Python and Pandas for Data Manipulation

Data work slows down when analysts use inconsistent methods to clean, combine, and visualize information from multiple sources. This associate-level Python and Pandas course helps IT Directors and Training Managers standardize core data-manipulation practices across data analyst and data-adjacent IT teams.

The curriculum is a realistic onboarding or skills-validation path for practitioners who already have some data science or Python exposure. Plan for about 18.5 hours per learner, covering setup, Jupyter Notebook, Pandas Series and DataFrames, cleaning, sorting, filtering, duplicate handling, value extraction, text cleanup, grouping, combining DataFrames, time series work, DataReader, date ranges, and visualization.

For change management, Team Leads can assign the course in phases: environment setup first, core Series/DataFrame work next, then reporting-focused topics like grouping, time series, and visualization. CBT Nuggets Playlists and Team Reporting can help Training Managers sequence the rollout and track completion across assigned learners.

IT teams complete this training to make day-to-day data preparation more repeatable and less dependent on one-off spreadsheet work or tribal knowledge. The course maps to common analyst workflows: setting up the Python environment, working in Jupyter Notebook, cleaning and filtering DataFrames, combining sources, preparing text fields, and visualizing results.

• Cleaner operational reports: Analysts learn repeatable approaches for sorting, filtering, removing duplicates, and extracting values from DataFrames.
• Faster onboarding for data roles: New data analysts get a structured path from Pandas Series basics to DataFrame cleaning and combining.
• Better source-to-report workflows: Teams practice grouping methods, combining DataFrames, and working with time series and date ranges for reporting use cases.
• More useful data outputs: Visualization training helps teams turn manipulated data into charts and summaries that support decision-making.

Knowledge

• How to install and set up the Python/Pandas working environment for data manipulation.
• How Jupyter Notebook fits into interactive analysis workflows.
• Core Pandas concepts, including Series, Series attributes and methods, and DataFrames.
• Common DataFrame operations: cleaning, sorting, filtering, duplicate handling, and value extraction.
• How text data, grouped data, combined DataFrames, time series, date ranges, DataReader, and visualization fit into analysis workflows.

Ability

• Prepare a working analysis environment and use Jupyter Notebook for Pandas tasks.
• Clean, sort, filter, and de-duplicate DataFrames to improve data quality.
• Extract values and apply extraction methods needed for analysis and reporting.
• Split, strip, and clean text data for more consistent fields.
• Group and combine DataFrames to summarize information from multiple sources.
• Work with time-based data and create visualizations that communicate results.