Financial Business Analysis: Python | Excel | BI |Cloud

    Course Duration: 40 Hrs

    Objective: In today’s world, finance professionals are challenged with providing management a detailed analysis of the impact of the organization’s financial decisions. Therefore, finance professionals need to be skilled at reading through the numbers on the financial statements, analysing the figures, interpreting the various ratios and presenting this analysis in a dynamic manner. This course takes you from the first step of understanding the relationships between the different elements of financial statements through the process of calculating and analysing the financial ratios, to the last step of presenting recommendations.

    Technical Prerequisite: Knowledge of Python or Excel will be required.

    System requirement: Min 8 GB RAM, Windows 8 or above, high speed internet connection.


    Module 1: Introduction to Business Analysis

    • What is Business Analysis?
    • Uses of Financial Business Analysis.
    • Tools of Business Analysis.
    • Data analysis and predictive modelling.
    • Environment Setup for Power BI, Python, Firebase Cloud.

    Module 2: Quick Analysis

    • Beginners, Intermediate & Advance Functions with Tools.
    • Financial Function and Case Study.
    • Practical Implementation Build a P&L Chart.
    • Working with Pivot Table Slicer.
    • Pivot Table for Financial Reporting.
    • Building a Complete 3 Statement Model.
    • Creating Multiple Scenarios.
    • Methods for Financial Liabilities.
    • Filtering with Financial Data.
    • Statistical Computing with Data and Visualization.

    Module 3: Financial Analysis and Modelling

    • Financial Statement Analysis with raw Data.
    • Financial Statement Analysis with Tesla
    • Working with Capital Management.
    • Fundamental of Business and Financial Analysis.
    • Corporate Finance and Project Management.
    • Time value of Money and Interest Rate Components.
    • Value of Cash Flows with Different Timing.
    • Loan Calculation.
    • A Complete case study with Building a Financial Planning Model.

    Module 4: Revisited to Python & Anaconda Setup

    • Anaconda Jupyter Environment Setup.
    • Python language Overview.
    • Data types of Python, numbers, string.
    • If, el-if, Loops in python.
    • Functions and modules in python.
    • Lambda function.
    • Strings methods.
    • List and its methods.
    • Tuple, set, dictionary and their methods.

    Module 5: Financial Analysis using Python

    • Understanding the uses of various open source libraries.
    • Importing various modules with different methods.
    • Working with Numpy.
    • Numerical operations on Numpy array.
    • Exploring various use cases of Numpy.
    • Financial Analysis using scikit-learn, QuantLib, SciPy.

    Module 6: (EDA) & Conversion Excel Model to CSV

    • Pandas module and its uses in data analysis.
    • Series and Data frame.
    • Need of pre-processing of data.
    • Data wrangling and feature engineering.
    • Handling different pre-processing technique like missing value impute, explore data, convert from string to number etc.
    • Concepts of normalization and standardisation.
    • Standardize the dataset using Standard Scalar(), MaxMinScalar()
    • Data Source with Excel and csv Files.

    Module 7: Data Visualization using Python

    • Matplotlib, Seaborne, Plotly and Cufflinks.
    • Draw different types of graphs using above modules.
    • Pie chart, histogram, bar chart, boxplot, count plot etc.

    Module 8: Financial Data Analytics using Power BI

    • Dash Board Preparation with BI.
    • Connect to Kaggle Datasets.
    • Explore Pandas Data Frame.
    • Analyze and manipulate Pandas Data Frame.
    • Data cleaning with Python & Export to BI.
    • Data Visualization with Python.
    • Connect to web data with Power BI.
    • Clean and transform web data with Power BI.
    • Create data visualization with Power BI.
    • Publish reports to Power BI Service.
    • Transform less structured data with Power BI.
    • Connect to data source with excel.
    • Prep query with excel Power query.
    • Data cleaning with excel.
    • Create data model and build relationships.
    • Analyze data with Pivot Tables
    • Analyze data with Pivot Charts
    • Connect to data sources with Power BI
    • Join related data and create relationships with PowerBI

    Module 9: Financial Data Storage with Cloud

    • NoSQL Overview.
    • Concepts of JSON repo.
    • Firebase Query Wizard.
    • Create, insert, delete, update query with cloud.
    • Python firebase. Firebase Application () connectivity.


    • Financial Planning REP –I
    • Financial Planning REP & VIZ –II
    • Financial Planning REP Custom Control –III

    * Course topics and duration may be modified by the instructor based upon the knowledge and skill level of
    the course participants.