Welcome to STATS 6 - Introduction to Data Science
This course introduces the full data cycle. Topics include data collection and retrieval, data cleaning, exploratory analysis and visualization, introduction to statistical modeling and inference, and communicating findings. Applications include real data from wide-range of fields following reproducible practices.
Prerequisite: None
This course introduces the full data cycle. Topics include data collection and retrieval, data cleaning, exploratory analysis and visualization, introduction to statistical modeling and inference, and communicating findings. Applications include real data from wide-range of fields following reproducible practices.
Prerequisite: None
Course Goals
By the end of this course you will be able to:
By the end of this course you will be able to:
- explore data using descriptive statistics and visualizations;
- read, write, and tidy up datasets;
- make predictions and conclusions using models;
- consider impact of decisions related to data on humans, other livings, and the planet;
- write human- and machine-readable code using R.
Typical Week Workflow
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Monday 9 am
Prof. Dogucu holds office hours.
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Monday 2 pm
Weekly homework due
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Monday 2 - 3:30 pm
Lecture
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Tuesday 4:00 - 4:30 pm
Dr. Dogucu holds office hours
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Tuesday 11:59 pm
Review quizzes are due
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Wednesday 2 - 3:30 pm
Lecture
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Thursday 1:00 - 2: 00 pm
Dr. Dogucu holds office hours
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Thursday 11:59 pm
Review quizzes are due
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Friday 4:00 - 5:00 pm
Prof. Dogucu holds office hours.
Important Dates
Final Project Proposal due Nov 5th at 11:59 pm
Final Project Presentation on Dec 10th at 1:30 - 3:30 pm
Part of this work is supported by NSF HDR DSC award #2123366