Statistics for Information Science
This course introduces foundational statistical concepts for information science, including data organization, descriptive and inferential statistics, and practical data analysis using Excel.
Instructor: Dipto Das
Term: Spring
Location: Muenzinger Psyc & Biopsych E064
Time: TTh 8am-9:15am
Course Overview
This course introduces statistical thinking and data analysis for information science. Students will:
- Understand different types of data and how they are collected
- Use Microsoft Excel for data analysis and visualization
- Apply descriptive and inferential statistical methods
- Recognize bias and limitations in data and analysis
- Interpret and communicate statistical findings clearly
Prerequisites
- Basic algebra
- Familiarity with spreadsheets is helpful but not required
Textbooks
- OpenIntro Statistics (4th Edition) by Diez, Barr, & Çetinkaya-Rundel
- Supplementary readings provided via Canvas
Grading
- Homework: 40%
- Exams (Midterm 1, Midterm 2, Final): 40%
- Quizzes: 10%
- Attendance & Participation: 10%
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Course Introduction & Data Concepts Overview of the course, syllabus, and introduction to data types and statistical thinking. | ||
| 2 | Data Collection and Organization Understanding data sources, sampling, and organizing datasets. | ||
| 3 | Descriptive Statistics Summarizing data using measures of central tendency and variability. | ||
| 4 | Data Visualization Creating and interpreting visual representations of data in Excel. | ||
| 5 | Statistical Bias and Error Understanding bias, noise, and limitations in data collection and analysis. | ||
| 6 | Midterm 1 In-class midterm exam covering descriptive statistics and data analysis. | ||
| 7 | Probability and Distributions Introduction to probability concepts and distributions. | ||
| 8 | Statistical Inference Confidence intervals and hypothesis testing fundamentals. | ||
| 9 | Relationships Between Variables Exploring relationships using correlation and comparisons. | ||
| 10 | Advanced Inference Deeper exploration of hypothesis testing and interpretation. | ||
| 11 | Spring Break No classes. | ||
| 12 | Applied Statistical Analysis Applying inference techniques to real-world datasets. | ||
| 13 | Midterm 2 In-class exam covering inferential statistics. | ||
| 14 | Regression Analysis Introduction to regression and predictive modeling. | ||
| 15 | Advanced Regression and Review Interpreting regression results and course review. | ||
| 16 | Final Exam Comprehensive final exam. |