Programming for Data Science Overview
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Questions and Answers

What is one reason programming is essential for Data Science?

  • Designing user interfaces
  • Data Retrieval and Manipulation (correct)
  • Creating video games
  • Writing novels
  • What does REPL stand for?

    Read-Eval-Print Loop

    Compiled languages are executed directly without conversion to binary.

    False

    Which of the following is a characteristic of an interpreted language?

    <p>Code is executed line by line</p> Signup and view all the answers

    The command to test if Python is installed correctly is 'python ______'

    <p>--version</p> Signup and view all the answers

    What is the file extension for a Jupyter Notebook?

    <p>.ipynb</p> Signup and view all the answers

    Match the Python characteristics with their descriptions:

    <p>Space sensitive = Indentation is important for defining code blocks. Case sensitive = Variable names are case-sensitive, such as MyName, myName. Scope sensitive = Variables defined in a function are not accessible outside. Single-line comment = Initiated with a # symbol.</p> Signup and view all the answers

    What does the following code print? 'print("Hello World!")'

    <p>Hello World!</p> Signup and view all the answers

    Python allows for multiline comments using triple quotes.

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

    What are variables in programming?

    <p>Labels that carry values</p> Signup and view all the answers

    Study Notes

    Programming for Data Science

    • Data Engineering is improved by programming.
    • Programming allows access to pre-built analytics libraries.
    • Customization, Scalability, Automation and Integration are key benefits of programming for Data Science.

    Why Python?

    • Cross Platform
    • Used for Data Science, AI, IoT, Web and Scripting.
    • Can be used in various modes (REPL/Shell, Program, Interactive).
    • Flexible paradigm: Structural, Functional, and Object Oriented.
    • Large ecosystem of libraries.

    Programming Languages Overview

    • Genesis: Progression from Binary and Assembly to 3rd and 4th Generation Languages.
    • Purpose Driven: Languages designed for specific tasks.
    • General Purpose: Languages like Java and JavaScript designed for a wide range of applications.

    Compiled vs Interpreted Languages

    • Interpreted Languages execute line by line directly.
    • Compiled Languages translate code into binary for a specific operating system.
    • Hybrid languages combine elements of both.

    Programming Paradigms

    • Structural or Procedural (Pascal, Fortran) prioritize sequential execution.
    • Object Oriented emphasizes data and methods in objects.
    • Functional utilizes functions as primary building blocks.

    Installing Python

    Testing Python

    • Open a terminal or command line tool.
    • Type "python --version" to check if Python is installed correctly.
    • Type "code." to test if VS Code is installed correctly.

    Running Python

    • Interactive Shell or REPL Interpreter: Execute code line by line.
    • Python File: Create a file with the ".py" extension and run from the terminal using "python filename.py".
    • Jupyter Notebook: Combines interactive and program file modes.
    • Cells allow for code execution and Markdown documentation. Files have the ".ipynb" extension.

    Writing in Python

    • Space Sensitivity: Code blocks are defined by indentation using spaces.
    • Case Sensitivity: Variable names like "MyName", "myName" and "myname" are different.
    • Scope Sensitivity: Variables defined within functions are only accessible within those functions.

    Comments in Python

    • Single Line Comments: Use the "#" symbol.
    • No Multiline Comments: Instead, use multiple single line comments or a docstring for multiline documentation.

    Hello World!

    • The print("message") function outputs a message to the user.
    • print ("Hello World!") will display the text "Hello World!" on the screen.

    Variables

    • Variables are like labeled boxes that store values.
    • Think of them as containers holding specific types of data.

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    Description

    Explore the essential role of programming in data engineering and data science. Understand why Python is a favored language due to its flexibility and vast library ecosystem. Learn about the evolution of programming languages and the distinction between compiled and interpreted languages.

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