Quantitative Finance & Financial Analytics

EDUCBA via Coursera Specialization

Go to class Write review

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

Build practical quantitative finance and financial analytics skills using Python, SAS, Excel, predictive modeling, and Bayesian statistics. Transform financial data into forecasts, interpretable models, and evidence-based decisions. This Specialization provides an applied pathway for analyzing financial and economic data across multiple analytical tools. You will prepare datasets, perform descriptive and inferential analysis, examine correlations, build regression models, evaluate variance, and interpret financial time series. You will use Python to manage financial data, analyze trends, develop visualizations, and communicate decision-ready insights. With SAS, you will apply statistical procedures to economic data, financial markets, exchange rates, and forecasting scenarios. The Specialization also develops predictive modeling skills through CART classification models for term deposit investment decisions. You will construct decision trees, tune model parameters, apply pruning techniques, reduce overfitting, and validate performance on unseen data. Finally, you will apply Bayesian inference, MCMC sampling, PyMC, hierarchical models, and A/B testing methods to evaluate uncertainty and update predictions as new evidence becomes available. By completion, you will be prepared to support data-driven decisions in finance, banking, investment analysis, business analytics, and predictive modeling.

Syllabus

Course 1: Analyze Financial Data with Python for Decision Making
Course 2: Quantitative Finance with SAS: Apply, Analyze & Evaluate
Course 3: Predictive Analytics Model for Term Deposit Investment
Course 4: Bayesian Statistics: Excel to Python A/B Testing

Courses

0 reviews

View details

Learn how to apply the Classification and Regression Tree (CART) algorithm to build predictive models for term deposit investment decisions. In this course, you will explore the complete predictive modeling workflow using real-world financial marketing scenarios, from understanding business objectives and interpreting data variables to developing, optimizing, and validating decision tree models. You will begin by learning the foundations of CART and the characteristics of data used for term deposit prediction. Next, you will prepare data, construct binary classification models, and apply node splitting techniques to build effective decision trees. Finally, you will improve model performance through parameter tuning, pruning strategies, and validation techniques to reduce overfitting and evaluate model performance on unseen data. Designed for learners interested in predictive analytics, machine learning, financial marketing, and data-driven decision-making, this course emphasizes model transparency and interpretability while following industry-standard modeling practices. By the end of the course, you will be able to describe data characteristics, build CART classification models, optimize decision trees, and evaluate predictive model performance for term deposit investment scenarios.
0 reviews

View details

Master Bayesian Statistics: Apply, Implement & Optimize A/B Testing equips you with the knowledge and practical skills to apply Bayesian statistics to machine learning, A/B testing, and healthcare analytics. Throughout the course, you will build a solid foundation in Bayesian inference, learn how probabilistic thinking supports decision-making under uncertainty, and implement Markov Chain Monte Carlo (MCMC) sampling using PyMC to approximate posterior distributions. As you progress, you will apply hierarchical Bayesian models to evaluate A/B and multi-variant testing scenarios and gain practical experience organizing and preparing healthcare datasets using Microsoft Excel. You will analyze historical, demographic, predictive, and center-based trends, construct Bayesian probability tables, calculate joint probabilities, update prior beliefs with new evidence, and interpret predictive outcomes across repeated testing cycles. Designed for learners interested in Bayesian statistics, machine learning, A/B testing, and healthcare analytics, this course bridges statistical theory with practical implementation. Its structured, end-to-end approach takes you from the fundamentals of Bayesian inference through computational modeling and real-world applications, enabling you to confidently apply Bayesian methods for data-driven analysis, experimentation, and predictive decision-making.
0 reviews

View details

Develop practical quantitative finance skills by learning how to analyze, apply, and evaluate financial data using SAS. This course introduces the essential statistical techniques and SAS procedures used to interpret financial datasets, assess market behavior, and support data-driven financial decision-making. You will begin by exploring the SAS environment and applying descriptive statistics to financial data. As you progress, you will analyze datasets using T-Tests and correlation methods, then apply regression modeling and variance analysis to interpret statistical relationships and evaluate financial market behavior. Building on these foundations, you will use advanced SAS procedures to analyze economic datasets, forecast financial trends, and interpret real-world financial market data, including the BSE Sensex and foreign exchange markets. This course is designed for learners who want to strengthen their quantitative finance and financial data analysis skills using SAS. Its practical approach emphasizes applying statistical methods to real financial and economic scenarios rather than focusing on theory alone. By the end of the course, you will be able to analyze financial datasets, apply SAS procedures, interpret statistical outputs, forecast financial trends, and evaluate financial risks with greater confidence.
0 reviews

View details

Learners will apply Python programming to analyze financial data, interpret time-based trends, build regression models, and communicate insights through effective visualizations. By the end of this course, learners will be able to transform raw financial datasets into meaningful analytical outputs that support data-driven financial decisions. This course is designed to bridge the gap between Python programming and practical financial analytics. Learners gain hands-on experience with Python setup, essential libraries, DataFrame operations, and core analytical techniques before progressing to financial time series analysis, regression modeling, and advanced data visualization, including financial plots and 3D charts. Each concept is reinforced through structured lessons, practice quizzes, and graded assessments to ensure skill mastery. What makes this course unique is its end-to-end, finance-focused approach—moving from data preparation to modeling and visual storytelling—without assuming advanced programming or statistical knowledge. The course emphasizes real-world financial use cases, clarity in analysis, and interpretability of results. Upon completion, learners will be equipped with in-demand analytical skills applicable to finance, business analytics, and data-driven roles, making them more confident and competitive in today’s analytics-driven job market.