Data Science
Syllabus
Course Essential Questions
- How do we collect and evaluate data to ensure validity?
- How do we apply programming and mathematical concepts to describe, analyze and model relationships within data?
- How do we accurately visualize data to best represent a given dataset?
- What ethical considerations and standards should we adhere to when conducting research, and how can we ensure the integrity of data analysis methods and findings?
Course Learning Objectives
- Create valid datasets by gathering unbiased data using surveys.
- Write computer code to visualize data, choosing the appropriate type of graph and calculations, to accurately demonstrate the relationship among variables.
- Construct linear and non-linear models to describe and predict relationships within a dataset, demonstrating proficiency in translating real-world problems into mathematical models.
- Conduct a thorough examination of the social and ethical implications of data science work, critically evaluating data integrity, method validity, and ethical considerations, and apply this knowledge to several data analysis case studies.
Unit 1: Introduction to Data Science (Weeks 1-4)
Unit Level Essential Questions
- How does programming for data analysis influence various aspects of our lives, and what are the implications for things we hold dear or care about?
- How do we describe and display simple data?
- What are the basic programming functions to perform operations on and display data?
- What is the data cycle and how is it used?
Week 1: What is Data Science?
Weekly Learning Objectives
- Apply the rules and policies of the course, and expectations of you as a contributing member of our learning community.
- Build community by sharing important information with your teacher and classmates about yourself and your school.
- Describe the roles and diversity of professionals in mathematics, computing, and data science by identifying and explaining the contributions of key figures in these fields.
Week 2: Types of Data
Weekly Learning Objectives
- Identify examples of the following data types: numbers, strings, and Booleans.
- Differentiate between categorical or quantitative variables.
- Discuss what can be inferred from a visualization.
Week 3: Displaying Simple Data
Weekly Learning Objectives
- Use contracts to apply functions that produce numbers, strings, and images.
- Identify domain and range and describe how they relate to functions.
- Compare and contrast the information displayed in pie charts and bar charts.
- Write code to display appropriate visualizations for data sets using pie or bar charts.
- Discuss what can be inferred from a visualization.
Week 4: The Data Cycle
Weekly Learning Objectives
- Explain the phases of the data cycle.
- Describe the different types of statistical questions and provide examples of each.
- Determine how to take random samples from a population to draw conclusions about a larger population.
Unit 2: The Distribution of Data (Weeks 5-7)
Unit Level Essential Questions
- How can we evaluate and display the distribution of data?
- How can we describe the distribution of data using histograms and box plots?
- Discuss what can be inferred from a visualization.
Week 5: Histograms
Weekly Learning Objectives
- Create smaller subsets of data from a larger dataset.
- Compare and contrast the information displayed in bar charts and histograms.
- Write code to visualize data appropriately for given data sets, using histograms
- Discuss what can be inferred from a visualization.
Week 6: The Shape of Data
Weekly Learning Objectives
- Interpret how the shape of the graph describes the distribution of data.
- Analyze a dataset to describe the center of a distribution including mean, median and mode.
- Write code to calculate mean, median and mode for a given dataset.
- Discuss what can be inferred from a visualization.
Week 7: The Spread of Data
Weekly Learning Objectives
- Compare and contrast the information displayed in a box plot and a histogram.
- Write code to visualize data appropriately for given data sets, using box plots.
- Measure and display the spread of a dataset using standard deviation.
- Write code to calculate standard deviation for a given dataset.
- Discuss what can be inferred from a visualization.
Unit 3: Modeling the Data (Weeks 8-11)
Unit Level Essential Questions
- How can we describe the relationship between two variables?
- How can functions reduce the complexity of a problem?
- In what ways can we filter data to answer a question?
Week 8: Correlation
Weekly Learning Objectives
- Describe the relationship between explanatory variables and response variables.
- Explain how the correlation between two variables can be measured and described.
- Write code to visualize data appropriately for given data sets, using scatter plots.
- Explain how the correlation between two variables can be measured.
- Interpret the correlation, in terms of direction and strength, between two variables using a scatter plot.
- Discuss what can be inferred from a visualization.
Week 9: Linear Regression
Weekly Learning Objectives
- Explain how linear regression describes the strength of relationship between two variables.
- Write code to quantify the strength of the relationship between two variables for a given data set, using linear regression.
- Explain how subsets of data can be extracted from larger data sets.
- Write code to extract individual rows from a table and columns from a row.
- Discuss what can be inferred from a visualization.
Week 10: Programming Functions
Weekly Learning Objectives
- Write code to define functions that consume rows and produce more meaningful images.
- Write code to use helper functions to define custom pie and bar-charts, histograms, and scatter plots.
- Filter tables according to different criteria, using helper functions.
- Discuss what can be inferred from a visualization.
- Implement the phases of the data cycle to address a research question.
Week 11: Advanced Displays
Weekly Learning Objectives
- Write code to define functions that consume rows and produce more meaningful images.
- Write code to use helper functions to define custom pie and bar-charts, histograms, and scatter plots.
- Filter tables according to different criteria, using helper functions.
Unit 4: Using Data Responsibly (Weeks 12-15)
Unit Level Essential Questions
- How do we write computer code that is easy for others to understand and debug?
- What are the best practices to collect reliable and unbiased data?
- How do we evaluate and clean data sets for accurate analysis?
- What ethical practices should data scientists abide by?
Week 12: Investigating Real World Problems
Weekly Learning Objectives
- Document functions and code in a way that others can understand.
- Write table operations to create more sophisticated analyses.
- Discuss what can be inferred from a visualization.
- Implement the phases of the data cycle to address a research question.
Week 13: Solving Word Problems
Weekly Learning Objectives
- Write table functions that consume a row and produce a column, image, or Boolean.
- Construct grouped samples from a population to address a question.
- Identify how different data-hygiene problems disrupt accurate analysis of data.
- Describe ethical and privacy considerations in data science.
- Implement the phases of the data cycle to address a research question.
Week 14: Ethical Considerations
Weekly Learning Objectives
- Describe ethical and privacy considerations in data science.
- Describe the four categories of threats to validity and recognize each when evaluating claims.
- Explain the most important considerations to ensure valid conclusions.
- Implement the phases of the data cycle to address a research question.
Week 15: Drawing Valid Conclusions
Weekly Learning Objectives
- Describe the four categories of threats to validity and recognize each when evaluating claims.
- Explain the most important considerations to ensure valid conclusions.
- Implement the phases of the data cycle to address a research question.