### Day 60 - Code Combat - 11.10.15

 Unit 3 - Raining Reigning DataLesson 3.1 Visualizing DataThe goal of this lesson is for you to be able to learn from and communicate with large sets of data. You will handle some relevant data: the Census reports family and money data for your community; trends with your name are public record. But anything can be datafied! Computing has automated data collection. Aim the magic wand, and anything you love in life can produce terabytes to be analyzed. The impact on the workplace is profound; anything that was worth doing before is now worth examining under the lens of Big Data, and data scientists are sought from every corner of the job market.The collection of Big Data does pose societal concerns for some. Once collected, data might live forever. How do you want your data treated?We'll use Python to make useful graphic representations of data. We'll work from familiar ways of showing data to more unusual and creative ways of communicating information in pictures.Essential Questions:Q1 - How will computation impact fields other than computing itself?Q2 - How will computation impact society?Q3 - How can patterns be discovered in data?3.1.1 Time Series And TrendsActivity3.1.2 Issues with DataActivity3.1.3 Big Data and Parallel Processing3.1.4 Pie Charts and Bar ChartsActivity3.1.5 Histograms and DistributionsProjectLesson 3.2 Discovering Knowledge from DataAs in the previous lesson, the goal of this lesson is for students to be able to create a range of visualizations to analyze complex sets of large data and to meaningfully interpret the patterns they uncover. Students use statistics to deepen the meaning of knowledge gained by visualization. The hooks are again conclusions they can draw about themselves from relevant data, including various geographic perspectives on their life and facial recognition of their own features. The lesson uses Excel as well as Python to manipulate and visualize data. Students examine multidimensional data sets using scatter plot arrays and view geographic and social data using heat maps and directed graphs. Students experiment with object recognition and face recognition. They are challenged to discover clustering and linear correlation patterns lurking in data sets distributed across student computers and school sites, such that data cleaning and warehousing are necessary. Finally, student teams choose a question and answer it using large data.Essential QuestionsQ1 - How will computation impact fields other than computing itself?Q2 - How will computation impact society?Q3 - How can patterns be discovered in data?3.2.1 Inferential StatisticsActivity3.2.2 Image DataActivity3.2.3 Linked DataActivity3.2.4 Geographic DataActivityCode Combat3.2.7 Investigate with DataActivity UpdateN/AHelp Request List