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Capstone: Data Science Problem in Linear Algebra Framework

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Free Online Course: Capstone: Data Science Problem in Linear Algebra Framework provided by Coursera is a comprehensive online course, which lasts for 4 weeks long, 8-9 hours worth of material. The course is taught in English and is free of charge. Upon completion of the course, you can receive an e-certificate from Coursera. Capstone: Data Science Problem in Linear Algebra Framework is taught by Moussa Doumbia.

Howard University via Coursera
4 weeks long, 8-9 hours worth of material
In progress
Overview

  • In this course, you'll review the specifics of the Capstone project. In addition, you will create and run your regression model and share your results with your peers. Let's get started!

Syllabus

    • Introduction to Specialization and Course
      • In module 1, you’ll learn about the steps you will take to complete the Capstone Project. We will cover the following learning objectives.
    • Data Wrangling & Using the PCA Function
      • Let’s recap! In module 1, you were introduced to the Capstone project and Tasks 1-4. In module 2, you’ll complete Task 1: Data wrangling and Task 2: Use PCA to reduce the number of dimensions. We will cover the following learning objectives.
    • Run Your Model and Interpret Your Results
      • Let’s recap! In module 2, you learned how to complete Tasks 1 & Task 2. In module 3, you will learn how to complete Task 3: Run your regression model and Task 4: Interpret the results from your model. We will cover the following learning objectives.
    • Peer Review: Interpreting Results Using Your Model
      • Welcome to the final module of this course! Over the past 3 modules, you have been introduced to and gained knowledge on the following topics: Data wrangling (Task 1),Use PCA to Reduce the Number of Dimensions (Task 2), Run your regression model (Task 3) and, Interpret the results from your model (Task 4). In this final module you will prepare your final Capstone Project, submit it, discuss what you learned and complete peer reviews. We will cover the following learning objectives.

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