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Sung Won Kim
and
Jose Luis Rodriguez
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course.
Read more
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course.
Read more
This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course.
After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant.
Register for this course and see more details by visiting:
OpenCourser.com/course/8vy03s/applying
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What's inside
Syllabus
Course Introduction
In this course, we will introduce a number of financial analytic techniques. You will learn why, when, and how to apply financial analytics in real-world situations. We will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risks of corporate stocks, the analytical techniques can be leveraged in other domains. Finally, a short introduction to algorithmic trading concludes the course.
Read more
Syllabus
Course Introduction
In this course, we will introduce a number of financial analytic techniques. You will learn why, when, and how to apply financial analytics in real-world situations. We will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risks of corporate stocks, the analytical techniques can be leveraged in other domains. Finally, a short introduction to algorithmic trading concludes the course.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Emphasizes understanding financial analytics, which is a sought-after skill in various fields
Provides practical guidance on applying financial analytics in real-world situations
Taught by experienced instructors with expertise in financial analytics
Covers a range of topics, including time series analysis, performance measures, and portfolio optimization
Requires a background in basic statistics and finance
Assumes some familiarity with R programming
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Reviews summary
Data analytics in finance using r
According to learners, this course offers a solid introduction to applying data analytics techniques within the finance domain, particularly leveraging the R programming language. Many students found the coverage of time series analysis, forecasting models (like ARIMA), and Modern Portfolio Theory to be particularly valuable and practical. The course is seen as providing a strong foundation for further study or application in financial roles. Some reviewers mentioned that having prior knowledge of R or basic statistics is helpful, as the R-specific instruction might be challenging for complete beginners. While the course covers a broad range of topics, including a brief intro to algorithmic trading, learners feel some areas could benefit from more depth or updated examples.
Introduces various techniques but some areas lack depth.
"The course touches upon many important areas like time series, MPT, and algo trading, which is great for an overview."
"I wish the algorithmic trading section was more than just an introduction; it felt too short."
"Some of the theoretical explanations were a bit brief and required me to look up external materials for deeper understanding."
Teaches finance analytics using hands-on R programming.
"The hands-on exercises in R were crucial for understanding how to actually implement the models discussed."
"Using R with real financial data was a great way to solidify the theoretical concepts."
"I enjoyed learning how to apply the ARIMA model specifically using R code provided in the course."
Course provides a valuable basis in key finance analytics areas.
"This course gave me a very solid foundation for applying data analytics techniques to financial problems."
"I really appreciated the coverage of time series analysis and forecasting models. It was explained well."
"The module on Modern Portfolio Theory was particularly helpful and provided practical insights I can use."
"Learned a lot about ARIMA and Holt-Winters models and their application in financial data."
Beginners in R may find the coding sections difficult.
"If you are new to R, be prepared to spend extra time on the coding assignments. Some parts were challenging without prior experience."
"I struggled a bit with the R portions as I had no background in the language. It assumes some familiarity."
"While the course uses R effectively, the instruction on R itself is minimal, requiring external resources for novices."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Applying Data Analytics in Finance with these
activities:
Review Basics of Time Series Analysis
Show steps
Review the basics of time series analysis to ensure a strong foundation for the course.
Browse courses on
Time Series Analysis
Show steps
-
Read an introductory article on time series analysis.
-
Watch a tutorial video on the topic.
-
Complete a few practice exercises on time series data.
Walkthrough of a Basic Financial Analytics Project
Show steps
Participate in a guided tutorial that walks through a basic financial analytics project to gain practical experience.
Browse courses on
Financial Data Analysis
Show steps
-
Identify a financial analytics project idea and collect the necessary data.
-
Follow the tutorial steps to analyze the data and build a financial model.
-
Present the findings and recommendations from the project.
Collaborate on Time Series Forecasting Models
Show steps
Engage in peer sessions to collaborate on developing and evaluating time series forecasting models, fostering teamwork and knowledge sharing.
Browse courses on
Time Series Forecasting
Show steps
-
Form a study group with peers.
-
Select a time series dataset to forecast.
-
Develop multiple forecasting models using different techniques.
-
Evaluate and compare the performance of the models.
-
Discuss the results and insights gained.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a Visual Guide to Portfolio Optimization Techniques
Show steps
Create a visual guide to portfolio optimization techniques, reinforcing understanding and providing a valuable resource for future reference.
Browse courses on
Portfolio Optimization
Show steps
-
Research and gather information on portfolio optimization techniques.
-
Design and create visual representations of the techniques.
-
Write explanatory text to accompany the visual guide.
-
Share the visual guide with peers or publish it online.
Solve Algorithmic Trading Practice Problems
Show steps
Engage in algorithmic trading practice problems to refine problem-solving skills and gain proficiency in applying analytical techniques.
Browse courses on
Algorithmic Trading
Show steps
-
Identify online platforms or textbooks with algorithmic trading practice problems.
-
Solve a set of practice problems regularly.
-
Analyze the solutions and identify areas for improvement.
Contribute to an Open-Source Financial Analytics Library
Show steps
Contribute to an open-source financial analytics library, gaining practical experience and enhancing understanding through collaboration.
Browse courses on
Open Source
Show steps
-
Identify an open-source financial analytics library to contribute to.
-
Review the library's documentation and codebase.
-
Identify areas where you can contribute.
-
Develop and submit a pull request with your contributions.
-
Collaborate with the library maintainers to refine your contributions.
Review Basics of Time Series Analysis
Show steps
Review the basics of time series analysis to ensure a strong foundation for the course.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Read an introductory article on time series analysis.
- Watch a tutorial video on the topic.
- Complete a few practice exercises on time series data.
Walkthrough of a Basic Financial Analytics Project
Show steps
Participate in a guided tutorial that walks through a basic financial analytics project to gain practical experience.
Browse courses on
Financial Data Analysis
Show steps
Show steps
Show steps
- Identify a financial analytics project idea and collect the necessary data.
- Follow the tutorial steps to analyze the data and build a financial model.
- Present the findings and recommendations from the project.
Collaborate on Time Series Forecasting Models
Show steps
Engage in peer sessions to collaborate on developing and evaluating time series forecasting models, fostering teamwork and knowledge sharing.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Form a study group with peers.
- Select a time series dataset to forecast.
- Develop multiple forecasting models using different techniques.
- Evaluate and compare the performance of the models.
- Discuss the results and insights gained.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a Visual Guide to Portfolio Optimization Techniques
Show steps
Create a visual guide to portfolio optimization techniques, reinforcing understanding and providing a valuable resource for future reference.
Browse courses on
Portfolio Optimization
Show steps
Show steps
Show steps
- Research and gather information on portfolio optimization techniques.
- Design and create visual representations of the techniques.
- Write explanatory text to accompany the visual guide.
- Share the visual guide with peers or publish it online.
Solve Algorithmic Trading Practice Problems
Show steps
Engage in algorithmic trading practice problems to refine problem-solving skills and gain proficiency in applying analytical techniques.
Browse courses on
Algorithmic Trading
Show steps
Show steps
Show steps
- Identify online platforms or textbooks with algorithmic trading practice problems.
- Solve a set of practice problems regularly.
- Analyze the solutions and identify areas for improvement.
Contribute to an Open-Source Financial Analytics Library
Show steps
Contribute to an open-source financial analytics library, gaining practical experience and enhancing understanding through collaboration.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source financial analytics library to contribute to.
- Review the library's documentation and codebase.
- Identify areas where you can contribute.
- Develop and submit a pull request with your contributions.
- Collaborate with the library maintainers to refine your contributions.
Career center
Learners who complete Applying Data Analytics in Finance will develop knowledge and skills
that may be useful to these careers:
Financial Analyst
Financial Analyst
Financial Analysts use various analytical techniques to evaluate investments and make recommendations to clients. The Applying Data Analytics in Finance course can be a valuable tool for those seeking to enter this field, as it provides a comprehensive overview of financial analytics and teaches students how to apply these techniques to real-world situations. The course covers topics such as time series analysis, portfolio optimization, and algorithmic trading, which are all essential knowledge for Financial Analysts.
Investment Banker
Investment Banker
Investment Bankers provide financial advice to corporations and governments, assisting them with mergers and acquisitions, capital raising, and other strategic transactions. The Applying Data Analytics in Finance course can be beneficial for those interested in a career in Investment Banking, as it provides a strong foundation in financial analysis and valuation techniques. The course also covers topics such as portfolio optimization and algorithmic trading, which are increasingly used by Investment Bankers in their work.
Portfolio Manager
Portfolio Manager
Portfolio Managers are responsible for managing investment portfolios for individuals and institutions. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a comprehensive overview of financial analytics and portfolio management techniques. The course covers topics such as time series analysis, risk and return measurement, and portfolio optimization, which are all essential knowledge for Portfolio Managers.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and managing financial risks within organizations. The Applying Data Analytics in Finance course can be helpful for those seeking to enter this field, as it provides a strong foundation in financial analysis and risk management techniques. The course covers topics such as time series analysis, risk and return measurement, and portfolio optimization, which are all essential knowledge for Risk Managers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in financial analysis and modeling techniques. The course covers topics such as time series analysis, risk and return measurement, and portfolio optimization, which are all essential knowledge for Quantitative Analysts.
Data Scientist
Data Scientist
Data Scientists use data analysis techniques to extract insights from data and solve business problems. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in data analysis and visualization techniques. The course also covers topics such as time series analysis and forecasting, which are increasingly used by Data Scientists in their work.
Financial Planner
Financial Planner
Financial Planners provide financial advice to individuals and families, helping them to achieve their financial goals. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in financial planning and analysis techniques. The course covers topics such as time series analysis, risk and return measurement, and portfolio optimization, which are all essential knowledge for Financial Planners.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty in the insurance and finance industries. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in financial analysis and risk management techniques. The course covers topics such as time series analysis, risk and return measurement, and portfolio optimization, which are all essential knowledge for Actuaries.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in data analysis and modeling techniques. The course also covers topics such as time series analysis and forecasting, which are increasingly used by Economists in their work.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in data analysis and visualization techniques. The course also covers topics such as time series analysis and forecasting, which are increasingly used by Statisticians in their work.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The Applying Data Analytics in Finance course may be useful for those seeking to enter this field, as it provides a strong foundation in data analysis and modeling techniques. The course also covers topics such as time series analysis and forecasting, which are increasingly used by Software Engineers in their work.
Data Analyst
Data Analyst
Data Analysts use data analysis techniques to extract insights from data and solve business problems. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in data analysis and visualization techniques. The course also covers topics such as time series analysis and forecasting, which are increasingly used by Data Analysts in their work.
Market Researcher
Market Researcher
Market Researchers study market trends and consumer behavior to help businesses make informed decisions. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in data analysis and visualization techniques. The course also covers topics such as time series analysis and forecasting, which are increasingly used by Market Researchers in their work.
Business Analyst
Business Analyst
Business Analysts use data analysis techniques to identify and solve business problems. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in data analysis and visualization techniques. The course also covers topics such as time series analysis and forecasting, which are increasingly used by Business Analysts in their work.
Consultant
Consultant
Consultants provide advice and expertise to businesses and organizations on a variety of topics. The Applying Data Analytics in Finance course can be useful for those seeking to enter this field, as it provides a strong foundation in data analysis and modeling techniques. The course also covers topics such as time series analysis and forecasting, which are increasingly used by Consultants in their work.
For more career information including salaries, visit:
OpenCourser.com/course/8vy03s/applying
Reading list
We've selected 15 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
Applying Data Analytics in Finance.
Provides a comprehensive introduction to time series analysis and forecasting. It covers a wide range of topics, including stationarity, ARIMA models, and forecasting methods. It valuable resource for anyone who wants to learn more about time series analysis.
Save
Comprehensive guide to forecasting. It covers a wide range of topics, including time series analysis, forecasting methods, and evaluation. It valuable resource for anyone who wants to learn more about forecasting.
Save
Provides a comprehensive introduction to algorithmic trading. It covers a wide range of topics, including trading strategies, risk management, and backtesting. It valuable resource for anyone who wants to learn more about algorithmic trading.
Save
Covers time series analysis methods with a focus on R programming. It valuable resource for learners who want to learn how to apply these techniques using R.
Provides a comprehensive introduction to modern portfolio theory. It covers a wide range of topics, including risk and return, asset allocation, and portfolio optimization. It valuable resource for anyone who wants to learn more about modern portfolio theory.
Covers advanced concepts in modern portfolio theory and investment analysis, including asset pricing models, portfolio optimization, and performance evaluation. It serves as a useful reference for those interested in learning more about this topic.
Combines theoretical concepts with practical examples using the R programming language. It recommended resource for learners who want to explore time series analysis in R.
Provides a comprehensive introduction to machine learning for asset managers. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection. It valuable resource for anyone who wants to learn more about machine learning for asset management.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection. It valuable resource for anyone who wants to learn more about statistical learning.
Provides an overview of algorithmic trading and direct market access (DMA) strategies. It valuable resource for learners who want to understand the practical aspects of algorithmic trading.
Provides a comprehensive introduction to data science. It covers a wide range of topics, including data wrangling, data analysis, and machine learning. It valuable resource for anyone who wants to learn more about data science.
Provides a comprehensive introduction to Python for data analysis. It covers a wide range of topics, including data wrangling, data analysis, and machine learning. It valuable resource for anyone who wants to learn more about Python for data analysis.
Provides a comprehensive introduction to machine learning for algorithmic trading. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection. It valuable resource for anyone who wants to learn more about machine learning for algorithmic trading.
Provides an accessible introduction to financial time series analysis techniques. It suitable resource for learners who are new to this field.
Provides a comprehensive introduction to statistics. It covers a wide range of topics, including probability, inference, and regression. It valuable resource for anyone who wants to learn more about statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8vy03s/applying
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Effort
4-6 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Instructors
Sung Won Kim
Jose Luis Rodriguez
Language
English
Transcripts
Español
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中文
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