Data Private

Financial Data Analytics (FIN400)

5 days

Analyze financial data end to end with Power BI and Python: ETL, data modeling, KPI dashboards, forecasting, and risk-aware recommendations.

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Course Overview

This course is designed for finance professionals who need to work as data analysts within their own domain, taking financial data from raw source through to forecast and recommendation. It covers the full financial analytics lifecycle in two phases, a Power BI phase spanning extract, transform, and load (ETL) work, data mastering, data modeling, and interactive data visualization and reporting, followed by a Python phase covering programmatic data preparation, predictive modeling, and risk analysis. It closes with applying those results to financial case problems drawn from accounting, auditing, corporate finance, and investment management, and communicating actionable business insights to both technical and non-technical audiences.

Course Benefits

Locate financial and organizational decisions where analytical evidence can improve the choice, and select an appropriate approach for each situation.
Extract, transform, and load financial data from spreadsheets, ledgers, and ERP exports, using both Power Query and Python.
Design financial data models, build measures that calculate financial metrics and KPIs, and deliver interactive dashboards and reports for technical and non-technical audiences.
Use statistical analysis, econometric regression, and machine-learning techniques to forecast financial outcomes and answer defined business questions.
Evaluate and interpret model outputs to perform risk analysis and make risk-aware recommendations that support strategic financial management and risk management.

Delivery Methods

Private Class
Delivered for your team at your site or online.

Course Outline

Day 1: Financial ETL and Data Modeling in Power BI
1. Financial Data Extraction, Transformation, and Loading: Extracting financial data from Excel spreadsheets, ledgers, and ERP-system exports; cleaning and shaping in Power Query; loading into the data model.
2. Star Schema Data Modeling for Finance: Fact and dimension tables for financial data; configuring relationships; cardinality and filter direction; integrating new data into an existing model.
3. Measuring Financial Performance with DAX: Creating measures and calculated columns; key aggregations; financial metrics such as totals, averages, ratios, and budget variance.
4. Time-Window Analysis for Financial Reporting: Window-lag measures; period-over-period comparison; shifting data perspectives for trend reporting.
Day 2: Financial Dashboards, Scenarios, and the Reporting Capstone
1. Dynamic KPI Reporting: Switch measures; KPI pulse reporting; combining measurement techniques; filtering visuals using existing measures.
2. What-If Analysis for Financial Scenario Planning: What-if parameters; dynamic hypotheticals; scenario evaluation for budgets and investments.
3. Row-Level Security and Shared Reporting: Row-level security roles; audience-appropriate views; publishing to the Power BI service.
4. Finance Reporting Capstone: Identifying a financial reporting opportunity (bottleneck, variance, forecast gap, compliance reporting gap); designing and building an interactive Power BI dashboard to address it; stakeholder briefing.
Day 3: Transitioning to Python for Financial Analysis
1. Setting Up the Python Environment: Setting up Anaconda; navigating Jupyter notebooks; running and documenting code.
2. Python Fundamentals for Finance: Variable types and assignment; type conversions; calculations with financial variables.
3. Lists, Dictionaries, and Structured Financial Data: Storing and accessing collections; mutability and subsetting; dictionaries for financial instruments.
4. Logic, Control Flow, and Filtering: Conditional logic; comparison operators for financial thresholds.
5. Functions and Packages: Built-in functions; importing packages; reuse of analysis steps.
6. Creating and Exploring Financial DataFrames: Building DataFrames; importing and exporting from Excel and CSV; exploring structure and contents.
Day 4: Cleaning, Combining, and Preparing Financial Data in Python
1. Filtering and Reshaping Financial Data: Filtering by conditions; subsetting; reshaping for analysis.
2. Cleaning and Repairing Financial Data: Correcting errors; handling missing values; fixing inconsistent entries.
3. Aggregation and Grouping: Grouping by category; aggregations for financial metrics.
4. Joining and Combining Datasets: Merging related tables; holistic analysis across sources.
5. Preparing Data for Predictive Modeling: Assessing data relevance; converting categorical data; standardizing values for modeling.
Day 5: Predictive Modeling and Risk-Aware Recommendations
1. From Data to Predictions: Predictions without machine learning; correlation and relationships among financial variables.
2. Regression Modeling for Financial Forecasting: Linear regression for continuous outcomes; interpreting fit and coefficients.
3. Classification for Financial Decisions: Logistic regression; probability prediction; threshold adjustment.
4. Evaluating and Comparing Models: Accuracy scoring; precision, recall, and confusion reporting; choosing the best estimator with financial risk context.
5. Interpreting Models for Risk-Aware Recommendations: Model outputs as decision support; prescriptive framing; presenting to stakeholders.

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Data class:

Data Analysis with Power BI or equivalent experience (connecting/shaping data, building a data model, and creating reports), plus routine work with financial data such as budgets and ledgers.

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