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DATASCI200

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DATASCI 200 - Introduction to Data Science Programming

School of Information Graduate INFO - School of Information

Subject

DATASCI

Course Number

200

Department

School of Information

Course Level

Graduate

Course Title

Introduction to Data Science Programming

Course Description

This fast-paced course gives students fundamental Python knowledge necessary for advanced work in data science. Students gain frequent practice writing code, building to advanced skills focused on data science applications. We introduce a range of Python objects and control structures, then build on these with classes on object-oriented programming. A major programming project reinforces these concepts, giving students insight into how a large piece of software is built and experience managing a full-cycle development project. The last section covers two popular Python packages for data analysis, Numpy and Pandas, and includes an exploratory data analysis.

Minimum Units

3

Maximum Units

3

Grading Basis

Default Letter Grade; S/U Option

Prerequisites

MIDS students only.

Repeat Rules

Course is not repeatable for credit.

Credit Restriction Courses. Students will receive no credit for this course if following the course(s) have already been completed.

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Credit Replacement Courses. Upon passing, students can use the following course(s) to replace a deficient grade for this course.

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Student Learning Outcomes

Be able to navigate a file system, manipulate files, and execute programs using a command line interface. Understand how to manage different versions of a project using Git and how to collaborate with others using Github. Be fluent in Python syntax and familiar with foundational Python object types. Be able to design, reason about, and implement algorithms for solving computational problems. Understand the principles of object-oriented design and the process by which large pieces of software are developed. Be able to test and effectively debug programs. Know how to use Python to extract data from different type of files and other sources. Understand the principles of functional programming. Know how to read, manipulate, describe, and visualize data using the Numpy and Pandas packages. Be able to generate an exploratory analysis of a data set using Python. Be prepared for further programming challenges in more advanced data science courses.

Formats

Lecture

Term

Summer

Weeks

Other

Weeks

14

Lecture Hours

3

Lecture Hours Min

3

Lecture Hours Max

3

Lecture Mode of Instruction

Online

Outside Work Hours

7

Outside Work Hours Min

7

Outside Work Hours Max

7

Term

Fall and Spring

Weeks

Other

Weeks

14

Lecture Hours

3

Lecture Hours Min

3

Lecture Hours Max

3

Lecture Mode of Instruction

Online

Outside Work Hours

7

Outside Work Hours Min

7

Outside Work Hours Max

7