Become a Financial Analyst
MS Excel | Power BI | Python
To become a financial analyst, mastering tools like Excel, Power BI, and Python is essential. Excel helps with financial modeling and data analysis, Power BI enables interactive data visualization, and Python automates analysis with advanced financial algorithms. Gaining expertise in these tools enhances decision-making and career growth in finance.
Outline
Module 1: Financial Analysis, Forecasting, and Dashboard Reporting Using MS Excel
Chapter 1: Financial Analysis with MS Excel
• Mastering Complex Data Management
Gain expertise in advanced Excel formulas like VLOOKUP, XLOOKUP, INDEX, and MATCH to efficiently analyze, summarize, and organize complex financial datasets from multiple sources.
• Working with Time and Financial Data
Learn how to manipulate dates, times, text, and arrays to effectively process financial data for deeper insights.
• Advanced Data Summarization
Understand how to sort, filter, and format financial data with multiple conditions for easy analysis and reporting.
Chapter 2: Foundations of Financial Modeling
• Financial Modeling Best Practices
Understand the fundamental principles of financial modeling, focusing on building accurate and efficient models that provide reliable insights.
• Structure and Layout of Financial Models
Plan and structure your financial models with a clear layout that enhances readability and ensures logical flow.
• Integrating Core Financial Statements
Learn how to incorporate the balance sheet, income statement, and cash flow statement into your financial models to provide a comprehensive view of a company's performance.
• Building Key Financial Components
Develop components of financial models, such as revenue projections, expense analysis, asset management, depreciation, debt servicing, equity, dividends, and tax calculations.
• Valuation Techniques in Modeling
Gain an understanding of financial valuation techniques (e.g., discounted cash flow) and learn how to incorporate them into your financial models.
• Error-Proofing Financial Models
Learn how to identify, manage, and prevent errors in your financial models to ensure accuracy and reliability.
• Scenario Analysis for Financial Decision-Making
Apply variable-based what-if analysis to explore different financial scenarios and their potential impact on business outcomes.
• Presenting Financial Models Through Dashboards
Master the art of creating clear and concise dashboards to present your financial models in a way that communicates key findings to stakeholders.
Chapter 3: Dashboard Development for Finance Professionals
• Aligning Deliverables with Business Requirements
Draft and refine financial reports and dashboards to meet specific business requirements and objectives.
• Data Cleaning and Transformation
Learn how to efficiently import, clean, and transform financial data to make it ready for analysis and reporting.
• Choosing the Right Visualization
Understand the key principles of data visualization and select the most effective charts, graphs, and tables to convey financial information.
• Interactive Data Analysis Tools
Master the use of pivot tables, slicers, and timelines to create interactive dashboards that allow users to drill down into financial data.
• Formatting for Clarity and Precision
Apply value-based formatting techniques to highlight key financial figures and improve the readability of financial reports.
• Dynamic Dashboard Design
Learn how to design visually appealing and functional dashboards tailored to business needs, ensuring ease of use and actionable insights.
• Dashboard Security and Sharing
Master the tools to protect and share your financial dashboards securely with colleagues and stakeholders.
Chapter 4: Data Visualization and Forecasting for Financial Insights
• Visualizing Time-Series Financial Data
Learn how to visualize financial time-series data to reveal key trends, seasonality, and performance over time.
• Using Moving Averages for Trend Analysis
Develop moving average charts to identify long-term trends in financial data and forecast future performance.
• Trendline Analysis for Financial Forecasting
Master how to create, interpret, and leverage trendlines to identify patterns in financial data and inform future business strategies.
• Seasonality and Its Impact on Financial Trends
Analyze the effect of seasonality on financial data trends and learn how to incorporate these variations into your financial models.
Module 2: Financial Analysis, Forecasting, and Dashboard Reporting Using MS Power BI
Chapter 1: Data Integration and Modeling with Power BI
• Combining Multiple Data Sources
Master the art of merging multiple tables into a single cohesive dataset using the appending technique to streamline financial reporting.
• Applying Joins for Data Merging
Understand and apply different types of joins in Power Query to effectively combine data from various systems for comprehensive analysis.
• Essential Data Modeling Concepts
Grasp the fundamental concepts of data modeling to build structured datasets that are vital for accurate financial analysis and reporting.
• Impact of Data Types on Financial Reporting
Explore how different data types can affect financial decision-making, ensuring your data models align with business needs.
• Data Sourcing and Connectivity
Learn how to efficiently source financial data from various systems and apply connectivity options to automate workflows and reporting.
• Data Profiling and Transformation Best Practices
Gain practical skills in profiling, cleaning, and transforming financial data to ensure accuracy and improve data readiness for analysis.
• Establishing Table Relationships
Understand the various types of relationships (such as one-to-one, one-to-many) and their implications on data modelling
• Star Schema vs SnowFlake Schema
Understand the differences between star and snowflake schemas, their structures, and how they impact data modeling and query performance
Chapter 2: Financial Reporting and Analysis with DAX
• Mastering DAX Functions
Develop expertise in writing and optimizing DAX functions to enhance financial models and reporting accuracy. E.g SUM MIN, MAX, COUNT, AVERAGE, SUMX, RELATED, CALCULATE, FILTER, DATEADD, SAMEPERIODLASTYEAR, YTD, QTD, MTD, LD, LM, LY, YTD growth, % changes etc.
• Creating and Managing Complex DAX Expressions
Use DAX tools to manage and create complex expressions that ensure robust and efficient data models such as working with Multi Date.
• Designing Effective Data Visualizations
Learn how to create dynamic data visualizations that clearly communicate key financial insights and metrics to decision-makers.
• Storytelling with Financial Data
Understand the essential of visualizations such as bar, line, pie, map and other charts.
• Explore the Data Insights through visualization
Gain valuable insights by analyzing trends, patterns, and key metrics to support data-driven decision-making including Drill through & Tooltip
Chapter 3: AI-Powered Visuals and Financial Insights
• Overview of AI-Powered Visuals
Learn how to leverage AI-powered visuals such as the Decomposition Tree, Key Influencers, and Anomaly Detection to enhance financial analysis.
• Key Influencers in Financial Metrics
Use the Key Influencers visual to identify factors affecting key financial metrics such as revenue, profitability, and expenses.
• Anomaly Detection in Financial Data
Learn how AI-powered anomaly detection automatically flags outliers, such as unexpected cost spikes or revenue dips, helping detect fraud or identify issues.
• Drilling Down into Financial Performance
Use AI visuals to break down financial data into granular insights, enabling you to explore profitability by region, department, or product.
• Scenario Analysis with AI Tools
Leverage AI-powered tools for dynamic scenario analysis, such as forecasting revenue under different market conditions or assessing the impact of various factors on financial outcomes.
Chapter 4: Power BI Service for Finance Professionals
• Integration with Historical Financial Data
Learn how to integrate historical data with predictive models in Power BI to forecast future trends and make informed financial decisions.
• Enhancing Collaboration with Power BI Service
Understand how the Power BI Service enhances collaboration and reporting, allowing for real-time financial insights across teams.
• Automating Data Refreshes and Gateways
Master the process of automating the refresh of financial data, ensuring that your reports always reflect up-to-date information.
• Sharing Dashboards with Stakeholders
Gain skills in sharing finance dashboards securely with stakeholders, ensuring that key business leaders have access to the most current financial insights.
• Role-Based Financial Dashboards
Use Power BI Apps to create personalized, role-based dashboards for finance teams, providing tailored insights for different stakeholders.
Module 3: Data Analytics with Python for Finance Professionals Including Artificial Intelligence, Machine Learning and Predictive Analysis
Chapter 1: Mastering Python Fundamentals
• Understanding Python Variables and Data Types
Gain a solid understanding of Python's data types and variables, providing the foundation for efficient data analysis and manipulation.
• Mastering Python Operators and Mathematical Expressions
Develop proficiency in Python operators and mathematical expressions to handle complex calculations and data processing tasks.
• Working with Essential Python Data Structures
Learn how to use key Python data structures such as lists and dictionaries to organize and manipulate data efficiently.
• Handling User Inputs in Python
Master the techniques for effectively capturing and processing user inputs within Python programs.
• Using Python Loops for Iteration
Learn to harness the power of Python loops to automate repetitive tasks and processes, saving time in data manipulation.
• Comprehending Control Flow in Python
Strengthen your understanding of Python's control flow statements, including conditionals and loops, to build dynamic data analysis solutions.
• Creating and Implementing Python Functions
Master function creation to structure and modularize your Python code for better efficiency and readability.
Chapter 2: Advanced Data Analysis with Python
• Utilizing Pandas for Data Analytics
Learn how to leverage Pandas for data cleaning, wrangling, and merging tasks, equipping you to handle complex datasets with ease.
• Advanced Data Analysis Techniques
Develop skills in hypothesis testing and pattern detection, enabling you to uncover deeper insights from financial data.
• Exploratory Data Analysis (EDA)
Learn the techniques to manipulate and summarize data through EDA, helping you identify important trends and patterns.
• Creating Basic Visualizations for Data Insight
Learn to create basic data visualizations to improve understanding and derive actionable insights from your data.
Chapter 3: Python for Financial Data Analysis
• Analyzing Financial Data with Python
Develop the ability to efficiently analyze and interpret financial data using Python, unlocking patterns and trends in business data.
• Using Python’s Analytical Power for Financial Reporting
Learn how to harness Python’s capabilities to extract valuable financial insights, helping you make data-driven decisions.
Chapter 4: Building Predictive Models in Python
• Regression Analysis with Python
Gain expertise in training and testing regression models like Simple and Multiple Linear Regression, key for making data-driven financial predictions.
• Classification Models for Financial Data
Learn how to apply binary and multiclass classification algorithms like Logistic Regression, SVM, and Decision Trees for business forecasting.
• Preprocessing Techniques for Financial Data
Master essential preprocessing techniques such as label encoding, one-hot encoding, and feature scaling with Min-Max and Standard Scaler.
• Time Series Data Analysis
Perform detailed exploratory data analysis on time-series data, identifying trends, seasonality, and patterns in financial datasets.
Chapter 5: Time Series Forecasting and Decision-Making
• Training Time-Series Prediction Models
Master the techniques for training and testing time-series prediction models, including ARIMA, SARIMA, and SARIMAX, to forecast future financial performance.
• Applying Data-Driven Insights to Real-World Scenarios
Leverage the skills acquired to apply data-driven insights to real-world financial scenarios, enhancing your ability to make informed predictions and decisions.
Course Fee
● Online
Rs. 9,000/- x 3 Installment
● In-Person:
Account Details
Bank: Habib Bank Limited
Account Title: AIN GenX
Account No: 5910-70000512-03
IBAN No: PK08 HABB 0059 1070 0005 1203
Instructor
Irfan Bakaly
Data Analyst
25+ years of experience in Data Analysis
Noor Surani
Data Analyst
Data Scientist
25+ years of experience in Data Analysis
Waqas Younas
ACMA, CAF
12+ years of experience in Financial Analysis
Sohail Ahmed
Data Scientist
5+ years Experience of Data Science
Who this course is for:
Finance Professionals, Business Analysts, Consultants, Managers and Decision-Makers, Investors and Shareholders, Company Management, Financial Institutions, Industry Professionals, Students and Learners
Duration
● 5 Months (60 Hours)
Classes
● Online via Zoom
Schedule
- Day: Sunday's Only
- Timing: 02:30 - 05:30 pm (PST)
Starting From
● 20 April, 2025
Participants from Top Organizations
Certificates
