We may earn an affiliate commission when you visit our partners.
Course image
EDUCBA

This comprehensive course introduces learners to the practical applications of SAS in quantitative finance, combining foundational statistical concepts with advanced market data analysis.

In Module 1, learners explore the SAS environment, conduct descriptive statistics, apply T-Tests, interpret correlation, and build regression and variance models to evaluate financial data.

In Module 2, participants progress to advanced applications by applying SAS procedures to economic datasets, forecasting financial trends, and analyzing real-world financial markets including the BSE Sensex and forex.

Read more

This comprehensive course introduces learners to the practical applications of SAS in quantitative finance, combining foundational statistical concepts with advanced market data analysis.

In Module 1, learners explore the SAS environment, conduct descriptive statistics, apply T-Tests, interpret correlation, and build regression and variance models to evaluate financial data.

In Module 2, participants progress to advanced applications by applying SAS procedures to economic datasets, forecasting financial trends, and analyzing real-world financial markets including the BSE Sensex and forex.

By the end of this course, learners will be able to analyze financial datasets, apply SAS procedures to real-world economic and market scenarios, interpret statistical outputs accurately, and evaluate financial risks for data-driven decision-making.

Enroll now

What's inside

Syllabus

Foundations of SAS in Quantitative Finance
This module introduces learners to the essential foundations of using SAS for quantitative finance. Participants will explore the SAS environment, apply statistical testing techniques, and interpret regression and variance models. By the end of this module, learners will be able to confidently use SAS tools to analyze financial data, evaluate relationships among variables, and apply regression modeling techniques to real-world market scenarios.
Read more

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Career center

Learners who complete Quantitative Finance with SAS: Apply, Analyze & Evaluate will develop knowledge and skills that may be useful to these careers:
Quantitative Analyst
A Quantitative Analyst is a professional who applies mathematical and statistical methods to financial and risk management problems. This role involves developing and implementing complex models to forecast market movements, price financial instruments, and assess risk. This course directly prepares individuals for a Quantitative Analyst position by teaching the practical applications of SAS in quantitative finance. Learners will master statistical testing techniques, build regression and variance models to evaluate financial data, and analyze real-world financial markets. The ability to interpret statistical outputs accurately and evaluate financial risks for data-driven decision-making using SAS is paramount in this field, and this course provides that essential expertise. Many roles for a Quantitative Analyst typically require an advanced degree.
Financial Engineer
A Financial Engineer designs and develops innovative financial products, models, and solutions using advanced mathematical and computational methods. This highly specialized role often involves complex quantitative analysis to solve sophisticated financial problems. This course provides a strong foundation essential for a Financial Engineer. It focuses on applying core statistical concepts, regression and variance models, and advanced SAS procedures for analyzing financial datasets. The ability to evaluate relationships among variables, interpret financial indicators, and assess market volatility using SAS is crucial for designing, testing, and implementing sophisticated financial instruments and strategies. An advanced degree is typically required for this role.
Risk Analyst
A Risk Analyst identifies, assesses, and mitigates financial risks that could impact an organization's assets, earnings, or reputation. This involves analyzing market data, creditworthiness, and operational exposures to develop robust risk management strategies. This course directly addresses the core competencies required for a Risk Analyst. Learners explicitly evaluate financial risks for data-driven decision-making, analyze financial datasets, and interpret statistical outputs accurately using SAS. The comprehensive understanding of statistical testing techniques, regression modeling, and analysis of market volatility using SAS provides an essential toolkit for effectively managing and reporting on financial risks.
Financial Modeler
A Financial Modeler constructs complex spreadsheets and software-based models to forecast financial performance, value assets, and assess various financial scenarios. This role is critical for strategic planning, investment analysis, and risk management within organizations. This course is highly beneficial for aspiring Financial Modelers, as it focuses on building regression and variance models to evaluate financial data and forecasting financial trends using SAS. The program equips learners with the skills to apply SAS procedures to economic datasets and interpret statistical outputs accurately, which are fundamental for developing robust and precise financial models, ensuring reliable data-driven decision-making.
Financial Data Analyst
A Financial Data Analyst collects, processes, and analyzes financial information to identify trends, generate reports, and support critical financial decision-making within an organization. This role is foundational for data-driven insights in finance. This course directly benefits an aspiring Financial Data Analyst. Learners explore the SAS environment, apply descriptive statistics, T-Tests, correlation, and regression models to analyze financial datasets. The ability to apply SAS procedures to real-world economic and market scenarios, coupled with interpreting statistical outputs accurately, is central to excelling in this role and providing comprehensive, reliable financial data insights.
Econometrician
An Econometrician applies statistical and mathematical methods to economic data to test theories, forecast economic trends, and analyze causal relationships. This role is fundamental in economic research, policy analysis, and financial forecasting. This course is exceptionally relevant for an Econometrician, as it extensively covers applying SAS procedures to economic datasets, forecasting financial trends, and evaluating economic data. The strong emphasis on statistical testing techniques, regression and variance modeling, and interpreting statistical outputs accurately using SAS directly aligns with the core responsibilities of developing and applying robust economic models. An advanced degree is typically required for this role.
Quantitative Research Scientist
A Quantitative Research Scientist conducts in-depth analysis and develops advanced statistical and mathematical models to understand complex phenomena, often within academic, scientific, or financial domains. This role involves significant research and model validation. This course provides an excellent foundation for a Quantitative Research Scientist, especially those focusing on finance. Learners will explore the SAS environment, apply statistical testing techniques, and build regression and variance models to evaluate financial data. The emphasis on analyzing real-world economic and market scenarios, coupled with accurately interpreting statistical outputs, is crucial for developing and validating robust quantitative research methodologies in finance. An advanced degree is often required.
Data Scientist, Finance
A Data Scientist Finance professional applies advanced analytical techniques, machine learning, and statistical modeling to extract actionable insights from vast financial datasets. This role is crucial for developing predictive models, optimizing trading strategies, and enhancing risk assessment. This course provides a robust foundation for a Data Scientist Finance career by emphasizing the application of SAS for statistical testing, regression, and variance models to analyze financial and economic datasets. The ability to interpret statistical outputs accurately, analyze market volatility, and work with real-world financial markets using SAS directly translates into identifying patterns and building sophisticated data-driven solutions in finance.
Algorithmic Trader
An Algorithmic Trader develops and implements automated trading strategies based on complex quantitative models and real-time market data. This role requires a robust understanding of financial markets, statistical analysis, and programming for execution. For an Algorithmic Trader, developing predictive models and understanding market dynamics are paramount. This course, with its focus on applying SAS for forecasting financial trends, analyzing real-world financial markets, and evaluating financial risks for data-driven decision-making, helps build a foundational skill set. The ability to interpret financial indicators and analyze market volatility using SAS is crucial for developing and refining robust trading algorithms.
Investment Analyst
An Investment Analyst researches financial instruments, markets, and economic trends to provide insights and recommendations that guide investment decisions. This role requires a keen ability to interpret complex data and understand market drivers. This course significantly benefits an aspiring Investment Analyst. Learners will acquire skills in analyzing financial datasets, interpreting financial indicators, and evaluating economic data using SAS. The application of T-Tests, correlation analysis, and regression models to evaluate financial data, coupled with the analysis of real-world financial markets like the BSE Sensex and forex, are essential for making informed and data-driven investment recommendations.
Portfolio Manager
A Portfolio Manager is responsible for constructing and managing investment portfolios for clients or institutions, aiming to maximize returns while managing risk according to specific objectives. This role demands strong analytical capabilities and a deep understanding of market dynamics. This course enhances the skills crucial for a Portfolio Manager by focusing on analyzing financial datasets, forecasting financial trends, and understanding market volatility using SAS. The emphasis on applying statistical testing techniques, interpreting financial indicators, and evaluating financial risks for data-driven decision-making directly supports optimizing investment strategies and managing portfolio performance effectively.
Credit Risk Analyst
A Credit Risk Analyst assesses the creditworthiness of individuals or entities, developing models and strategies to evaluate and manage potential losses from loan defaults and other credit exposures. This role is vital for financial institutions. This course may be useful for a Credit Risk Analyst in developing strong analytical skills. It teaches applying statistical testing techniques, building regression models to evaluate financial data, and interpreting economic datasets using SAS. These skills are directly applicable to constructing and validating credit risk models, assessing potential financial risks, and contributing to data-driven decision-making in lending and credit portfolio management.
Treasury Analyst
A Treasury Analyst manages an organization's liquidity, investments, and financial risk exposures, optimizing cash flow and ensuring financial stability. This role requires a blend of financial acumen and analytical precision. This course may be helpful for a Treasury Analyst by enhancing skills in financial data analysis and risk evaluation. It covers analyzing financial datasets, forecasting financial trends, and evaluating financial risks for data-driven decision-making using SAS. These capabilities are highly relevant for monitoring cash positions, managing short-term investments, and mitigating market exposures, thereby contributing to the organization's financial health and strategic planning.
Market Research Analyst
A Market Research Analyst gathers and analyzes data to understand market conditions, consumer preferences, and competitive landscapes. While often broader, this role can specialize in financial products or markets, influencing strategic decisions. While typically not purely quantitative finance, a Market Research Analyst focusing on financial markets will find this course helpful. The course equips learners to analyze financial datasets, interpret correlation, and apply regression models, which are critical for understanding market dynamics and forecasting financial trends in specific sectors. Analyzing real-world financial markets like the BSE Sensex and forex directly supports market-driven insights and strategic positioning.
Actuarial Analyst
An Actuarial Analyst evaluates financial risks associated with insurance and pension programs, using statistical models to calculate probabilities and costs of future events. This role is critical for pricing, reserving, and solvency assessment. An Actuarial Analyst may find this course helpful in building a foundation in statistical analysis and data manipulation. The skills in applying descriptive statistics, T-Tests, correlation, regression, and variance models to financial data using SAS are transferable to actuarial modeling for evaluating risks and making data-driven decisions within the insurance context. The course's focus on interpreting statistical outputs also aligns with actuarial reporting.

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Quantitative Finance with SAS: Apply, Analyze & Evaluate.
Provides a detailed guide to the risk management process. It covers all the steps involved in risk management, from risk identification to risk mitigation. It valuable resource for anyone who wants to learn more about how to manage risk effectively.
Provides a comprehensive and current introduction to the foundations of linear regression analysis. It is widely used as a textbook in academic institutions and is suitable for gaining a broad understanding. The latest edition includes new material on generalized regression techniques and updated examples.
Is highly regarded for its example-driven approach, making it ideal for beginners and those who prefer hands-on learning. It progresses from basic concepts to more advanced topics, helping to solidify understanding through numerous code examples. It's a popular choice for self-study and as a companion to introductory SAS courses.
Provides a practical and engaging introduction to regression analysis and other statistical methods, focusing on real-world data and research questions. It's suitable for a broad audience and emphasizes the importance of data visualization and model checking.
Aimed specifically at beginners, this book guides users through the SAS Studio environment, particularly relevant for those using SAS OnDemand for Academics. It covers basic tasks and introduces writing SAS programs with easy-to-follow, step-by-step instructions. This great resource for those just starting out with SAS and the cloud-based interface.
A solid reference book that covers a wide range of theoretical aspects in regression analysis. While extensive, some users note the code examples might be in a less common dialect. It valuable resource for deepening understanding and is often used at the undergraduate and graduate levels.
Provides a comprehensive introduction to financial econometrics. It covers a wide range of topics, including time series analysis, regression analysis, and forecasting.
Provides a comprehensive overview of Bayesian data analysis. It covers topics such as Bayesian inference, Markov chain Monte Carlo, and model selection. It valuable resource for researchers and students who want to develop a strong foundation in Bayesian data analysis.
Provides a comprehensive treatment of econometric analysis of cross section and panel data. It covers topics such as linear regression, generalized linear models, and nonlinear models. It valuable resource for researchers and students who want to develop a strong foundation in econometrics.
Provides a comprehensive guide to corporate financial forecasting and planning, covering a wide range of topics including financial statement analysis, forecasting techniques, and risk management. It is suitable for both students and practitioners.
Focuses on contemporary topics in SAS, specifically machine learning using the SAS Viya platform. It covers various machine learning techniques and their implementation in the modern SAS environment. This is highly relevant for those interested in current data science applications with SAS.
A comprehensive and widely used textbook covering linear statistical models, this book is excellent for developing a deep understanding of the subject. It is often used in undergraduate and graduate programs and serves as a valuable reference for professionals. It covers regression, analysis of variance, and experimental design.
Considered a more advanced counterpart to 'An Introduction to Statistical Learning,' this book delves deeper into the theoretical underpinnings of statistical learning. It's a valuable reference for those looking to deepen their understanding and is highly regarded in both academic and industry settings. It covers a wide range of topics relevant to statistical modeling and machine learning.
Provides a comprehensive introduction to machine learning for finance. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides proofs for many of the formulas and theorems used in descriptive statistics.
Provides a unique insight into the art of risk assessment. It discusses the different factors that need to be considered when assessing risk, and it provides a number of practical tips on how to do so effectively. It valuable resource for anyone who wants to learn more about how to assess risk effectively.
Emphasizes the practical application of regression analysis through numerous examples. It's an excellent resource for understanding how to perform regression and interpret the results, making it suitable for both broad understanding and practical application. The focus is on exploratory data analysis rather than just theory.
Offers in-depth coverage of regression analysis and generalized linear models with a focus on applications, particularly in the social sciences. It balances theoretical concepts with practical examples and is suitable for both students and researchers. The latest edition includes new chapters on mixed-effects models.
Bridges the gap between statistical modeling and machine learning, focusing on the practical aspects of building predictive models. It covers various techniques and provides guidance on the entire modeling process. It's highly relevant for those interested in applying statistical modeling for prediction.
This is the official guide for the advanced SAS programming certification. It is designed for experienced SAS users who want to validate their skills and deepen their understanding of advanced techniques. It includes a workbook with sample scenarios to practice coding solutions.
Provides a comprehensive overview of economic data analysis. It covers topics such as data collection, data cleaning, and data analysis. It valuable resource for students and practitioners who want to develop a strong foundation in economic data analysis.
Considered a classic, this book offers a deep and intuitive look at statistical models, particularly in the linear setting. Written by a highly respected statistician, it provides valuable insights into the principles of model building. It's suitable for those with a solid statistical background looking for a foundational perspective.
Standard reference for analyzing categorical data using statistical models like logistic regression and log-linear models. It's essential for researchers and practitioners who work with data that fall into categories. It provides comprehensive coverage of methods and examples.
Provides a detailed overview of descriptive statistics, including measures of central tendency, dispersion, and skewness.
Focuses on developing a comprehensive strategy for regression modeling, covering various types of regression models and emphasizing the importance of model validation and interpretation. It's a valuable resource for practitioners and researchers building complex regression models.
Provides a comprehensive overview of data analysis techniques that are commonly used in economics and finance. It covers topics such as data cleaning, exploratory data analysis, regression analysis, and time series analysis.
Provides a clear and concise introduction to causal inference. It covers topics such as causality, confounding, and identification. It valuable resource for students and researchers who want to develop a strong foundation in causal inference.
Provides clear explanations and examples for performing various statistical analyses using SAS procedures. It's a valuable resource for understanding how to apply statistical methods within the SAS environment and interpreting the output. Suitable for those with some statistical background.
Provides a practical approach to data analysis using SAS, focusing on real-world scenarios and techniques. It's a good resource for applying SAS skills to solve practical data problems and gaining hands-on experience.
For users looking to move beyond the basics, this book delves into more advanced SAS programming techniques. It's helpful for optimizing code, working with complex data structures, and utilizing advanced procedures. good next step after mastering the fundamentals.
A long-standing and well-regarded text that provides a thorough introduction to applied regression analysis. It balances theory and examples and has been a popular choice for practitioners for many years. While not the most recent, its content remains relevant for a solid understanding.
Covers statistical modeling from a computational perspective, which is increasingly important in modern statistics. It delves into computational methods relevant to fitting and analyzing statistical models. It's suitable for graduate students and researchers interested in the computational aspects.
Provides a practical guide to financial data analysis, with a focus on the use of statistical techniques. It includes a number of real-world examples and case studies, making it a valuable resource for financial professionals.
Focuses on the specialized area of analyzing complex survey data using SAS. It's highly relevant for researchers and statisticians working with data from surveys involving stratification, clustering, and unequal weights. It provides detailed guidance on using the appropriate SAS procedures.
A practical guide to using SAS functions with numerous examples. is an excellent reference for anyone working with data manipulation and transformation in SAS. It helps users understand and effectively utilize the wide array of SAS functions available.
Offers an innovative approach to statistical modeling, emphasizing the use of models to understand and quantify variation in data. It introduces statistical reasoning with a focus on applying concepts using R. It's suitable for building a solid foundation and understanding the logic behind statistical inference.
Provides a detailed treatment of risk-neutral pricing, a fundamental concept in quantitative finance. It covers advanced topics such as the Heath-Jarrow-Morton framework, LIBOR market models, and credit risk models, and is suitable for readers with a strong background in financial mathematics and stochastic calculus.
A more accessible follow-up to 'Mostly Harmless Econometrics', this book focuses on key methods for causal inference, including regression-based approaches, with a strong emphasis on intuition and examples. It's an excellent resource for understanding how to use regression to answer causal questions.
Provides a comprehensive overview of forecasting in economics and finance, covering a wide range of topics including time series analysis, econometric models, and forecasting techniques. It is suitable for both students and practitioners.
Comprehensive guide to analyzing categorical data using SAS procedures like PROC FREQ, PROC LOGISTIC, and PROC GENMOD. It's suitable for researchers and statisticians who work with this type of data and need to deepen their understanding of the relevant SAS techniques.
Provides a comprehensive overview of equity portfolio management, covering topics such as portfolio optimization, risk management, and performance evaluation. It is suitable for readers who are interested in developing and implementing investment strategies for equity portfolios.
A detailed exploration of logistic regression theory and its application using SAS. is valuable for those performing this specific type of statistical modeling and provides a deeper understanding of both the statistical concepts and the SAS implementation.
Designed as a hands-on companion to introductory SAS learning, this workbook provides exercises and projects to reinforce concepts learned from other resources like 'The Little SAS Book' or 'Learning SAS by Example'. It's excellent for solidifying understanding through practice.
Introduces the use of SAS for analyzing mixed models, a common statistical technique in various research fields. It's suitable for graduate students and researchers who need to apply mixed models and provides a solid introduction to the relevant SAS procedures.
Focuses specifically on linear models and their implementation in R. It's a practical guide for understanding and applying linear regression and ANOVA, commonly used statistical modeling techniques. It's suitable for students and practitioners who want to work with linear models in R.
Provides a practical introduction to statistical concepts relevant to data science, including regression. It focuses on the 'how' and 'why' using R and Python, making it accessible to those with a programming background but limited statistical training. It's a good resource for applying regression in a data science workflow.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Quantitative Finance with SAS: Apply, Analyze & Evaluate.

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser