Python Excel Applications

Available Dates & Locations

This course is offered exclusively as In-House or Virtual, with no public sessions currently available – please contact us for inquiries.

COURSE OVERVIEW

This course equips participants with practical data analysis and visualization skills by combining the power of Python with the familiarity of Microsoft Excel. Participants learn how to enhance their everyday analytical work using simple, accessible techniques that require no prior programming experience. The course introduces a structured way to transform raw data into meaningful insights by creating automated analyses and professional visual outputs. With a focus on real business scenarios, participants gain confidence in using python analytic built-in feature directly within Excel to reinforce concepts and deepen understanding of the analytical workflow.

COURSE OBJECTIVES

By the end of this course, participants will be able to:

Create DataFrames from Excel tables and ranges using Python
Examine and prepare datasets for analysis inside Excel
Generate a range of visual charts directly within Excel using Python
Interpret statistical outputs to support business decisions
Apply analytical workflows using Python in Excel to streamline reporting
Visualize distributions, patterns, and relationships across datasets
Learn Python skills and scripts

Note: Participants must bring their own laptop running Microsoft Excel 365 with Python in Excel enabled, as well as Anaconda Jupyter Notebook or access to Google Colab.

TARGET COMPETENCIES

Data analysis
Data visualization
Python fundamentals
Excel integration
Statistical concepts
Analytical reporting
Exploratory analysis

This course is designed for professionals who rely on Excel for decision-making and want to enhance their analytical capabilities using Python inside Excel. Suitable for: Finance Professionals, Business Analysts, Reporting Specialists, Operations Analysts, and MIS Professionals.

The course combines structured demonstrations, clear explanations, real-world examples, and progressive walkthroughs to develop practical Python-in-Excel skills supported by optional notebook-based practice.

Introduction to Using Python in Excel

Python activation and usage within Excel
Python cell behavior and execution structure
Table and range recognition within Python cells
Data input options from Excel worksheets
Output display and data return options

Working with DataFrames in Excel

DataFrame layout and structure elements
Column and row referencing techniques
Data cleaning rules and preparation needs
Handling of missing and inconsistent values
Grouping, summarization, and aggregation concepts
Table merging and joining fundamentals

Numerical Foundations for Analysis in Excel

Array structure and numeric representation
Statistical measurement components
Mathematical operations for numerical analysis
Interaction between arrays and DataFrames
Percentile, ranking, and distribution elements

Visualizations Using Python Inside Excel

Chart figure configuration elements
Style and formatting structures
Distribution chart types and components
Relationship visualization categories
Categorical visualization structures
Multi-visual layout organization

Exploratory Data Analysis Using Python in Excel

Summary statistics layout and interpretation
Correlation matrix reading and structure
Outlier recognition indicators
Variable pattern exploration components
Trend identification structures
Dataset preparation for EDA workflows

Advanced Visual Structures and Visual Insights

Histogram design elements and layout
Pair plot relationship structures
Swarmplot categorical representation layers
Joint plot configuration types
KDE distribution principles
Hex joint plot density logic
Violin chart distribution interpretation
Heatmap correlation mapping structure

Automating Analysis and Visualization Workflows in Excel

Template creation structure for repeatable analysis
Parameter adjustment concepts for flexible use
Data filtering logic using Python within Excel
Output writing and table-return elements
Chart export and placement considerations
Dashboard integration using Python-generated visuals