Skip to content

Latest commit

 

History

History
69 lines (53 loc) · 5.48 KB

resources.md

File metadata and controls

69 lines (53 loc) · 5.48 KB
Raw
layout nav_order title description
page
3
Resources
Resources

Resources 📚

{:.no_toc}

Table of contents

{: .no_toc .text-delta }

  1. TOC {:toc}

Python Documentation

Learning a new programming language like Python is a lot like learning a new language: you have to learn vocabulary and learn how to string words together to make sentences that are gramatically and syntactically correct. Just like you might use a dictionary to learn the meaning of new words, programmers use documentation to understand what functions and data types do and how they operate. Here are documentation sources that are helpful for understanding the functions and objects we use in Data 6:

  • Data 6 Python Reference Guide (D6PR)
    • This has documentation for all of the functions we expect you to know in Data 6. You can use this as your "cheat sheet" while working on assignments. We will also provide a shortened version of the reference guide for the final exam.
  • Data 8 Python Reference (D8PR)
    • The Data 6 Python Reference is based off of the Python Reference for Data 8, which contains many of these same functions that we use plus some that are out of scope for this class.
  • Stanford’s Python Reference (SPR)
    • This website is a Python guide written for Stanford’s intro CS class, and is a great reference if you need a refresher on how something works in Python.
  • Official Python Documentation
    • This may be helpul if you want to learn more about the intricate details of how Python works, but just remember that a lot of these details are out of scope for this class.
  • datascience Library Documentation
    • If you come across a weird error involving the datascience library (anything to do with tables), you might be able to debug the error by looking at the datascience documentation. Again, a lot of this is way more detailed than we will be in Data 6, so don't worry if you don't understand everything in the documentation.

Supplemental Readings

Although there is no specific textbook for this class, we will draw on concepts from a couple of textbooks from similar data science and computer science classes. For most lectures, we will post links to chapters from textbooks covering the topics we will discuss in lecture. These supplemental readings are optional, but you might find it helpful to use these resources to review or to develop a better understanding of certain concepts.

All of these textbooks cover the fundamentals of Python, but in slightly different ways. Only the first link will contain any code that uses the datascience library that we use in this course, but some of the more general Python code may still be helpful/useful.

We do not expect you to read these textbooks in their entirety (in fact, most of their material is out of scope for this class).

  • Computational and Inferential Thinking (CIT), the textbook for Data 8 at UC Berkeley, overlaps with our course significantly with Data 6, though there’s a lot in there that we won’t cover and there’s a lot that we’ll cover that won’t be in there.
  • Python Programming for Data Science (PPDS) is also a good references that covers the material in our class and more.
  • Composing Programs (CP), the textbook for CS 61A and CS 88 at UC Berkeley, covers Python from a more traditional computer science perspective rather than the data science perspective we will take; as such, only a few sub-chapters are relevant to us but you may find it useful nonetheless.
  • Problem Solving with Python (PSP) teaches a lot of practical Python tools, and was written specifically for beginner engineering students.
  • How to Think Like a Computer Scientist is also a great reference.
  • A Reader on Data Visualization (RDV) provides an interesting overview of and introduction to data visualization. This text focuses more on the design of visualizations instead of the technical aspects of creating them.
  • Fundamentals of Data Visualization (FDV) is the online version of Claus Wilke's book that covers the basics of how to tell informative and ethical stories with data visualizations.

Past Exams

Exams from previous semesters can be found below. These exams may differ slightly in terms of material covered and syntax used, as the datascience library and Data 6 curriculum has changed over time.

Semester Final
Summer 2024 Exam (Solutions)
Summer 2023 Exam
Summer 2022 Exam (Solutions)

Other

<script src="../assets/darkmode.js"></script> <script> window.addEventListener("DOMContentLoaded", (event) => { onLoad(); }); </script>