Data Analysis and Statistical Inference

          Module 1: Introduction to Data Analysis and Statistics

Section 1: Understanding Data Types
- Lesson 1: What Is Data?
  - Module 1, Section 1, Lesson 1: Exploring different kinds of data and their everyday presence.
- Lesson 2: Categories of Data
  - Module 1, Section 1, Lesson 2: Distinguishing between qualitative and quantitative data.

Section 2: The Role of Data in Everyday Life
- Lesson 1: Data in Daily Decision Making
  - Module 1, Section 2, Lesson 1: Recognizing how data supports everyday choices.
- Lesson 2: Identifying Reliable Data Sources
  - Module 1, Section 2, Lesson 2: Learning to evaluate the trustworthiness of various data sources.

Section 3: Introduction to Statistics
- Lesson 1: What Is Statistics?
  - Module 1, Section 3, Lesson 1: Defining statistics and understanding its significance.
- Lesson 2: Real World Examples of Statistics
  - Module 1, Section 3, Lesson 2: Looking at simple examples of statistics in real-life situations.

Section 4: Descriptive vs. Inferential Statistics
- Lesson 1: Descriptive Statistics Essentials
  - Module 1, Section 4, Lesson 1: Understanding how descriptive statistics summarize data.
- Lesson 2: Introductory Inferential Statistics
  - Module 1, Section 4, Lesson 2: Learning the basics of making inferences from data.

Section 5: The Importance of Data Analysis
- Lesson 1: How Data Analysis Informs Decisions
  - Module 1, Section 5, Lesson 1: Examining examples of data-driven decision making.
- Lesson 2: Analyzing Simple Datasets
  - Module 1, Section 5, Lesson 2: Practicing analysis on basic datasets.

Module 2: Data Collection and Sampling Techniques

Section 1: Introduction to Data Collection
- Lesson 1: Methods of Gathering Data
  - Module 2, Section 1, Lesson 1: Overview of surveys, observations, and experiments.
- Lesson 2: Designing a Data Collection Plan
  - Module 2, Section 1, Lesson 2: Creating simple plans for gathering data.

Section 2: Understanding Sampling
- Lesson 1: What Is a Sample?
  - Module 2, Section 2, Lesson 1: Defining samples and their role in data collection.
- Lesson 2: Benefits of Sampling
  - Module 2, Section 2, Lesson 2: Discussing why sampling is effective and efficient.

Section 3: Types of Sampling Methods
- Lesson 1: Random Sampling
  - Module 2, Section 3, Lesson 1: Learning the basics of random sampling techniques.
- Lesson 2: Non-Random Sampling Methods
  - Module 2, Section 3, Lesson 2: Understanding convenience and purposive sampling.

Section 4: Avoiding Sampling Bias
- Lesson 1: What Is Sampling Bias?
  - Module 2, Section 4, Lesson 1: Identifying bias and its effects on data integrity.
- Lesson 2: Strategies to Minimize Bias
  - Module 2, Section 4, Lesson 2: Best practices for reducing bias in sampling.

Section 5: Practical Data Collection
- Lesson 1: Collecting Data from Surveys
  - Module 2, Section 5, Lesson 1: Hands-on tips for survey design and implementation.
- Lesson 2: Organizing and Cleaning Data
  - Module 2, Section 5, Lesson 2: Basic data cleaning and organization for analysis.

Module 3: Descriptive Statistics and Data Visualization

Section 1: Measures of Central Tendency
- Lesson 1: Mean, Median, and Mode Basics
  - Module 3, Section 1, Lesson 1: Introduction to the concept of central tendency.
- Lesson 2: Calculating Central Tendency
  - Module 3, Section 1, Lesson 2: Simple techniques for computing mean, median, and mode.

Section 2: Measures of Spread
- Lesson 1: Range and Interquartile Range
  - Module 3, Section 2, Lesson 1: Understanding variability in data.
- Lesson 2: Standard Deviation and Variance
  - Module 3, Section 2, Lesson 2: Basic introduction to measuring spread.

Section 3: Data Visualization Fundamentals
- Lesson 1: Creating Bar Graphs and Pie Charts
  - Module 3, Section 3, Lesson 1: How to visually represent data with basic charts.
- Lesson 2: Interpreting Graphs and Charts
  - Module 3, Section 3, Lesson 2: Understanding what data visuals tell us.

Section 4: Organizing Data: Tables and Histograms
- Lesson 1: Constructing Data Tables
  - Module 3, Section 4, Lesson 1: Techniques to organize data in table formats.
- Lesson 2: Drawing and Reading Histograms
  - Module 3, Section 4, Lesson 2: Visualizing data distributions with histograms.

Section 5: Introduction to Software Tools
- Lesson 1: Basic Data Entry Tools (Spreadsheets)
  - Module 3, Section 5, Lesson 1: Using spreadsheet software for data entry and simple analysis.
- Lesson 2: Simple Data Visualization Software
  - Module 3, Section 5, Lesson 2: An overview of accessible tools for creating visuals.

Module 4: Foundations of Probability and Statistical Inference

Section 1: Basics of Probability
- Lesson 1: Understanding Chance and Uncertainty
  - Module 4, Section 1, Lesson 1: Introduce the concept of probability in everyday language.
- Lesson 2: Simple Probability Calculations
  - Module 4, Section 1, Lesson 2: Basic techniques for calculating probabilities.

Section 2: Sampling Distributions
- Lesson 1: Concept of a Sampling Distribution
  - Module 4, Section 2, Lesson 1: Learn what sampling distributions are and how they form.
- Lesson 2: Examples of Sampling Distributions
  - Module 4, Section 2, Lesson 2: Examining simple real-world cases.

Section 3: Introduction to Confidence Intervals
- Lesson 1: What Are Confidence Intervals?
  - Module 4, Section 3, Lesson 1: Defining and understanding confidence intervals at a basic level.
- Lesson 2: Calculating Simple Confidence Intervals
  - Module 4, Section 3, Lesson 2: Step-by-step introduction to forming confidence intervals.

Section 4: Hypothesis Testing Fundamentals
- Lesson 1: Understanding Hypotheses
  - Module 4, Section 4, Lesson 1: Introducing the basics of null and alternative hypotheses.
- Lesson 2: Simple Steps in Hypothesis Testing
  - Module 4, Section 4, Lesson 2: Overview of a basic hypothesis test process.

Section 5: Interpreting Statistical Findings
- Lesson 1: Making Decisions with Data
  - Module 4, Section 5, Lesson 1: How to draw meaningful conclusions from statistical results.
- Lesson 2: Common Misinterpretations
  - Module 4, Section 5, Lesson 2: Learning to avoid basic mistakes in interpreting data.

Module 5: Practical Applications and Data Interpretation

Section 1: Real-World Data Projects
- Lesson 1: Identifying a Data Project Topic
  - Module 5, Section 1, Lesson 1: How to choose a simple, real-world data project.
- Lesson 2: Planning a Data Analysis Project
  - Module 5, Section 1, Lesson 2: Outlining steps for a project from start to finish.

Section 2: Collecting and Preparing Project Data
- Lesson 1: Gathering Data for Your Project
  - Module 5, Section 2, Lesson 1: Practical tips for collecting data relevant to a chosen topic.
- Lesson 2: Data Cleaning and Preprocessing
  - Module 5, Section 2, Lesson 2: Simple methods for preparing data for analysis.

Section 3: Analyzing Project Data
- Lesson 1: Applying Descriptive Statistics to Projects
  - Module 5, Section 3, Lesson 1: Using statistical measures to summarize project data.
- Lesson 2: Visualizing Your Project Data
  - Module 5, Section 3, Lesson 2: Creating clear visuals to interpret project findings.

Section 4: Drawing Inferences from Projects
- Lesson 1: Introduction to Inference in Projects
  - Module 5, Section 4, Lesson 1: How to make basic inferences from collected data.
- Lesson 2: Reporting Your Findings
  - Module 5, Section 4, Lesson 2: Learning how to communicate conclusions effectively.

Section 5: Ethical Data Practices and Future Exploration
- Lesson 1: Understanding Ethical Considerations
  - Module 5, Section 5, Lesson 1: Basic ethical guidelines for handling and analyzing data.
- Lesson 2: Exploring Future Opportunities in Data Analysis
  - Module 5, Section 5, Lesson 2: Looking ahead at how data analysis skills can be used in various fields.