Python For Banking & Finance Analytics with AI Training
The Python for Finance and Data Analytics Course at Educad Academy is a career-focused, industry-aligned program designed to develop advanced skills in Python programming, data analytics, financial analysis, and automation. This course is ideal for students and professionals aiming to build high-demand skills for roles in data analytics, fintech, and business intelligence.
The course begins with core Python fundamentals and environment setup, then progresses into real-world data analysis using Pandas and NumPy. Learners gain hands-on experience working with financial datasets, performing data cleaning, transformation, and extracting actionable business insights.
Participants will automate business workflows, generate reports, and integrate Python with SQL databases and REST APIs for live financial data processing. The course also focuses on building interactive dashboards using Stream lit and Plotly, enabling learners to present data-driven insights effectively.
Advanced modules cover financial modeling techniques such as NPV, IRR, portfolio analysis, and risk assessment, along with the application of machine learning for predictive analytics and decision-making. Learners will also explore AI-powered tools and large language models to enhance productivity and automate data workflows.
By the end of the course, learners will be able to design and deploy end-to-end data analytics solutions, build real-world projects, and create a strong professional portfolio. Graduates of Educad Academy will be prepared for careers in data analytics, finance, fintech, and business intelligence roles.
Course Objectives:
- Build a strong foundation in Python programming
- Develop clean, efficient, and reusable code
- Work with real-world data and multiple file formats
- Perform data analysis using Pandas and NumPy
- Automate reporting and business workflows
- Integrate Python with databases and APIs
- Create visualizations and interactive dashboards
- Apply financial models and basic machine learning
- Deploy Python applications in real environments
- Develop practical, project-based skills
Module 1: Introduction to Python and Environment
- What Python is, why it is the dominant language and how to set it up.
- You will install Python, configure your environment, and run your first script.
- Covers the difference between interpreted and compiled languages, choosing an IDE, and how to share and get help with code.
Module 2: Python Programming Foundation
- The building blocks of Python.
- Numbers, strings, booleans, lists, dictionaries, variables, built-in functions, and user-defined functions.
- Covers default arguments, keyword arguments, and reusable code structure.
Module 3: Python Programming: Loops, Logic, and Structure
- For loops, while loops, conditional statements, string formatting, working with dates and times, importing modules, and understanding
- Python packages.
- Introduces PEP coding guidelines and how to write clean, readable code that others can understand and maintain.
Project 1: depend on background
Module 4: Working with Data in Python
- Understanding the types of data you encounter in finance structured tables, semi-structured JSON, unstructured PDFs and emails.
- Reading and writing CSV, Excel, JSON, and XML files.
- Introduction to Pandas for data handling.
- Cleaning messy financial data: missing values, duplicates, wrong formats, and inconsistent entries.
Module 5: Data Analysis with Pandas and NumPy
- Deep work with Pandas Data Frames and Series.
- Filtering, sorting, grouping, aggregations, pivot tables, and merging datasets.
- NumPy for numerical operations, array math’s, and date-time handling.
- Practical focus on the kinds of analysis done every day in finance teams trend analysis, period comparisons, and data segmentation
Module 6: Automation: Reports, Emails, and File Processing
- Automating the repetitive work that takes hours every week.
- Generating formatted Excel reports with charts using openpyxl and xlsxwriter.
- Creating automated PDF reports. Sending emails with attachments using Python.
- Scheduling scripts to run automatically on any machine.
Project 2: depend on domain you have experience with
Module-7: SQL Databases and Python Integration
- How databases work in production environments.
- Connecting Python to MySQL.
- Writing and executing SQL queries from Python.
- Fetching results into Pandas Data Frames.
- Building end-to-end pipelines from database to report.
- Introduction to dbt for data transformation in analytics.
Module 8: NoSQL Databases and Modern Data Storage
- When relational databases are not the right tool.
- Introduction to document based databases with MongoDB.
- Inserting, querying, updating, and deleting records.
- Working with datetime fields and indexes.
- Brief introduction to cloud data storage options used in modern fintech pipelines.
Module 9: Working with APIs and Live Data
- What APIs are and how they work.
- Calling REST APIs from Python.
- Working with market data APIs for stocks, FX, commodities, and crypto.
- Calling payment and open banking APIs.
- Handling authentication, pagination, and rate limits.
- Parsing and storing API responses for analysis.
Module 10: Data Visualisation for Finance
- Why visualisation matters and how to do it well.
- Matplotlib and Seaborn for static charts.
- Plotly for interactive charts.
- Chart types used: line charts for time series, bar charts for comparisons, heatmaps for correlation, waterfall charts for P&L, and scatter plots for risk return.
- Design principles for charts that communicate clearly
Module 11: Building Dashboards
- Turning analysis and charts into interactive dashboards that non-technical users can explore.
- Building full dashboards with Stream lit.
- Introduction to Plotly Dash for more complex applications.
- Integrating Python visuals and data into Power BI.
- Designing dashboards around what users actually need to see.
Project 3: depends on the user domain
Module 12: Applied Use Cases with Python
- Real calculations and models used across finance sectors.
- Time value of money, NPV, IRR, bond pricing, option pricing basics (Black Scholes), portfolio return and volatility, Value at Risk (VaR), and financial statement ratios.
- These are not academic exercises.
- Each is built as a reusable Python function.
Module-13: Machine Learning for Finance
- What machine learning is and where it is genuinely useful.
- Supervised learning for classification and regression.
- Predicting loan default risk, customer churn, and price direction.
- Feature engineering from financial data.
- Evaluating model performance.
- Rule based systems for fraud detection as a bridge between logic and ML.
Module-14: AI Tools and LLMs in Workflows
- How large language models are being used in finance today.
- Calling the OpenAI API and other LLM APIs from Python.
- Extracting structured data from financial documents using AI (invoices, annual reports, contracts).
- Building a financial Q&A tool over your own data using retrieval-augmented generation (RAG).
- Using AI assisted coding tools in your daily workflow.
Module-15: Deploying Python Applications
- How to take a Python script or dashboard from your laptop to somewhere others can access it.
- Packaging code cleanly, using environment files, and writing a requirements file.
- Deploying a Streamlit dashboard to Streamlit Cloud (free).
- Basic introduction to Fast API for building a simple financial data API.
- Version control with Git and GitHub for analytics teams.
Learning Outcomes:
- Write clean and efficient Python code
- Analyze and process data using Pandas and NumPy
- Clean and transform real-world datasets
- Automate reporting and routine tasks
- Work with SQL/NoSQL databases and APIs
- Create data visualizations and dashboards
- Apply financial models and basic machine learning
- Use AI tools to enhance workflows
- Deploy Python applications and projects
- Build a job-ready portfolio
Who Should Join
- Banking and finance professionals ready to upgrade into high-demand analytics roles
- Data analysts who want to move beyond spreadsheets and apply Python in real financial environments
- Students and fresh graduates aiming to launch a career in data analytics, finance, or fintech
- Professionals looking to strengthen skills in Python, automation, and data-driven decision-making
- Finance, accounting, and business experts transitioning into modern data-focused roles
- IT, MIS, and software professionals in banks who want to automate reporting and build dashboards
- Aspiring data science and AI learners looking for practical, job-ready experience
International Student Fees: 550 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
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