Statistics for Computer Science Basics

Course Overview

This course introduces students to the fundamental concepts of statistics, with a focus on applications in business, computer science, and data analysis. Students learn to summarize and graphically represent data, analyze measures of central tendency, and interpret relationships between variables.

• Course Materials: Berenson / Levine / Szabat / Stephan: Basic Business Statistics, 14th Global Edition; MyLabStat online platform.

• Assessment: Homework, project presentation, midterm, final exam, and in-class activity.

Professional Competencies

• Data & Variables: Define types/scales of data, plan and assess data collection and quality.

• Visualization: Organize, summarize, and present data using tables, charts, and diagrams.

• Descriptive Measures: Compute and interpret summary statistics (mean, median, mode, etc.).

• Probability & Distributions: Apply probability rules; use and understand discrete and continuous models.

• Sampling & Inference: Understand sampling, confidence intervals, and hypothesis testing.

• Information & Decision: Interpret statistical results and make data-driven decisions.

Methodological Competencies

• Build clear, correctly labeled tables and charts in Excel/LibreOffice.

• Select meaningful data (variables, record consistently) and interpret results in context.

• Summarize and visualize data using Python.

Personal/Social Competencies

• Communicate statistical results clearly and tailored to the audience (purpose, technical depth, and format).

Course Structure & Weekly Topics

The course is organized into weekly modules, each focusing on a key area of statistics relevant to business and computer science.

Key Statistical Concepts

Types of Data

Understanding the types of data is essential for selecting appropriate statistical methods.

• Qualitative (Categorical) Data: Data that can be grouped by categories (e.g., gender, color).

• Quantitative (Numerical) Data: Data that can be measured numerically (e.g., height, income).

• Scales of Measurement: Nominal, ordinal, interval, and ratio scales.

Data Collection and Cleaning

Accurate data collection and cleaning are crucial for reliable analysis.

• Data Collection: Methods include surveys, experiments, and observational studies.

• Data Cleaning: Removing errors, handling missing values, and ensuring consistency.

Visualizing Data

Data visualization helps in understanding patterns and relationships.

• Charts and Graphs: Bar charts, histograms, pie charts, scatter plots.

• Tables: Organize data for clarity and comparison.

Measures of Central Tendency

Central tendency describes the center of a data set.

• Mean: The average value.

• Median: The middle value when data are ordered.

• Mode: The most frequently occurring value.

Measures of Variation

Variation measures the spread of data.

• Range: Difference between the highest and lowest values.

• Variance: Average squared deviation from the mean.

• Standard Deviation: Square root of variance.

Quartiles, Covariance, and Correlation

These measures help describe data distribution and relationships between variables.

• Quartiles: Divide data into four equal parts.

• Covariance: Measures how two variables change together.

• Correlation: Standardized measure of relationship strength.

Probability

Probability quantifies the likelihood of events.

• Basic Rules: Addition and multiplication rules.

• Conditional Probability: Probability of event A given event B.

Discrete and Continuous Probability Distributions

Probability distributions describe how probabilities are distributed over values.

• Discrete Distributions: Binomial, Poisson, etc.

• Continuous Distributions: Normal, exponential, etc.

Sampling and Estimation

Sampling allows inference about populations from samples.

• Sampling Methods: Simple random, stratified, cluster sampling.

• Estimation: Point and interval estimates of population parameters.

Confidence Intervals

Confidence intervals provide a range of values for population parameters.

• Formula for Confidence Interval (mean):

Hypothesis Testing

Hypothesis testing is used to make decisions about population parameters.

• Steps: State hypotheses, select significance level, compute test statistic, make decision.

• Test Statistic Example (z-test):

Communicating Results

Effective communication of statistical findings is essential for decision-making.

• Tailor reports: Adjust technical depth and format for the audience.

• Use clear visuals: Support findings with tables and charts.

Additional info: The course emphasizes practical skills in Excel/LibreOffice and Python for data analysis, and includes project work and presentations to develop communication skills.