Python For Banking & Finance Analytics Training
This course is designed to help banking and finance professionals, analysts, and aspiring data specialists build strong Python programming skills with direct applications in the financial industry. The program starts with Python fundamentals and progresses toward advanced data handling, reporting automation, and building financial dashboards. Each module is backed with practical projects and real-world use cases relevant to corporate banking.
Key Highlights
- Learn Python from basics to advanced level with a focus on finance analytics.
- Hands-on projects: calculators, transaction analysis, fraud detection, churn prediction.
- Work with Excel, SQL databases, APIs, and automation scripts.
- Build dashboards and reports tailored for banking KPIs.
- Apply coding best practices for real-world corporate environments.
Module 1: Introduction to Python Programming
- Python Introduction
- Why Python for banking & finance analytics
- Python Installation, Windows Python Environment Setup
- Setting up Python (Anaconda, Jupyter, VS Code, IDEs)
- Interpreted vs. compiled programming languages
- Creating and running our first Python script
- Choosing an integrated development environment (IDE)
- How to share your code with us and get help with errors
Module 2: Python Programming Foundation
- Basic types – numbers, strings, string manipulation
- Basic types – Boolean operators
- Lists (arrays), Dictionaries, Variables
- Built-in functions, User-defined functions
- Adding arguments to a function
- Functions: creating and reusing code
- Default arguments, Keyword arguments, Infinite arguments
Project #1 – Building a Basic calculator
Module 3: Python Programming Advanced Concepts
- PEP guidelines
- Breaking out of while loops
- Classes & objects, Instance variables
- Class & variables
- How to add comments to your code
- Importing modules from relative paths
Module 4: Python Programming Advanced
- For Loop, While Loop
- Conditional Statements
- String Formatting
- Modules, Libraries and Packages
- Installing Python Packages
- Working with Date and Time objects
- Project#2 -Mortgage Calculator with Python
Module 5: Working with Data in Python
- Understanding banking datasets (structured, semi-structured, unstructured)
- Lists, tuples, sets, dictionaries
- File handling: Reading/writing CSV, Excel, text, JSON, XML
- Introduction to Pandas for data handling
- Handling missing values, duplicates, and inconsistent formats
Hands-on:
- Load customer transactions from Excel/CSV, clean and summarize them.
Module 6: Working with Excel files in Python
- Getting Started with Pandas
- Loading Data in Python from CSV
- Indexing and Slicing Dataframes
- Dropping Dataframe Columns and Rows
- Updating and Adding new Columns and Rows
- Series and DataFrames deep dive
- Filtering, sorting, grouping, aggregations
- Pivot tables in Pandas (Excel-like analysis)
Module-7 Working with Numbers
- NumPy with Python
- Using pandas Data Frames to solve complex tasks
- Use pandas and NumPy to handle Excel Files
- NumPy arrays for numerical operations
- Date-time manipulation (for transaction, loan, and deposit analysis)
Hands-on:
- Monthly customer deposits vs withdrawals trend analysis.
- Segment customers by card spending patterns.
Module 8: Learning About Database
- PyMongo Introduction and setup
- Inserting documents, Bulk inserts
- Counting documents, Multiple find conditions
- Datetime and keywords, Indexes
Project #4 – Crud Operations
Module 9: Data Visualization for Business Insights
- Importance of visualization in banking analytics
- Matplotlib & Seaborn basics
- Plot types: bar, line, histogram, scatter, heatmaps
- Visualizing financial KPIs (NPL, loan growth, deposits, churn, etc.)
- Dashboards in Python (intro to Plotly)
Hands-on:
- Visualize ATM transaction trends by region.
- Customer segmentation using transaction frequency and spending.
Module 10: Automation with Python
- Automating repetitive tasks (emails, reports, file processing)
- Working with Excel using openpyxl / xlsxwriter
- Automating report generation (PDF, Excel dashboards)
- Scheduling Python jobs (Task Scheduler / Cron)
- Automating API calls for financial data
Hands-on:
- Automate daily branch performance report in Excel with charts.
- Script to email daily customer transaction summary.
Module 11: Database & SQL Integration
- Introduction to databases in banking
- Connecting Python with SQL (SQLite, SQL Server, Oracle, etc.)
- Executing queries from Python
- Fetching results into Pandas DataFrames
- Building end-to-end pipelines (SQL → Python → Excel/Report)
Hands-on:
- Connect to banking database and pull top 100 high-value transactions.
- Automate customer loan default risk extraction report.
Module 12: Python for Reporting & Dashboards
- Exporting reports in Excel, PDF, and PowerPoint
- Creating interactive dashboards with Plotly Dash / Streamlit
- Bank KPIs dashboard (deposits, loans, card usage, fraud alerts)
- Integrating with Power BI (Python scripts in Power BI)
Hands-on:
- Build a simple customer churn dashboard with Streamlit.
Module-13: Corporate Banking Use Cases
- Automating customer segmentation reports
- Credit card transaction analysis
- Loan performance monitoring
- Customer churn prediction (basic ML model)
- Fraud detection with rule-based Python scripts
Additional Resources
- Cheat sheets (Pandas, NumPy, Matplotlib, SQL)
- GitHub repository with template scripts
- Python coding best practices in corporate environment
- How to collaborate using Git for analytics team
What You’ll Learn
- Python Programming Foundations: Variables, functions, loops, conditionals, classes, and
modules. - Data Handling: Pandas, NumPy, and file handling for CSV, Excel, JSON, and more.
- Database Integration: Work with SQL and NoSQL (PyMongo) for financial datasets.
- Visualization & Reporting: Use Matplotlib, Seaborn, Plotly, and Streamlit for business dashboards.
- Automation: Generate daily reports, automate emails, and schedule financial data processing tasks.
- Banking Use Cases: Customer segmentation, loan monitoring, fraud detection, and churn prediction.
Who Should Join
- Banking and finance professionals looking to upskill in analytics.
- Data analysts aiming to apply Python in financial contexts.
- Students and graduates seeking careers in fintech and corporate analytics.
- IT and MIS staff in banks who want to automate reporting and dashboards.
Projects & Hands-On Practice
- Basic & Mortgage Calculators (Python scripting)
- Transaction Analysis (Excel/CSV financial data cleaning and reporting)
- Customer Spending Segmentation (NumPy & Pandas)
- CRUD Operations with PyMongo
- Financial Dashboards (Plotly, Streamlit, Power BI integration)
- Automation Scripts (daily branch performance, churn reporting, fraud detection)
International Student Fees: 500 USD
Flexible Class Options
- Corporate Group Training | Fast-Track
- Weekend Classes For Professionals SAT | SUN
- Online Classes – Live Virtual Class (L.V.C), Online Training
Related Courses
Introduction to SQL Database
Python Data Science Beginners Course
Data Sciences Specialization Training
Python Machine Learning with Data Science
Python Programming Basic to Advanced Course
Administering a SQL Database Infrastructure
Intermediate Querying SQL Database using T-SQL
Master SQL, MongoDB & Redis | Complete Database Course
Mastering Python Machine Learning with Data Science Course
Python, Data science, Machine Learning and AI for Beginners Course
Mastering Python – Data Science with Machine Learning Serial Port Addition
Designing and Implementing a Data Science Solution With Azure Training Course
