High School Data Analytics and Database Design
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License Model
FlexPoint or School/District Hosted
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Number of Credits
1.0
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Estimated Completion Time
32-36 weeks
Suggested Prerequisites
Foundations of Programming, Procedural ProgrammingDescription
Did you know that data can tell a story? In this course, you will explore data and databases through the fictional pet services company Critter Sitters. By examining real-world examples, you will learn how data can tell a story and gain the skills to analyze and make decisions from it. You will also expand and apply your Python skills to data science principles, from data dashboards to statistical analysis. Additionally, you will learn about data management, including using and storing quality data and how to spot biases within it. You will uncover how to create a database, write queries, and explore strategies for securing databases. By the end of this course, you will have a foundational understanding of data and databases and the exciting career opportunities available in the data science field.Module One: Storytelling
-Data science
-Storytelling with data
-Data representations
-Decoding data
-Time series data
-Drawing conclusions and making predictions using data
-Data biases
-Categorizing data
-Using statistics
-Google Colaboratory
-Python packages
-Creating tables with the Pandas package
-Reading csv/Excel files with Pandas
-Data processing cycle
-Types of data
-Collecting and organizing data
-Online datasets
-Interpreting data
Module Two: Visualizing
-Visual cues
-Simple and complex charts
-Shaping data
-Chart types
-Chart elements
-Chart junk
-Planning a presentation
-Designing a presentation
-Considering the audience
-Coding multi-dimensional data
-Strengths and weaknesses of data dashboards
-Cleaning data
-Planning a dashboard
-Slicing and dicing data
-Interactive dashboards
-Arranging dashboards
-Visual noise
Module Three: Analyzing
-Permutations
-Combinations
-Percentiles
-Conditional probabilities
-Probability trees
-Independent and dependent events
-Bayes theorem
-Discrete and continuous probabilities
-Binomial probabilities
-Mean, median, and mode
-Ranges and variances
-Normal distribution
-Z-score
-Skewed data distributions
-Statistical sampling
-Standard error
-Confidence intervals
-Hypothesis testing
-Null and alternative hypotheses
-Type I and Type II errors
-Significance test
-Sampling bias
-Cognitive bias
Module Four: Extracting
-Computers and storing/accessing data
-Everyday use of databases
-Types of databases
-SQL
-SQLite and Python
-Queries
-SELECT/SELECT DISTINCT clause
-WHERE clause
-Logical operators
-Formatting dates
-Date functions
-Filtering
-String functions
-Sorting data
-Grouping data
-Aggregating data
-Primary and foreign keys
-Relational databases
-Joining data
-Combining queries
-Subqueries
Module Five: Designing
-Atomicity, Consistency, Isolation, and Durability
-Designing a database steps
-Entities in a database
-Attributes of entities
-Primary key usage and rules
-Foreign key usage
-Table relationships
-Referential integrity
-Normalization
-First, Second, and Third Normal Form
-Denormalization
-Common data sources
-Creating a database/tables
-Adding table relationships
-Loading data into the database
-Testing the database
Module Six: Securing
-Identity protection
-Personal Identifiable Information
-Privacy laws
-Parts of the data processing ecosystem
-Common data processing issues
-Different types of cyber-attacks
-Mitigating cyber-attacks
-Data breaches
-Keeping PII safe during data breaches
-Data life cycle phases
-Data governance
-Data inventory process
-Risk assessment for data processing