Merge pull request #177 from LibraryCarpentry/learning_obj

Refine learning objectives in index.md

Author: jt14den

index.md

@@ -28,15 +28,7 @@ This lesson is an introduction to programming in Python for library and informat
 
 After attending this training, participants will be able to:
 
-- Navigate the JupyterLab interface and run Python cells within a notebook.
-- Assign values to variables, identify data types, and display values in a Jupyter Notebook.
-- Create and manipulate lists in Python, including indexing, slicing, appending, and removing items to manage data collections effectively.
-- Call built-in Python functions, and use the help function to understand their usage and troubleshoot errors.
-- Use Python libraries like Pandas to import modules, load tabular data from CSV files, and perform basic data analysis.
-- Apply 'for' loops to iterate over collections, using the accumulator pattern to aggregate values and trace variable states to predict loop outcomes.
-- Manipulate pandas DataFrames to select data, calculate summary statistics, sort data, and save results in various formats, demonstrating basic data handling and analysis proficiency.
-- Write Python programs using conditional logic with 'if', 'elif', and 'else' statements, including Boolean expressions and compound conditions within loops.
-- Construct Python functions that encapsulate tasks, manage parameters, local, and global variables, and return values to enhance code modularity and readability.
-- Transform complex datasets into a tidy format using pandas functions like 'melt()' for reshaping, 'groupby()' for aggregation, and 'to_datetime()' for date handling. Address practical challenges and demonstrate the benefits of tidy data for analysis.
-- Create and customize data visualizations using Pandas and Plotly, generating various plot types (line, area, bar, histogram) to analyze trends and draw insights from time-series data.
-- Prepare for advanced Python topics such as web scraping and APIs.
+- Learn core Python syntax, variables, data types, and functions while using the JupyterLab interface.
+- Control program execution using lists, loops, conditional logic, and custom functions to automate data tasks.
+- Load, clean, aggregate, and reshape tabular datasets using the Pandas library to create analysis-ready data.
+- Generate insightful charts from tidy datasets using Pandas and Plotly.