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In this session, Susan Walsh will share real-life examples of dirty data, and the consequences it has on the output, such as decision making, reporting, analytics, AI, and machine learning. You’ll also learn how to make quick, accurate checks and changes to your own data in excel, regardless of your level of experience, explain why data accuracy and maintenance are so important, and implement best practices for this.
One of the most common data science questions is what language beginners should learn, R or Python. This has led to a rivalry between the two languages, termed the "Language War". The purpose of this talk is to announce that this rivalry is over, and we are entering a new era. We'll go through the main defining features of both languages (influenced by their history) and how they compare between different workflows in data science (i.e., data visualization, machine learning) and data types (i.e., text, image, or time series). As a final element, I'll show what methods are available for combining both in the same workspace and demonstrate this with a case study. At the end of the talk, you'll be able to appreciate why being bilingual is essential for a modern data scientist and what are the best ways to get started.
This talk will introduce the foundational business skills you'll need to deliver business value and grow your career as an analyst or data scientist. Drawing on best practices, published research, case studies, and personal anecdotes from two decades of industry experience, David Stephenson will give an overview of foundational skills related to Company, Colleagues, Storytelling, Expectations, Results, and Careers--emphasizing how each topic relates to your unique position as an analytics professional within a larger corporation.