proofreading edits

script/codegraf/ees_project/index.md

@@ -24,25 +24,28 @@ The learner will:
 ## Criteria
 
 1\.	Data Acquisition (20 points)
-•	Exemplary (20-18 points): Student successfully acquires data from a tabular data source, loading it into a pandas DataFrame. The process is well-documented and error-handling is implemented effectively.
-•	Proficient (17-14 points): Student is able to acquire data and load it into a pandas DataFrame, with minor errors in documentation or handling.
-•	Developing (13-10 points): Student encounters some difficulties in acquiring and loading the data, with notable errors in documentation or handling.
-•	Needs Improvement (9-0 points): Student struggles significantly with acquiring and loading data, with inadequate documentation or han- dling of errors.
+- Exemplary (20-18 points): Student successfully acquires data from a tabular data source, loading it into a pandas DataFrame. The process is well-documented and error-handling is implemented effectively.
+- Proficient (17-14 points): Student is able to acquire data and load it into a pandas DataFrame, with minor errors in documentation or handling.
+- Developing (13-10 points): Student encounters some difficulties in acquiring and loading the data, with notable errors in documentation or handling.
+- Needs Improvement (9-0 points): Student struggles significantly with acquiring and loading data, with inadequate documentation or handling of errors.
+
 2\.	Data Wrangling (30 points)
-•	Exemplary (30-27 points): Student extracts necessary data from the DataFrame and successfully wrangles it into a usable form for anal- ysis. Data manipulation techniques are effectively applied.
-•	Proficient (26-21 points): Student extracts most of the necessary data and conducts basic data wrangling, with some inconsistencies or minor errors.
-•	Developing (20-15 points): Student struggles with extracting necessary data or encounters difficulties in wrangling the data into a usable form.
-•	Needs Improvement (14-0 points): Student demonstrates limited abil- ity to extract necessary data or perform data wrangling.
+- Exemplary (30-27 points): Student extracts necessary data from the DataFrame and successfully wrangles it into a usable form for analysis. Data manipulation techniques are effectively applied.
+- Proficient (26-21 points): Student extracts most of the necessary data and conducts basic data wrangling, with some inconsistencies or minor errors.
+- Developing (20-15 points): Student struggles with extracting necessary data or encounters difficulties in wrangling the data into a usable form.
+- Needs Improvement (14-0 points): Student demonstrates limited ability to extract necessary data or perform data wrangling.
+
 3\.	Python Proficiency (30 points)
-•	Exemplary (30-27 points): Student demonstrates proficient use of ba- sic Python statements (if, for, assignment), effectively utilizes func- tions from modules, and applies methods to objects.
-•	Proficient (26-21 points): Student applies basic Python statements adequately but may demonstrate some inconsistencies or errors.
-•	Developing (20-15 points): Student struggles with basic Python state- ments and may have difficulty applying functions or methods correctly.
-•	Needs Improvement (14-0 points): Student demonstrates limited understanding or application of basic Python statements.
+- Exemplary (30-27 points): Student demonstrates proficient use of ba- sic Python statements (if, for, assignment), effectively utilizes functions from modules, and applies methods to objects.
+- Proficient (26-21 points): Student applies basic Python statements adequately but may demonstrate some inconsistencies or errors.
+- Developing (20-15 points): Student struggles with basic Python state- ments and may have difficulty applying functions or methods correctly.
+- Needs Improvement (14-0 points): Student demonstrates limited understanding or application of basic Python statements.
+
 4\.	Data Visualization (20 points)
-•	Exemplary (20-18 points): Student creates multiple clear and informative visualizations using matplotlib.pyplot that effectively repre- sent the analyzed data.
-•	Proficient (17-14 points): Student creates a visualization that represents the data adequately, but may have minor inconsistencies or lack some clarity.
-•	Developing (13-10 points): Student attempts to create a visualization but encounters difficulties in clarity or effectiveness.
-•	Needs Improvement (9-0 points): Student demonstrates limited ability to create a visualization or fails to do so.
+- Exemplary (20-18 points): Student creates multiple clear and informative visualizations using matplotlib.pyplot that effectively represent the analyzed data.
+- Proficient (17-14 points): Student creates a visualization that represents the data adequately, but may have minor inconsistencies or lack some clarity.
+- Developing (13-10 points): Student attempts to create a visualization but encounters difficulties in clarity or effectiveness.
+- Needs Improvement (9-0 points): Student demonstrates limited ability to create a visualization or fails to do so.
 
 Total: 100 points