Data Science Roadmap Overview
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Questions and Answers

What is the suggested first programming language for someone starting in data science?

  • Java
  • C++
  • R
  • Python (correct)
  • Which statistical concept is particularly emphasized as important to understand in data science?

  • Variance
  • Standard Deviation
  • Normal Distribution (correct)
  • Chi-Square Distribution
  • What resource is recommended for learning about Linear Algebra for data science?

  • Linear Algebra Notes by Queen Mary University of London (correct)
  • Coursera Linear Algebra Course
  • MIT OpenCourseWare
  • Khan Academy
  • Which of the following is NOT a database technology mentioned for learning CRUD operations?

    <p>PostgreSQL</p> Signup and view all the answers

    When transitioning to Machine Learning, which book is recommended?

    <p>Hands-On Machine Learning with Scikit-learn and TensorFlow</p> Signup and view all the answers

    Which programming library is specifically mentioned for data visualization?

    <p>Seaborn</p> Signup and view all the answers

    What is an example of a resource for learning Linux commands?

    <p>Basic Commands of Linux by CodeWithHarry</p> Signup and view all the answers

    What mathematical concept related to optimization should one learn for data science?

    <p>Gradient Descent</p> Signup and view all the answers

    Study Notes

    Data Science Roadmap

    • To become a data scientist, learn the necessary things
    • College degree not strictly required, basic math understanding sufficient
    • Resources for each subject are available at the end of the document

    Requirements

    • Statistics and Maths:

      • Learn linear algebra from resources like Queen Mary University of London
      • Understand basic concepts like mean, median, mode, and dy/dx
      • Review probability and statistics using a book like by William Hines
      • Focus on normal distribution and optimization
      • Learn about Gradient Descent and Graphs
    • Programming (Python):

      • Python is recommended as the first programming language
      • Use online resources like 100 Days of Code by CodeWithHarry
      • Learn basic Python concepts, data science libraries (NumPy, Pandas)
      • Free resources and paid courses on platforms like Udemy and Amazon are available
      • Practice creating quick data reports
    • Databases:

      • Learn CRUD operations (Create, Read, Update, Delete) on different database technologies like MySQL, MongoDB, PyMongo, SQLAlchemy.
      • Pick the technology based on data fetching needs
    • Machine Learning:

      • Transition to Machine Learning after gaining proficiency in Python and data science projects
      • Use resources like "Hands-on ML with Scikit-learn and TensorFlow" and relevant project videos
      • Update knowledge by following relevant guides on GitHub or other platforms.
    • Linux and Git:

      • Learn basic Linux commands using videos like from CodeWithHarry
      • Learn version control using Git and how to use it to share code with GitHub
    • Optional Tools:

      • Consider AWS for cloud-based solutions, web scraping using BeautifulSoup, and tools like Tableau or Excel VBA based on needs

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    Related Documents

    Data Science Roadmap PDF

    Description

    This quiz outlines the essential requirements for becoming a data scientist. It covers necessary skills in statistics, programming with Python, and database knowledge, providing resources for each subject. Understand the foundational concepts that will set you on the right path to a career in data science.

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