Getting started with Python with Google
You’ll begin by exploring the basics of Python programming and why Python is such a powerful tool for data analysis. You’ll learn about Jupyter Notebooks, an interactive environment for coding and data work. You’ll investigate how to use variables and data types to store and organize your data; and, you'll begin practicing important coding skills.
- Explain how to convert data types in Python
- Define fundamental data types such as integer, float, and string
- Interpret the naming conventions and restrictions for variables in Python
- Describe the purpose and procedure of assigning variables
- Define fundamental concepts of object-oriented programming such as object, class, method, and attribute
- Recognize the uses and benefits of Jupyter Notebook for data work
- Identify the basic features and capabilities of the Python programming language
- Explain how to import modules in Python scripts using import
- Introduce Jupyter notebook as a python environment
- Perform arithmetic operations
- Use built-in functions and keywords to explore data
- Understand why Python is relevant to data science
- Understand packages and interpreter options for Python
- Identify the concept of algorithms in python
- Identify the value of Python competency for data careers
- Define Python and what syntax means
- This part of the repository contains an annotated guide (as a Jupyter Notebook) for the module about Python syntax, as well as a short hands-on activity (and an exemplar for that activity) about Python syntax familiarization.
- No additional files (datasets) are needed to for the notebooks to work.
- Should the notebooks are intended to be used, just download the notebook and run the cells.
Next, you’ll discover how to call functions to perform useful actions on your data. You’ll also learn how to write conditional statements to tell the computer how to make decisions based on your instructions. And you’ll practice writing clean code that can be easily understood and reused by other data professionals.
- Explain the purpose and logic of conditional statements such as if, else, and elif
- Use comparators and logical operators to compare values
- List the benefits of commenting on code
- Identify best practices for writing clean code such as reusability, modularity, and refactoring
- Describe how to define Python functions using the def and return keywords
- Compare values using quality operators and logical operators
- Write comments and break code into blocks to reduce complexity
- Refactor code to enhance code readability and reuse
- Define and call functions utilizing parameters and return data
- Use data type conversions
- Differentiate and convert between different data types using variable expressions and built-in functions
- Define variables, expressions, operators
- Introduce basic python syntax
This part of the repository contains an annotated guide (as a Jupyter Notebook) for the module about functions and conditional constructs, as well as hands-on activities (and exemplars for those activities) about the use of functions and conditional constructs. No additional files (datasets) are needed to for the notebooks to work. Should the notebooks are intended to be used, just download the notebook and run the cells.