project: Conceptual knowledge check: What is data science?

Hard Prerequisites
IMPORTANT: Please review these prerequisites, they include important information that will help you with this content.
  • DATA-SCIENCE: What is data science?
  • DATA-SCIENCE: Introduction to the data science method
  • DATA-SCIENCE: Why Python and Git
  • CODING_APTITUDE_ASSESSMENT: Basic introduction to Github - Helloworld
  • PROJECTS: Tilde project tutorial: Simple repository projects

    • How to submit your work

    Please follow the following instructions to submit your work:

    [TODO] TOPIC: How to submit your markdown files

    Questions

    1. Consider the steps in the DSM. Should the steps always happen in the same order? Why? Or Why not?

    2. How do computational advances specifically contribute to the field of data science beyond traditional statistical analysis?

    3. In what ways does the interdisciplinary nature of data science enhance its problem-solving capabilities?

    4. Why is Exploratory Data Analysis (EDA) critical before proceeding to the modeling phase in data science projects?

    5. What ethical considerations should be taken into account during the data collection and preparation phase of a data science project?

    6. How does effective communication of results contribute to the success of a data science project?

    7. Why does it make sense for professional data-scientists to be aware of software development best practices? And can you name any practices worth considering? We mentioned some things in this course, feel free to do further reading

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