CS250: Python for Data Science

Unit 2: Python for Data Science

• Upon successful completion of this unit, you will be able to:

  • create a Python notebook using Google Colab;
  • execute instructions using built-in Python data and control structures;
  • apply methods for random numbers within the random module; and
  • implement basic plotting and data rendering instructions using the matplotlib module.

• 2.1: Google Colaboratory

  • Introduction to Google Colab URL

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    The vast majority of coding examples in this course will be phrased in the form of a Python notebook. Google Colaboratory (or, just Google Colab) is an online interface for creating Python notebooks. It is extremely convenient because a vast set of Python modules (and, in particular, the modules used in this course) are already installed and, once imported, are ready for use.

    Assuming you have a Gmail account and are logged in, click the link provided to run Google Colab notebooks. In the menu bar, choose

    File --> New notebook

    
    After doing so, you should see an editable code cell appear in which you can enter a series of Python instructions. Your notebook is automatically linked to the Google Drive associated with your Gmail account. Your Google Colab notebooks are automatically created on your Google Drive in My Drive in a folder named Colab Notebooks. You can treat them as files within Google Colab and perform typical file operations such as renaming, opening, and saving.

    Upon entering some basic lines of Python code in the code cell such as:

    print('Hello world')
    a = 10
    print('a =', a)

    you can practice executing your code cell in one of several ways:
    
    

    1. Click the Play button on the left side of the cell.
    2. Type Alt+Enter: runs the cell and inserts a new, empty code cell immediately following it.
    3. Type Shift+Enter: runs the cell and moves attention to the next cell (adding a new cell if none exists).
    4. Type Cmd/Ctrl+Enter: runs the cell, but control does not advance to the next cell (control remains "in place").

    In this course, you will be introduced to several Python modules applicable for data science computations and program implementations:

    • numpy: array operations
    • pandas: dataframe operations
    • scipy.stats: basic statistics
    • matplotlib, pandas, seaborn: visualization
    • scikit-learn: data mining
    • statsmodels: time series analysis

    Upcoming units will explain and apply these modules. Before doing so, this unit will first review the basics of the Python programming language.

• 2.2: Datatypes, Operators, and the math Module

  • Data Types in Python Page

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    Fundamental to all programming languages are datatypes and operators. This lesson provides a practical overview of the associated Python syntax. Key takeaways from this unit should be the ability to distinguish between different data types (int, float, str). Additionally, you should understand how to assign a variable with a value using the assignment operator. Finally, it is essential to understand the difference between arithmetic and comparison (or "relational") operators. Make sure to thoroughly practice the programming examples to solidify these concepts.
    

  • Operators and the math Module Page

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    The math module elevates Python to perform operations available on a basic calculator (logarithmic, trigonometric, exponential, etc.). At various points throughout the course, you will see this module invoked within programming examples using the import command:

    import math

    Commands from this module should be part of your repertoire as a Python programmer.

    We conclude this section by putting together concepts regarding datatypes, operators, and the math module. Upon completing this tutorial, you should be pretty comfortable with the basics of the Python programming language.
    

• 2.3: Control Statements, Loops, and Functions

  • Functions, Loops, and Logic Book

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    The core of Python programming is conditional if/else/elif control statements, loops, and functions. These are powerful tools that you must master to solve problems and implement algorithms. Use these materials to practice their syntax. If/else/elif blocks allow your program to make conditional decisions based upon values available at the time the block is executed. Loops allow you to perform a set of operations a set number of times based on a set of conditions. Python has two types of loops: for loops and while loops. For loops work by counting and terminating when the loop has executed the prescribed number of times. While loops work by testing a logical condition, and the loop terminates when the logical condition evaluates to a value of false. Functions are designed to take in a specific set of input values, operate on those values, and then return a set of output results.
    

  • Functions and Control Structures Page

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    Use this video to reinforce the syntax and applications of if/else/elif statements, loops, and functions. Make sure you feel comfortable writing functions and testing values as they are input and returned from functions that you have written. Additionally, understanding program flow as you navigate through if-elif blocks implies that you can predict the outcome of the block before the code executes. This way, you will convince yourself that you understand how if/else/elif statements work. Finally, practice writing loops and predicting what values variables should take during each loop iteration.
    

• 2.4: Lists, Tuples, Sets, and Dictionaries

  • Data Structures in Python Page

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    Python has several built-in data structures. Lists allow you to create a collection of items that can be referenced using an index, and you can modify their values (they are "mutable"). Tuples are similar to lists except that, once created, you cannot modify their values (they are "immutable"). Dictionaries allow you to create a collection of items that can be referenced by a key (as opposed to an index). Use this section to learn about and practice programming examples involving lists, tuples, and dictionaries.
    

  • Sets, Tuples, and Dictionaries Book

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    Here is more practice with tuples and dictionaries. In addition, the Python built-in data structure known as a set is also covered. Sets are not ordered, and their elements cannot be indexed (sets are not lists). To understand Python set operations, remind yourself of basic operations such as the union and intersection. Use this tutorial to compare and contrast the syntax and programming uses for lists, tuples, sets, and dictionaries.
    

  • Examples of Sets, Tuples, and Dictionaries Page

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    Watch this video for more examples of tuples, sets, and dictionaries. Practice the examples and make sure you understand how to apply these data structures using functions. This video is designed to help you put together the concepts in this section.
    

• 2.5: The random Module

  • Python's random Module Page

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    Before diving into some aspects of random number generation using more advanced modules, review how to generate random numbers using the random module by practicing the code shown in these videos.
    

• 2.6: The matplotlib Module

  • Visualization and matplotlib Page

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    The matplotlib module is the first of several modules you will be introduced to for plotting, visualizing, and rendering data. Beginning at 20:40, follow this video to learn the basics of applying matplotlib. It is fundamental to understand how to annotate a graph with labels. We will revisit many of these concepts in upcoming units. Make sure to implement the programming examples to get used to the syntax.
    

  • Precision Data Plotting with matplotlib Page

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    Although matplotlib can work with Python lists, it is often applied within the context of the numpy module and the pandas module. This tutorial briefly references these modules, which will be introduced and elaborated upon in upcoming units. For now, follow along with the programming examples to learn some more basic matplotlib plotting commands.
    

• Unit 2 Assessment

  • Unit 2 Assessment Quiz

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    Take this assessment to see how well you understood this unit.

    • This assessment does not count towards your grade. It is just for practice!
    • You will see the correct answers when you submit your answers. Use this to help you study for the final exam!
    • You can take this assessment as many times as you want, whenever you want.