Explore the Capstone: Data Science Problem in Linear Algebra Framework course offered by Howard University. This hands-on course equips you with the skills to tackle real-world data science problems using linear algebra. Throughout the course, you'll delve into the intricacies of creating and running regression models, data wrangling, and utilizing the PCA function to reduce dimensions. The comprehensive Capstone project will allow you to apply your newfound knowledge and share your results with peers.
Key topics covered include:
This course is designed for individuals seeking hands-on experience in data science and linear algebra, and is suitable for both beginners and those with some background in the field. By the end, you'll have the skills and knowledge to confidently represent and solve data science problems within a linear algebra framework.
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This course comprises an introduction to the specialization and course, data wrangling, using the PCA function, running regression models, and peer review, culminating in a comprehensive Capstone project. Gain practical experience in linear algebra and data science problem-solving.
Embark on your journey with an introduction to the specialization and course, getting acquainted with the instructor and Capstone project details. Engage with peers in the meet and greet forum to prepare for the upcoming modules.
Delve into the fundamentals of data wrangling, utilizing techniques to clean and transform your dataset. Learn to apply Principal Component Analysis (PCA) to effectively reduce the number of dimensions in your dataset, thereby enhancing its efficiency and usability.
Run regression models and gain insights into interpreting results from linear regression modeling. Apply your knowledge to real-world problems and understand the practical implications of using linear regression modeling in various scenarios.
Combine the skills learned in the previous modules to create your Capstone project. Reflect on your learnings and share your insights gained from completing this comprehensive project.
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