Unit Title: Introduction to Data Science and Analytics

Unit Overview: This unit aims to provide students with a comprehensive understanding of data analytics and its importance in various fields. Students will learn about the data analysis process, different types of data, levels of measurement, data collection methods, and sampling methods. Through hands-on activities and real-world examples, students will develop the necessary skills to collect, analyze, and interpret data effectively.

Unit Duration: 4 weeks

Learning Objectives: 1. Understand the importance of data analytics in decision-making processes. 2. Identify and differentiate between different types of data. 3. Explain the levels of measurement and their implications in data analysis. 4. Demonstrate knowledge of various data collection methods. 5. Apply appropriate sampling methods to collect representative data.

Assessment: 1. Class participation and engagement in discussions and activities - 20% 2. Individual or group projects on data analysis process and types of data - 30% 3. Written exam on levels of measurement, data collection methods, and sampling methods - 50%

Week 1: Introduction to Data Analytics and the Data Analysis Process - Importance of data analytics in various fields - Overview of the data analysis process - Steps involved in data analysis - Introduction to data visualization techniques

Week 2: Types of Data and Levels of Measurement - Categorical data vs. numerical data - Discrete data vs. continuous data - Nominal, ordinal, interval, and ratio scales of measurement - Implications of levels of measurement in data analysis

Week 3: Data Collection Methods - Primary data collection methods (surveys, interviews, observations) - Secondary data collection methods (existing databases, literature review) - Pros and cons of different data collection methods - Ethical considerations in data collection

Week 4: Sampling Methods - Simple random sampling - Stratified sampling - Cluster sampling - Systematic sampling - Non-probability sampling methods (convenience sampling, purposive sampling) - Determining sample size and its significance

Teaching Strategies: 1. Lectures and presentations to introduce and explain concepts 2. Group discussions and case studies to encourage critical thinking and application of knowledge 3. Hands-on activities and exercises to practice data collection and sampling methods 4. Real-world examples and case studies to demonstrate the relevance of data analytics in different fields 5. Guest speakers from industry or academia to share their experiences and insights in data analytics

Resources: 1. Textbook: “Data Science for Beginners” by John Doe 2. Online articles and research papers on data analytics and data collection methods 3. Data analysis software (e.g., Excel, R, Python) for hands-on activities and projects 4. Real-world datasets for practice and analysis

Note: The unit plan provided is a general outline and can be modified or expanded based on the specific needs and requirements of the course and students.