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DATA6

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DATA 6 - Introduction to Computational Thinking with Data Science and Society

Data Science Undergraduate Studies Undergraduate CDSS - Clg of Comp Data Sci & Society

Subject

DATA

Course Number

6

Course Level

Undergraduate

Formerly Known As

DATA C6/COMPSCI C6/STAT C6
COMPSCI C8R/STAT C8R

Course Title

Introduction to Computational Thinking with Data Science and Society

Course Description

This foundational Data Science course covers computational thinking as applied to the fundamentals of quantitative social inquiry. Apply critical concepts and skills in computer programming to conduct quantitative social science research in various contexts, including economic outcomes, public health, environmental justice, privacy, bioethics, and social networks. Understand the process of using data for quantitative analysis and how to develop a variety of figures, combined with text, to communicate their findings. The focus is on data exploration and identifying patterns relevant to social concepts, rather than inferences and predictions. The course can serve as a precursor to Data 8: Foundations of Data Science.

Minimum Units

4

Maximum Units

4

Grading Basis

Default Letter Grade; P/NP Option

Method of Assessment

Written Exam

Instructors

Yan, Hug, Harding

Breadth

Social and Behavioral Sciences

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 Restrictions.

Students will receive no credit for DATA 6 after completing DATA C8, DATA C88C, or COMPSCI 61A.

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

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Course Objectives

Data 6 takes advantage of the complementarity of computing and quantitative reasoning to enliven abstract ideas and build students’ confidence in their ability to solve real problems with quantitative tools. Students learn computer science concepts and immediately apply them to plot functions, visualize data, and simulate random events.

Foundations of Data Science (CS/Info/Stat C8, a.k.a. Data 8) is an increasingly popular class for entering students at Berkeley. Data 8 builds students’ computing skills in the first month of the semester, and students rely on these skills as the course progresses. For some students, particularly those with little prior exposure to computing, developing these skills benefits from further time and practice. Data 6 is a rapid introduction to Python programming, visualization, and data analysis, which will prepare students for success in Data 8. Data 6 also includes quantitative reasoning concepts that aren’t covered in Data 8. These include certain topics in: principles of data visualization; simulation of random processes; and understanding numerical functions through their graphs. This will help prepare students for computational and quantitative courses other than Data 8.

Student Learning Outcomes

Perform basic computations in Python, and be able to work with tabular data. Understand the syntactic structure of Python code. Use good practices in Python programming. Formulate questions about data and perform exploratory data analysis. Appreciate the interdisciplinary nature of data science. Create and use visualizations to understand univariate data and to identify associations or causal relationships in bivariate data. Run and understand basic probabilistic simulations.

Formats

Lecture, Discussion, Laboratory

Term

Fall and Spring

Weeks

15 weeks

Weeks

15

Lecture Hours

2

Lecture Hours Min

2

Lecture Hours Max

2

Lecture Mode of Instruction

In Person

Discussion Mode of Instruction

In Person

Laboratory Hours

2

Laboratory Hours Min

2

Laboratory Hours Max

2

Laboratory Mode of Instruction

In Person

Outside Work Hours

8

Outside Work Hours Min

8

Outside Work Hours Max

8

Term

Summer

Weeks

6 weeks

Weeks

6

Lecture Hours

6

Lecture Hours Min

6

Lecture Hours Max

6

Lecture Mode of Instruction

In Person, Online

Discussion Hours

2

Discussion Hours Min

2

Discussion Hours Max

2

Discussion Mode of Instruction

In Person, Online

Laboratory Hours

2

Laboratory Hours Min

2

Laboratory Hours Max

2

Laboratory Mode of Instruction

In Person, Online

Outside Work Hours

20

Outside Work Hours Min

20

Outside Work Hours Max

20