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In the last couple of years, the term data science is increasingly used to describe advanced analytical methods that can extract business value from data. While many of these methods are decades old, have solid foundations in the fields of statistics and computer science (e.g. machine learning, data visualization) and can unarguably provide tremendous business value, there is a fair amount of hype, abuse of terms (especially related to “big data”) and misunderstanding of data science both in the media and in the business and tech community. This talk will help anyone with interest in data science, but especially business and tech leaders (startup co-founders, CEOs, CTOs, analytics executives, product managers etc.) get a more realistic picture of data science, set the right expectations for data science projects and learn where to focus on in order to succeed in extracting business value from data. First, we will review the main components of the data analysis process (business understanding, data acquisition, exploratory data analysis, data cleaning, statistical modeling, model validation, deployment, communication of results) and we will discuss some of the most common pitfalls. Next we will review some and the tools that are commonly used to perform the tasks of data analysis, we will discuss what are the skills to look for when you hire a data scientist, and we will talk about organizational structures that make data science projects most effective. Finally, we will close the session with Q&A (we will reserve plenty of time for questions, so that participants can get information personalized to their needs).
To benefit the most, attendees are expected to have some basic business, technical and analytical knowledge.
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