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.
This preparatory course is ideal for:
Professionals without a programming background
Business or finance professionals who want to transition to data-driven roles
Participants interested in data analytics but unfamiliar with Python or data formats
Future attendees of the advanced "Python for Financial Data Analysis" course
By the end of this training, participants will be able to:
Understand the role of programming and data in business environments
Write and run simple Python scripts using free, user-friendly tools
Read and manipulate CSV and Excel files using Python
Perform basic data cleaning and exploration
Understand key concepts like variables, functions, and loops
Use basic data visualization techniques
Gain readiness to take more advanced analytics training
Day 1:
Introduction to Programming Concepts
What is Python and why is it used?
Setting up your Python environment (Jupyter Notebook/Google Colab)
Variables, numbers, and strings
Writing and running basic code
Simple calculations and outputs
Practice:
Write your first Python script (printing messages, simple math, combining text)
Day 2:
Data Structures and Logic
Lists and dictionaries explained simply
Conditional statements (if, elif, else)
Introduction to loops: for and while
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
Introduction to CSV and Excel files: what they are and where they’re used
Reading files using Python and pandas
Exploring tabular data (rows and columns)
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
Handling missing values
Converting and renaming columns
Combining and splitting data
Saving data to new CSV or Excel files
Practice:
Clean a messy dataset and prepare it for analysis
Day 5:
Simple Visualization and Reporting
Plotting data using matplotlib and pandas
Bar charts, line charts, and histograms
Exporting charts and cleaned data to Excel/PDF
Summary reporting basics