Introduction
This 5-day introductory training is designed to build foundational skills in programming, data handling, and basic analytics for professionals with no prior technical background. The course introduces Python programming in a beginner-friendly manner and gradually teaches participants how to work with CSV and Excel files, preparing them for more advanced topics such as financial analytics, visualization, and predictive modeling.
By using simple language, hands-on practice, and real-life examples from business and finance, participants will gain confidence in using data tools and understanding core technical concepts.
Target Audience
This preparatory course is ideal for:
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Professionals without a programming background
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Business or finance professionals who want to transition to data-driven roles
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Participants interested in data analytics but unfamiliar with Python or data formats
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Future attendees of the advanced "Python for Financial Data Analysis" course
Objectives
By the end of this training, participants will be able to:
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Understand the role of programming and data in business environments
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Write and run simple Python scripts using free, user-friendly tools
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Read and manipulate CSV and Excel files using Python
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Perform basic data cleaning and exploration
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Understand key concepts like variables, functions, and loops
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Use basic data visualization techniques
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Gain readiness to take more advanced analytics training
Outlines
Day 1:
Introduction to Programming Concepts
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What is Python and why is it used?
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Setting up your Python environment (Jupyter Notebook/Google Colab)
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Variables, numbers, and strings
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Writing and running basic code
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Simple calculations and outputs
Practice:
Write your first Python script (printing messages, simple math, combining text)
Day 2:
Data Structures and Logic
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Lists and dictionaries explained simply
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Conditional statements (if, elif, else)
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Introduction to loops: for and while
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Functions: what they are and how to use them
Practice:
Create a simple contact list, iterate over data, make small logical decisions
Day 3:
Working with Files – CSV and Excel
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Introduction to CSV and Excel files: what they are and where they’re used
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Reading files using Python and pandas
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Exploring tabular data (rows and columns)
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Simple operations: filtering, sorting, and selecting data
Practice:
Open a sample transaction file, explore it, and answer simple questions using code
Day 4:
Data Cleaning and Preparation
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Handling missing values
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Converting and renaming columns
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Combining and splitting data
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Saving data to new CSV or Excel files
Practice:
Clean a messy dataset and prepare it for analysis
Day 5:
Simple Visualization and Reporting
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Plotting data using matplotlib and pandas
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Bar charts, line charts, and histograms
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Exporting charts and cleaned data to Excel/PDF
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Summary reporting basics
