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Super Actionable Study Plan | Start Chat

Act as a coding tutor that creates actionable study plans to help people learn to code. You will be given the learning goals, time commitment of the students, and resources preferences. You will create a study plan with timelines and links to resources. Only include relevant and popular resources. My first question:" I want to become a data scientist but I do not know how to code in Python. I can spend 10 hours a week learning and prefers video resources. Create a study plan for me to learn Python for data science.

Prompt

Act as a coding tutor that creates actionable study plans to help people learn to code. You will be given the learning goals, time commitment of the students, and resources preferences. You will create a study plan with timelines and links to resources. Only include relevant and popular resources. My first question:" I want to become a data scientist but I do not know how to code in Python. I can spend 10 hours a week learning and prefers video resources.  Create a study plan for me to learn Python for data science.

Welcome Message

Great! Here's a study plan with a timeline and list of resources for you to learn Python for data science:

Week 1-2: Python Basics

  • Spend 2-3 hours per week learning the basics of Python syntax, variables, data types, and control structures.

  • Complete one of the following video courses:

    • "Python for Data Science Bootcamp" by Jose Portilla on Udemy (approx. 22 hours)

    • "Complete Python Bootcamp: Go from zero to hero in Python 3" by Jose Portilla on Udemy (approx. 24 hours)

    • "Python Fundamentals for Data Science" on DataCamp (approx. 16 hours)

Week 3-4: Data Science Libraries in Python

  • Spend 2-3 hours per week learning popular data science libraries such as NumPy, Pandas, and Matplotlib.

  • Complete one of the following video courses:

    • "Data Science and Machine Learning Bootcamp with R" by Jose Portilla on Udemy (approx. 26 hours) - This course also covers Python data science libraries.

    • "Data Manipulation with pandas" on DataCamp (approx. 13 hours)

    • "NumPy for Data Science" on DataCamp (approx. 6 hours)

Week 5-6: Data Cleaning and Preprocessing

  • Spend 2-3 hours per week learning about data cleaning and preprocessing techniques using Pandas.

  • Complete one of the following video courses:

    • "Data Wrangling with Pandas" on DataCamp (approx. 17 hours)

    • "Exploratory Data Analysis in Python" by Coursera (approx. 8 hours)

Week 7-8: Advanced Topics in Data Science

  • Spend 2-3 hours per week learning advanced topics in data science such as Scikit-Learn, TensorFlow, and Keras.

  • Complete one of the following video courses:

    • "Machine Learning A-Z" by Kirill Eremenko on Udemy (approx. 40 hours)

    • "Applied Data Science with Python" on Coursera (approx. 35 hours)

Week 9-10: Practice and Projects

  • Spend 1-2 hours per week practicing what you have learned and working on projects to build your portfolio.

  • Participate in Kaggle competitions or build your own data science projects.

Resources:

Conversation

ASSISTANT: Great! Here's a study plan with a timeline and list of resources for you to learn Python for data science:

Week 1-2: Python Basics

  • Spend 2-3 hours per week learning the basics of Python syntax, variables, data types, and control structures.

  • Complete one of the following video courses:

    • "Python for Data Science Bootcamp" by Jose Portilla on Udemy (approx. 22 hours)

    • "Complete Python Bootcamp: Go from zero to hero in Python 3" by Jose Portilla on Udemy (approx. 24 hours)

    • "Python Fundamentals for Data Science" on DataCamp (approx. 16 hours)

Week 3-4: Data Science Libraries in Python

  • Spend 2-3 hours per week learning popular data science libraries such as NumPy, Pandas, and Matplotlib.

  • Complete one of the following video courses:

    • "Data Science and Machine Learning Bootcamp with R" by Jose Portilla on Udemy (approx. 26 hours) - This course also covers Python data science libraries.

    • "Data Manipulation with pandas" on DataCamp (approx. 13 hours)

    • "NumPy for Data Science" on DataCamp (approx. 6 hours)

Week 5-6: Data Cleaning and Preprocessing

  • Spend 2-3 hours per week learning about data cleaning and preprocessing techniques using Pandas.

  • Complete one of the following video courses:

    • "Data Wrangling with Pandas" on DataCamp (approx. 17 hours)

    • "Exploratory Data Analysis in Python" by Coursera (approx. 8 hours)

Week 7-8: Advanced Topics in Data Science

  • Spend 2-3 hours per week learning advanced topics in data science such as Scikit-Learn, TensorFlow, and Keras.

  • Complete one of the following video courses:

    • "Machine Learning A-Z" by Kirill Eremenko on Udemy (approx. 40 hours)

    • "Applied Data Science with Python" on Coursera (approx. 35 hours)

Week 9-10: Practice and Projects

  • Spend 1-2 hours per week practicing what you have learned and working on projects to build your portfolio.

  • Participate in Kaggle competitions or build your own data science projects.

Resources: