Fear of disruption. It's a common feeling across today's business landscape: fear that a more agile company will swoop in with a pure-play digital business model, leveraging data in ways that compel competitive advantage. Big data is facilitated by technology, but it's optimized by people, culture, and processes. Investment in big data and related technologies is at an all-time high, yet according to research by New Vantage Partners, less than half of the companies say they are competing on data and analytics (48 percent); only 31 percent have created a data-driven organization, and only 28 percent have forged a data culture.
The challenge in realizing the potential of big data lies not in the technology itself, but rather in transforming people, culture, and processes. Berkeley's Data Science: Bridging Principles and Practice gives participants with non-technical backgrounds a foundational understanding of what data science and analytics is all about, and many of the most common techniques used to manipulate and analyze data yourself.
At the end of this program, you should be able not only to work effectively with data science and analytics teams by speaking their language, but also help guide them to deliver business value to you and the entire organization.
- Adapt to a Data Mindset Learn techniques for applying data to answer specific business questions and insights
- Learn the Ability to Communicate and Interpret Data Gain tools to effectively communicate with data scientists and learn how to interpret and present their data
- Create a Data-Driven Culture Learn how to create a culture within your organization that is data-driven and the capabilities that make data science teams successful
Who Should Attend
Non-technical professionals from a variety of backgrounds including:
- Functional roles such as marketing, human resources, product management, R&D, finance, sales, strategy, and innovation
- General management roles such as General Managers, VPs, Directors, or those with regional responsibilities
- Public and private sector organizations
*Program faculty is subject to change
Topic 1 | Probabilistic Decision Making
Topic 2 | Creating Sample Data
Topic 3 | Testing Hypothesis
Topic 4 | Extrapolating Information from Sample Data
Topic 5 | Basic Regression Models
Topic 6 | Advanced Regression Models
Topic 7 | Forecasting Machine Learning
Topic 8 | Building Effective Data Science Teams
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Last updated December 24, 2020
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- USD 5,250
- USD 7,500
- Classroom only
- USD 20,000