Computer file

Disks

Buffers

Pages

Records

B+ Trees

Indices & B+ TreeRefinements

Buffer Management

Relational Algebra

Sorting & Hashing

Iterators & Joins

Query Optimization

Transactions

Concurrency

DB Design: Entity-Relationship Models

DB Design: FDs and Normalization

Recovery

Parallel Query Processing

Distributed Transactions

NoSQL

MapReduce

Apache Spark

CS 186 Introduction to Database Systems UC Berkeley Spring 2023 This project-heavy course covers access methods, data models, query languages, database services, and interfaces. It introduces transaction processing and requires CS 61A, CS 61B, and CS 61C as prerequisites/corequisites. It suggests proficiency in Java for project work. Access methods and file systems to facilitate data access. Hierarchical, network, relational, and object-oriented data models. Query languages for models. Embedding query languages in programming languages. Database services including protection, integrity control, and alternative views of data. High-level interfaces including application generators, browsers, and report writers. Introduction to transaction processing. Database system implementation to be done as term project.  We will expect students to have taken CS 61A, CS 61B, and CS 61C. CS61A/B will be enforced as a prerequisite, and CS61C will be enforced as a corequisite. This class is project-heavy, and there will almost always be a project assigned to you throughout the semester. Students have noted an increase in difficulty compared to offerings of this course predating Fall 2018. We expect proficiency with Java (around the level taught in CS 61B) in order to work on the projects, with the exception of Project 1. Projects vary in difficulty. You can find more information about them in the Projects section below. There is no required textbook for this class, and we will not be assigning required readings. However, if you would like to use a textbook while in this class, we recommend the following:

- [Database Management Systems](http://pages.cs.wisc.edu/~dbbook/), 3rd Edition, by Ramakrishnan and Gehrke (Former official textbook of this class)

- [Database Systems Concepts](https://www.db-book.com/), 6th Edition, by Silberschatz, Korth and Sudarshan

- [Database Systems: The Complete Book](http://infolab.stanford.edu/~ullman/dscb.html), 2nd Edition, by Garcia-Molina, Ullman, and Widom (Previously used by Prof. Cheung)

Note that exams and all other aspects of the course will center on material as covered in the lectures, so defer to course material if there are any discrepancies with the textbook(s) you are using.

If you want to try to target your reading to the topics of this class, reading assignments from previous offerings of CS 186 for the Database Management Systems textbook can be found [here](https://docs.google.com/spreadsheets/d/1JfrHl8hn8-iMqL2se4SYS0lxQyiwz54YIATFXuzaPlA/edit?usp=sharing). Reading assignments from a database class taught at a different university by Prof. Cheung for Database Systems: The Complete Book can be found [here](https://courses.cs.washington.edu/courses/cse414/18sp/calendar/lecturelist.html), although the topics taught in that class differ from those taught in this class.

CS 186 Introduction to Database Systems

Database Systems

scikit-image

scikit-learn

pandas (software)

Jupyter Notebook

Code Complexity

Memory management

Python

List (abstract data type)

File Processing

pandas DataFrame

Data Science Libraries

Search

Joins

Images

Statistics

Indexes

Control Structures

Data structure

Data Visualization

Class (computer programming)

Algorithmic Efficiency

Ethics

p-hacking

Tree (data structure)

NumPy

String (computer science)

Machine learning

Object (computer science)

Geospatial Data

Convolution

Algorithmic fairness

Algorithmic Privacy

CSE 163 Intermediate Data Programming University of Washington Summer 2022 This course offers an intermediate level of data programming, focusing on different data types, data science tools, code complexity, and memory management. It emphasizes the efficient use of concepts for data programming. The world has become data-driven. Domain scientists and industry increasingly rely on data analysis to drive innovation and discovery; this reliance on data is not only restricted to science or business, but also is crucial to those in government, public policy, and those wanting to be informed citizens. As the size of data continues to grow, everyone will need to use powerful tools to work with that data.

This course teaches intermediate data programming. It is a follow on to CSE142 (Computer programming I) or CSE160 (Data Programming).

The course complements CSE143, which focuses more deeply on fundamental programming concepts and the internals of data structures. In contrast, CSE163 emphasizes the efficient use of those concepts for data programming.

In this course, students will learn:

1. More advanced programming concepts than in CSE142 or CSE160 including how to write bigger programs with multiple classes and modules.
1. How to work with different types of data: tabular, text, images, geo-spatial.
1. Ecosystem of data science tools including Jupyter Notebook and various data science libraries including scikit image, scikit learn, and Pandas data frames.
1. Basic concepts related to code complexity, efficiency of different types of data structures, and memory management.  This is class is designed as the second introductory programming course that focuses on writing programs that work with data. The prerequisites for the class require students having taken CSE 142 or CSE 160 and the class has been designed to be accessible to students from either of those backgrounds. Students that have taken 143 are welcome to take this class as it will serve as a complement to the material learned in 143 with only minor overlap.

Because this course will have students coming from many different class backgrounds, the first couple of weeks will be pretty different for students depending on what classes they have taken. Here is what we expect students to see in the first weeks based on their background:

- 142: The first two weeks might go pretty fast, but will be doable since you already know all the concepts (loops, conditionals, methods) and you are just learning all the new “words” in Python to use those concepts. This might require a little bit of extra practice early in the quarter so you are familiar with translating all the ideas you have learned in 142 to this new language. The first week has been designed to be a recap of all things 142 so you don’t also have to be learning a ton of new material while learning a new language in the first week.
 - 160: The first week will just be a bit of a review for you, but the class will start covering material you haven’t seen before starting in the second week.
- 143: You are in a similar boat as the 142 students, where you know a lot of the concepts but don’t know the Python language. You’ll probably see a few things that you saw in 143 in this class, but I think the new context of processing data in a new language will still keep it new, exciting, and challenging. 

CSE 163 Intermediate Data Programming

Computer Programming

Data Science

Control flow

Expressions

Dictionaries

Computer memory

Search algorithm

Decomposition

Python Control Flow

Graphics

List of Lists

Internet

Python Variables

Function (computer programming)

Graphical Animation

Nested Structures

Function Variables

CS 106A: Programming Methodologies Stanford University Spring 2023 This is an introductory course offering step-by-step instruction on key programming concepts with Python, including control flow, decomposition, data science, and more. Students are guided through exercises to learn by doing. ### How Does This Class Work?

- Start the very beginning
- Careful to go step by step, little exercises every day
- Like swimming, you can't just read about it
- Learn by doing
- "reps" - Nick's favorite word
- Technology to practice ideas right away
- In just a couple weeks .. amazed at what you can do
- Partly because computers are powerful
- Partly because coding is easier than you think  No prior programming experience required. There is one required text for this class, entitled Karel the Robot Learns Python. It's a short interactive tutorial that introduces the major concepts in programming in the context of an extremely simple robot world. This text is available for free on the class website. In addition to this text, we will also have reference material on Python available via the class webpage.

CS 106A: Programming Methodologies

Computer file

3 courses cover this concept

CS 186 Introduction to Database Systems

CSE 163 Intermediate Data Programming

CS 106A: Programming Methodologies