Databases
(1) Researching with databases
Video - Nutrition - Reducing the threat of osteoporosis [9:28]
Task - Many physicians recommend their patients substitute polyunsaturated for saturated fats (increase unsaturated:saturated ratio), reduce sodium intake, and minimize cholesterol. Use the nutrition database to determine foods that should be minimized or eliminated to reduce the risk of cardiovascular disease. Summarize your findings and include a screen shot to support your claim.
(2) Create a database
Video - Database basics [13:42]
Task - Create a database of information relevant to your teaching. Your database should contain a minimum of 10 records (rows) and 5 fields (columns) and illustrate the features discussed in the video.
(3) Mapping Data
Video -MyMaps - Mapping data [earthquakes & volcanoes] [6:56]
Video -Importing MyMaps data to Google Earth [earthquakes & volcanoes] [8:37]
Video -My Maps - Mapping data [schools] [8:46]
Video -Importing KML files to -Google Earth [6:34]
Task - Create a multi-layered MyMaps map of geographic data using data such as the schools data or volcanoes & earthquakes data shown in the video, OR a Google Earth map using kml/kmz files. Provide photo and text annotations on your map.
(4) Teaching with Databases
Video - Gapminder - [11:07] investigate trends in world data
Video - Wolfram Alpha - [4:08] Compare and contrast data using a computational knowledge engine
Video - pTable [8:48] Dynamic periodic table database
Task -
Select one or more online databases of your choice, or database apps such as GapMinder (world statistics, country comparisons ), Wolframalpha (computational knowledge engine), or pTable (periodic table of the elements).
Write ten (10) engaging questions which require students to analyze data and create models or explanations.
Provide sample answers to your questions.
(5) Data Science
Task - The following is a list of the salient features of data science. Provide a specific example of each of the following activities of data science
Data Collection: Data scientists collect data from various sources, which can include databases, sensors, web scraping, surveys, and more. This data can be in structured formats (e.g., databases) or unstructured formats (e.g., text documents).
Data Cleaning: Raw data often contains errors, missing values, and inconsistencies. Data scientists clean and preprocess the data to ensure its quality and reliability.
Data Exploration: Data exploration involves exploring the data to understand its characteristics, distributions, and relationships. This step helps in identifying patterns and potential insights.
Data Analysis: Data scientists use statistical and computational techniques to analyze data and uncover meaningful insights. This can involve descriptive statistics, hypothesis testing, and more advanced methods like machine learning.
Machine Learning: Machine learning is a subset of data science that focuses on developing algorithms and models that can learn from data and make predictions or decisions. This is used for tasks like predictive analytics, classification, clustering, and recommendation systems.
Data Visualization: Data scientists create visual representations of data to help stakeholders understand and interpret complex information. Data visualization tools like charts, graphs, and dashboards are commonly used.
Model Deployment: When a machine learning model or analysis is complete, data scientists deploy it into production systems so that it can be used to make real-time decisions or generate predictions.
Continuous Learning and Improvement: Data science is an evolving field, and data scientists must stay updated with the latest tools, techniques, and best practices to continue improving their models and analyses.