What will you in the Data Science in Real Life Course
Understand how data science is applied in real-world projects beyond idealized classroom settings.
Identify challenges such as poor experimental design, data bias, and communication gaps.
Explore techniques to manage data pipelines, deal with missing data, and avoid common pitfalls.
Learn how data science managers navigate team dynamics and stakeholder expectations.
Differentiate between traditional statistical inference and machine learning approaches in applied work
Program Overview
1. Introduction to Real-World Data Science
Duration: 4 hours
Discusses the contrast between textbook data science and real-life projects.
Overview of experimental design challenges including bias, missingness, and randomization.
Addresses data acquisition issues, reproducibility, and team communication.
Highlights the role of a data analysis leader in a practical business or research setting.
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Job Outlook
Data Science Managers: Gain a real-world framework for managing teams and stakeholder expectations.
Business Analysts: Understand the reality of working with incomplete or messy data.
Data Analysts & Scientists: Learn the non-technical factors that impact project success.
Researchers: Improve your ability to design clean, interpretable, and testable experiments.
Executives & Stakeholders: Gain context for how data-driven teams operate and deliver value.
Specification: Data Science in Real Life
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