Statistical Tools for Financial Analysis
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Statistical Tools for Financial Analysis
This course is part of Financial Analytics & Decision Science Specialization
Instructor: EDUCBA
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What you'll learn
Apply statistical measures to analyze financial datasets and market performance.
Use spreadsheet functions for financial calculations and data evaluation tasks.
Interpret stock return trends using quantitative and spreadsheet-based analysis.
Skills you'll gain
Tools you'll learn
Details to know
May 2026
8 assignments
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There are 2 modules in this course
Learn how to analyze financial datasets using statistical tools and Excel-based analytical techniques to identify trends, evaluate market data, and support data-driven financial decisions. Master practical financial analytics skills used by analysts and finance professionals.
This course provides a hands-on introduction to statistical analysis for financial data using spreadsheet tools and real-world market examples. Youβll learn how to calculate and interpret key statistical measures such as mean, trimmed mean, geometric mean, mode, maximum and minimum values, and standard deviation to evaluate financial performance and data variability. Throughout the course, youβll apply practical spreadsheet functions including SUMPRODUCT, SUMIF, exponential, logarithmic, and absolute value calculations to analyze financial datasets effectively. Youβll also learn how to rank stock returns, apply threshold-based filtering, and compare financial data across stock exchanges such as BSE and NSE to identify meaningful patterns and market insights. What makes this course unique is its practical, spreadsheet-driven approach that combines statistical reasoning with real financial data analysis. Instead of focusing only on theory, learners gain hands-on experience applying quantitative methods directly to financial datasets. By the end of the course, youβll be able to confidently analyze financial data, interpret statistical results, apply advanced spreadsheet functions, and make informed financial decisions using quantitative analysis techniques.
This module introduces fundamental statistical concepts used in financial data analysis. Learners explore essential measures of central tendency and dispersion, including mean, trimmed mean, geometric mean, mode, maximum and minimum values, and standard deviation. Through practical Excel-based examples, the module builds foundational skills for interpreting financial datasets and understanding how statistical tools support financial decision-making.
What's included
6 videos4 assignments
6 videosβ’Total 42 minutes
- Introduction to Statistical Toolβ’6 minutes
- Trimmed Meanβ’6 minutes
- Geometric Meanβ’6 minutes
- What is Modeβ’7 minutes
- Max and Min Valueβ’5 minutes
- Standard Deviationβ’12 minutes
4 assignmentsβ’Total 60 minutes
- Foundations of Statistical Analysis for Financial Dataβ’30 minutes
- Introduction to Statistical Conceptsβ’10 minutes
- Understanding Central Tendencyβ’10 minutes
- Measuring Data Range and Volatilityβ’10 minutes
This module focuses on applying statistical and mathematical functions to real financial datasets using spreadsheet tools. Learners practice working with advanced Excel functions such as SUMPRODUCT, SUMIF, exponential, absolute, and logarithmic calculations. The module also demonstrates how to rank financial data, apply threshold filtering, and compare stock market datasets such as BSE and NSE to extract meaningful financial insights.
What's included
6 videos4 assignments
6 videosβ’Total 55 minutes
- Sum Product and Sum Ifβ’5 minutes
- Exponential Absolute and Logβ’7 minutes
- Calculating Averages in Financial Dataβ’11 minutes
- Ranking Stock Returns in Excelβ’11 minutes
- Threshold Filtering and Data Rankingβ’11 minutes
- Comparing BSE and NSE Data Setsβ’11 minutes
4 assignmentsβ’Total 60 minutes
- Applying Statistical Tools in Financial Analysisβ’30 minutes
- Excel Functions for Financial Calculationsβ’10 minutes
- Analyzing and Ranking Financial Dataβ’10 minutes
- Advanced Financial Data Filtering and Comparisonβ’10 minutes
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The Hong Kong University of Science and Technology
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