Financial Modeling and Business Analytics

BY
Boston Institute of Analytics

Master financial modeling techniques and business analytics tools to make data driven decisions.

Mode

Part time, Online

Duration

4 Months

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based

Course overview

The Financial Modeling and Business Analytics course is being offered by the Boston Institute of Analytics (BIA). This Financial Modeling and Business Analytics course equips participants with essential financial and analytical skills required in today’s corporate environment. This Financial Modeling and Business Analytics course combines theory with practical exposure, covering advanced financial modeling, data analysis and business analytics using tools like excel, power BI, tableau, SQL, and python. 

The duration of this Financial Modeling and Business Analytics course is 4 months. This Financial Modeling and Business Analytics course is designed for individuals from diverse backgrounds including commerce, arts, engineering, and science, as well as professionals aiming to upskill or transition into finance, investment, and analytics roles.

The highlights

  • 4 months course duration.
  • Immersive classroom experience
  • Online blended learning options
  • Training by top financial modelers
  • 50+ assignments and case studies
  • Practical hands-on capstone projects
  • Exclusive job opportunities portal
  • Globally recognized dual certification
  • Real-world financial modeling projects
  • Real-world business analytics projects
  • 200+ hours of learning and practicals
  • 360° career support
  • No-cost EMI options

Program offerings

  • Real-world projects
  • Expert instructors
  • Peer & alumni community access
  • Doubt clearing sessions
  • Case studies
  • No cost emi options available
  • 4 months course

Course and certificate fees

certificate availability

Yes

certificate providing authority

Boston Institute of Analytics

Who it is for

The target audience for the Financial Modeling and Business Analytics course includes:

Eligibility criteria

To enroll for this Financial Modeling and Business Analytics course the participants must have a minimum high school diploma or equivalent to that. Not mandatory, but professional experience in finance or analytics is advantageous.

What you will learn

Financial knowledge Business management Knowledge of banking Data analysis Knowledge of python Knowledge of excel Tableau knowledge

This Financial Modeling and Business Analytics course equips participants with robust financial modeling skills, enabling them to construct comprehensive models for budgeting, valuation and merger acquisitions. The participants also learn to leverage business analytics tools to extract actionable insights from data, enhancing decision-making across corporate functions. By mastering advanced Excel, SQL, Python, and visualization tools like Power BI and Tableau. The participants gain the ability to automate workflows, perform predictive analysis, and present data-driven strategies effectively.

The syllabus

Financial Modeling and Business Analytics: Orientation

Course Expectations and Structure
  • Overview of the Course Modules 
  • Brief on Assignments and Assessments
Introduction to the Learning Environment
  • Digital Platforms and Resources
  • Communication Channels

Advanced Excel for Financial Analysis

Excel Fundamentals for Finance
  • Essential formulas and shortcuts for financial modeling.
  • Data validation and conditional formatting.
  • Creating dynamic dashboards for financial analysis.
Excel-Based Sensitivity and Scenario Analysis
  • Automating scenario testing.
  • Monte Carlo simulations using Excel.
Advanced Excel Functions
  • Using pivot tables, solver, and goal seek for financial analysis.

Mastering Financial Modeling

Building Financial Models
  • Structuring and modeling historical financial statements.
  • Understanding key drivers and assumptions.
Modeling Projections
  • Revenue and expense modeling.
  • Asset schedule and depreciation.
Advanced Financial Modeling
  • Interlinking financial statements.
  • Modeling loans, advances, and taxation.
Modeling WACC and Sensitivity Analysis
  • Calculating WACC and cost of equity.
  • Running sensitivity analyses for key inputs.
Assignments
  • Create a comprehensive equity valuation model.

Mergers and Acquisitions (M&A) Overview

Understanding M&A
  • The theory and practice of M&A.
  • Synergy analysis and value creation.
  • Equity separations: Spin-offs, carve-outs.
Private Equity and Strategic Deals
  • Private equity deal structures.
  • Strategic investment and financing strategies.
Valuation, Pricing, and Fee Structures
  • M&A valuation techniques (e.g., DCF, Comparable Analysis).
  • Financing and exit strategies.
Assignments
  • Build an M&A model using real-world deal data.

Advanced M&A Modeling

Synergy and Contribution Analysis
  • Modeling accretion/dilution impact.
  • Sensitivity analysis on acquirer’s EPS.
Merger Accounting
  • Overview of merger accounting principles.
  • Modeling financing plans.
Assignments
  • Perform accretion/dilution analysis and synergy modeling for a case study.

GenAI and Prompt Engineering in Financial Modeling and Business Analytics

Introduction to Generative AI in Finance
  • Overview of GenAI applications in financial modeling.
  • Automating repetitive financial tasks.
  • Ethical considerations and responsible AI use.
Fundamentals of Prompt Engineering
  • Structuring prompts for financial modeling.
  • Tools and techniques for effective prompting.
Integrating GenAI with Existing Tools
  • Combining GenAI with Excel, Python, and BI tools.

Assignments
  • Develop a financial report using GenAI.

Introduction to Business Analytics

Types of Analytics
  • Descriptive Analytics: Summarizing historical data for trend identification (e.g., revenue trends, expense patterns).
  • Diagnostic Analytics: Understanding the reasons behind trends (e.g., variance analysis).
  • Predictive Analytics: Using historical data to forecast future outcomes (e.g., sales projections).
  • Prescriptive Analytics: Recommending actions based on analytics (e.g., optimal portfolio strategies).
Data Analytics Lifecycle
  • Data collection and cleaning.
  • Data exploration and visualization.
  • Insights generation and reporting.
Relevance of Analytics in Finance
  • Risk assessment and mitigation.
  • Budgeting and forecasting.
  • Investment and portfolio management.
Methodologies
  • Exploratory Data Analysis (EDA) for financial insights.
  • Hypothesis testing in finance-related scenarios.
Descriptive Statistics
  • Measures of central tendency (mean, median, mode).
  • Measures of dispersion (variance, standard deviation).
  • Correlation and covariance for understanding relationships between financial variables.
  • Applications in finance (e.g., analyzing stock returns, calculating volatility).
Inferential Statistics
  • Sampling Techniques
  • Confidence Intervals and Hypothesis Testing
  • Regression Analysis
Finance-Specific Applications
  • Estimating future cash flows using regression.
  • Understanding market trends using statistical techniques.

Data Analysis Using SQL

Introduction to SQL for Finance
  • Overview of SQL and its significance for finance professionals.
  • Retrieving and organizing financial data using SQL queries.
Core SQL Functions for Data Analysis
  • Filtering data using WHERE and HAVING clauses.
  • Grouping and summarizing financial data (e.g., COUNT, SUM, AVG).
  • Sorting and ordering data for analysis.
Advanced SQL for Financial Insights
  • Performing JOIN operations for data consolidation.
  • Using subqueries for layered financial analysis.
  • Building temporary tables for scenario testing.
Practical Applications of SQL in Finance
  • Cash flow analysis using SQL queries.
  • Identifying financial trends through SQL aggregations.
Assignments
  • Write SQL queries to analyze financial statements.
  • Perform a join operation to consolidate multi-source financial data.

Python for Finance Automation

Introduction to Python for Finance
  • Basics of Python programming.
  • Libraries for financial analysis: pandas, NumPy, matplotlib.
Automating Financial Tasks with Python
  • Data extraction, cleaning, and analysis.
  • Automating valuation and financial metrics calculations.
Python-Excel Integration
  • Using Python to enhance Excel workflows.
  • Building reusable financial templates and dashboards.
Assignments
  • Develop a Python script to automate financial data analysis.

Mastering Data Visualization

Mastering Power BI
  • Data modeling and DAX calculations.
  • Creating interactive dashboards.
Mastering Tableau
  • Data cleaning, transformation, and visualization.
  • Building advanced dashboards for financial storytelling.
Assignments
  • Create and publish a financial dashboard using Power BI or Tableau.

Capstone Project

Objective
  • Create and present a comprehensive Financial Model, including valuation and Ratio analysis, on a public company.

Evaluation Criteria
  • Quality and depth of analysis
  • Financial modeling accuracy
  • Presentation skills and ability to defend the thesis

Career Enhancement

Soft Skills Training
  • Presentation Skills
  • Email Etiquettes
  • LinkedIn Profile Building
  • Personality Development and Grooming
Interview Preparation
  • Interview Do’s and Don’ts
  • Mock Interviews
  • HR and Technical Interview Prep
  • One-on-One Feedback

Admission details

Follow the steps below to join the Financial Modeling and Business Analytics course.

Step 1 - Click on the link below: https://bostoninstituteofanalytics.org/financial-modeling-and-business-analytics/

Step 2 - Click on “Enquire Now”. Submit the form and your contact details and a representative will contact you.

Step 3 - Once the fees are paid, you are enrolled in the Financial Modeling and Business Analytics course.

How it helps

Earning this certification validates participants' expertise in financial modeling and analytics. This certificate enhances career prospects, boosts credibility and demonstrates readiness for complex corporate challenges.

FAQs

What is the duration of the Financial Modeling and Business Analytics course?

The duration of this Financial Modeling and Business Analytics course is 4 months.

Do I need prior finance experience?

No, there is no prior finance experience required.

What tools are taught?

Tools like Excel, Python, SQL, Power BI, Tableau, and Generative AI applications will be taught in this course.

Is the certification globally recognized?

Yes, BIA’s dual certification is recognized internationally.

Is this certification recognized globally?

Yes, BIA’s certificate is recognised globally.

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