In Assignment Part 1, you will required to apply appropriate data management and data visualization techniques for a given scenario to create charts.  The techniques required to complete this assignment are covered in Module 1 to 4 of the subject. You will have to explain what conclusions you draw from the charts.

Scenario

The California Cooperative Oceanic Fisheries Investigations (CalCOFI) was formed in 1949 to study the ecological aspects of the sardine population collapse off California. CalCOFI conducts quarterly cruises off southern & central California, collecting a suite of hydrographic and biological data on station and underway. The CalCOFI data set represents the longest (1949 present) and most complete (more than 50,000 sampling stations) time series of oceanographic in the world.

The physical, chemical, and biological data collected at regular time and space intervals quickly became valuable for documenting climatic cycles in the California Current and a range of biological responses to them. Data collected at depths down to 500 m include: temperature, salinity, oxygen, phosphate, silicate, nitrate and nitrite, chlorophyll, transmissometer, PAR and C14 primary productivity.

You are provided with the following:-

1.    bottle.csv

2.    CalCOFI Database Tables Description - Bottle Table.pdf

The following website provides relevant information and documents about the CalCOFI project and data source.

https://calcofi.org/data/oceanographic-data/bottle-database/

Using the dataset and codebook provided, apply appropriate data management techniques. For this assessment, complete the following tasks.

1.    Select one quantitative variable from the dataset to draw a histogram. What is conclusion can you draw from the histogram?

2.    Select one categorical variable (as the explanatory variable) and one quantitative variable (as the   response variable) from the dataset to draw a box plot. What is conclusion can you draw from the box plot?

Note: Instead of selecting an existing categorical variable, you can generate the categorical variable by transforming (grouping) the original quantitative variable into a categorical variable.

3.    Select one quantitative variable from the dataset to draw a line chart. For this, you need to select one other variable as the corresponding explanatory variable. What is conclusion can you draw from the line chart?

4.    Select three quantitative variables from the dataset to draw a bubble chart. What is conclusion can you draw from the bubble chart?

5.    Go to the link below. Go through the different charts and the corresponding code provided there.

Top 50 matplotlib Visualizations – The Master Plots (with full python code)

https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots- python/

Then select one chart/plot from the 50 available and appropriate variable(s) from the dataset

provided (bottle.cvs). Then create the selected chart using/modifying the corresponding code

provided by the website for the variable data you selected from the dataset (bottle.cvs).  What is conclusion can you draw from the chart you have created?

(Note: you are required to select variable(s) which are different from what you select for previous   tasks (1-4). If you still want to select a variable which you’ve already selected for any previous task, you can do but you are required to manipulate the original data in different ways e.g. different

subsampling (filtering) or grouping.

Hint: Refer to Modules 1, 2, 3, and 4 and Practicals 1, 2, 3, and 4 for help on data management and data visualization

Ensure you complete, zip and submit both the ‘Assignment- Part 1 -FirstNameLastName.docx’ and ‘Assignment- Part1-FirstNameLastName.ipynb’ files to LearnJCU. Ensure you add your

FirstName and LastName inside the files and to the file names.

Assignment – Part 1 (15%) Marking Criteria (Rubric) – Total Raw Marks: 100 – 20 full marks for each task.

For each task (20 full marks), the following marking criteria is applied.