifecycle and how it differs from traditional project development Best practices to engage diverse team to develop clear business problem Ways to measure solution success
Workshop Takeaways:
Learn research questions to ask to apply data science in business Learn project management techniques to successfully deliver data science solutions
Friday, October 22, 2021
Modernizing Data Science and Analytics Roles
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Bill Franks
School of Data Science and Analytics at Kennesaw State University, Director of the Center for Statistics and Analytical ResearchBreaking the Obstacles to Healthcare Ecosystem Analysis
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Change Healthcare offers a complete solution for Healthcare Data Analysis 1. Comprehensive Healthcare Data Assets + Social Determinants of Health 2. Always on, Automated Compliance Framework Enabling Fast and Robust Analytics 3. Cloud Computing Facility that that Supports Customer Tools and Customer Data 4. High-Fidelity Identity Resolution to Integrate Data 5. Applications and Success Stories
Architecting Data Science Services in AWS
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In modern data science work our stakeholders ask a lot of practitioners, our role expands quickly from model builder to engineer, architect and optimizer when the project requires it. In addition to developing accurate models, projects require the processing of big data, regular retraining, flexible infrastructure, and speedy delivery. Slalom recently completed work to develop a model which predicted customer exposure for mixed channel advertising campaigns. However, the business partners did not want to know a predicted value – they wanted to know the best predicted value and how to get there. Our solution relies on the cohesion of services across the AWS Cloud – including Amazon SageMaker, API Gateway and Amazon ECR – in order to produce a robust and informed recommendation engine which returns optimal budget splits to users in seconds.
Improving Data Science ROI using ML Ops
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Artificial Intelligence and Machine Learning are key technologies that can help companies build new capabilities, access new markets, and improve operations. The challenge to modern companies is not just learning to leverage AI/ML, but to do so in a way that reliably converts effort to business value. Having seen first-hand the challenges and solutions associated with AI/ML, I can attest to the benefit of grounding this effort in a foundation of best practices. ML Ops is a relatively new take on emerging trends in software engineering and dev ops, adapted for data scientists, that simplifies and streamlines ways of working with AI/ML. Cloud platforms like AWS provide a wide array of tools that make this easy. In this presentation, I will describe the typical struggles most organizations have with AI/ML and chart a way forward that any company can use to start improving their ROI on advanced AI/ML initiatives.
Using Data Science to Inform Talent Strategy
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An overview of the People Analytics field, how companies can use data science and advanced analytics to inform their talent strategy with impactful use cases.
The Importance of Context
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One of the most valuable skills I’ve gained in my short time as a data/decision scientist is understanding the context of my work. It helps with maximizing the value I bring to the business, communicating the results of my analyses, and with prioritizing my work. In my experience there are a few context areas that I have found useful: stakeholders, your team, and the business. Understanding the context around these areas can lead to more impactful work, faster lead times, and better value-added project prioritization. These items seem obvious, but the impact can be tremendous.
While understanding the project requirements is important, it is also important to know how and why those requirements were determined. Often there are assumptions made within an analysis/project based on initial requirements gathering efforts. Understanding the context that stakeholders are working within can help limit the number of assumptions, and ensure assumptions are within an acceptable range of risk. Team context helps to ensure members are working on items that maximize their learning experience while making sure they are providing maximum business value. This leads to better team morale and a better product. Finally, business context helps analysts understand where they sit within the organization and how the users of that analysis operate. This is essential when building a tool (Dashboard, ML model, etc.) that has end users within the business, helping the analyst stay ahead of questions that are likely to be asked in the future and build the tool in a robust enough way to prevent additional re-work.
Understanding the context can be overlooked while trying to find the right algorithm, or analysis design, but can be the most valuable part of a project and is essential for ensuring maximum value for the business while maintaining a happy team environment.