Applying Data Analytics in Finance
Explore advanced techniques in financial modeling and portfolio management using cutting-edge data analytics tools and quantitative methods.
Explore advanced techniques in financial modeling and portfolio management using cutting-edge data analytics tools and quantitative methods.
This intermediate-level course introduces you to the application of data analytics in finance. You'll learn to analyze time series data, build forecasting models, and evaluate the risk-reward trade-off in modern portfolio theory. The course covers techniques for analyzing financial data, particularly stock prices and returns, but the skills are applicable to other domains. You'll explore forecasting processes, time series analysis, ARIMA modeling, and an introduction to algorithmic trading. By the end of the course, you'll be able to create and evaluate forecasts, build optimal portfolios using real stock price data, and understand the basics of algorithmic trading. This practical course equips analysts, managers, and consultants with essential skills for leveraging financial data in decision-making processes.
4.4
(211 ratings)
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English
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What you'll learn
Understand and apply various forecasting techniques in finance
Analyze time series data and evaluate forecast accuracy
Develop and interpret ARIMA models for financial forecasting
Apply concepts of modern portfolio theory to optimize investment portfolios
Evaluate risk-reward trade-offs in financial decision-making
Use R programming for financial data analysis and modeling
Skills you'll gain
This course includes:
4.5 Hours PreRecorded video
21 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course on Applying Data Analytics in Finance equips learners with practical skills to leverage data in financial decision-making. The curriculum is structured into five modules, covering a range of topics from basic forecasting techniques to advanced time series analysis and modern portfolio theory. Students begin by exploring the fundamentals of financial analytics and time series data, learning various forecasting methods and performance measures. The course then delves into more sophisticated analytical techniques, including Holt-Winters models and ARIMA (Autoregressive Integrated Moving Average) modeling. A significant portion of the course is dedicated to understanding and applying concepts from modern portfolio theory, teaching students how to balance risk and return in investment portfolios. The final module introduces the basics of algorithmic trading, providing insight into how data analytics is reshaping financial markets. Throughout the course, students gain hands-on experience using R for financial analysis, making the learning process both theoretical and practical. This course is ideal for finance professionals, data analysts, or anyone looking to enhance their quantitative skills in the financial domain.
Course Introduction
Module 1 · 1 Hours to complete
Introduction to Financial Analytics and Time Series Data
Module 2 · 5 Hours to complete
Performance Measures and Holt-Winters Model
Module 3 · 5 Hours to complete
Stationarity and ARIMA Model
Module 4 · 4 Hours to complete
Modern Portfolio Theory and Intro to Algorithmic Trading
Module 5 · 6 Hours to complete
Fee Structure
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Instructors
Associate Professor in Environmental Engineering
Sung Won Kim is an Associate Professor at the University of Illinois Urbana-Champaign, specializing in environmental engineering with a focus on hydrology and water resources. His research investigates the communication processes of inter-organizational collaboration for social change and community development, addressing critical issues such as affordable housing, human trafficking, and disaster recovery. Dr. Kim has published extensively in reputable journals and has contributed significantly to the understanding of water resource management through innovative modeling techniques and machine learning applications. His expertise in environmental challenges is complemented by his commitment to educating future leaders in the field, making him a vital asset to the academic community.
Innovator in Financial Technology and Education
Jose Luis Rodriguez is the Vice President of Business Intelligence at Midwest BankCentre in St. Louis, MO, and previously served as the Director of the Margolis Market Information Lab at the University of Illinois Urbana-Champaign. During his tenure at Illinois, he transformed the lab into a cutting-edge environment for data science and finance education, significantly enhancing the learning experience for students in the Master of Finance and iMBA programs. In addition to his administrative role, Rodriguez has been actively involved in teaching and continues to engage with students as an instructor. His innovative work in developing Virtual Reality and Augmented Reality applications for finance earned him the R.C. Evans Innovation Fellowship in 2020. With a strong commitment to integrating technology into financial education, Rodriguez plays a crucial role in preparing future business leaders for the complexities of the financial landscape.
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4.4 course rating
211 ratings
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