Applying Data Analytics in Finance from Coursera

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.

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.

Module 1: Introduction to Financial Analytics and Time Series Data

In this module, we will introduce an overview of financial analytics. Students 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 our 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.

Module 2: Performance Measures and Holt-Winters Model

We will introduce analytical methods to analyze time series data to build forecasting models and support decision-making. Students will learn how to analyze financial data that is usually presented as time series data. Topics include forecasting performance measures, moving average, exponential smoothing methods, and the Holt-Winters method.

Module 3: Stationarity and ARIMA Model

In this module, we will begin with stationarity, the first and necessary step in analyzing time series data. Students will learn how to identify if a time series is stationary or not and know how to make nonstationary data become stationary. Next, we will study a basic forecasting model: ARIMA. Students will learn how to build an ARIMA forecasting model using R.

Module 4: Modern Portfolio Theory and Intro to Algorithmic Trading

We will introduce some basic measurements of modern portfolio theory. Students will understand about risk and returns, how to balance them, and how to evaluate an investment portfolio.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Highly rated finance analytics course

Learners say this course is an engaging way to apply data analytics in finance. R users and finance professionals may find this course particularly useful. Students enjoyed applying time series modeling in R, engaging assignments, and found the information detailed and easy to understand. Some mention this course is more for beginners and may not be appropriate for advanced learners looking for in-depth theory. Overall, this course is well received by students.

Assumes some familiarity with R programming.

"Very nice combination of R programming, financial concepts and statistical concepts."

"The course was upto the mark and helped me to learn data analytics Application in finance."

"Basics of R and other sub-topics need to be introduced before the topic is taken up in a video."

Recommended for learners with a beginner's knowledge of the topics.

"A very nice course for beginners and intermediates"

"Very nice course. But we had certain issue of run of the code in jupyter."

"I really enjoyed this course because I was able went deeper in topics that I reviewed in university but in a very slight way and now I have a more solid knowledge about that."

Emphasizes practical application of time series modeling techniques in R.

"Useful knowledge and many skills of R code"

"Covers basics of time series modeling techniques and applications in R."

"The course delivers what it promises. Although R is not necessary to advance through the course, you can still learn a few things as you go and be well-equipped to move forward."

May not go into enough depth for advanced learners.

"should have clearly stated in the course introduction that the learner would need knowledge of R."

"Doesn't go deep into statistics behind models"

"the selection of topics was fine, but were discussed in very little detail."

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

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Review the basics of time series analysis to ensure a strong foundation for the course.

Browse courses on Time Series Analysis

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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

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Participate in a guided tutorial that walks through a basic financial analytics project to gain practical experience.

Browse courses on Financial Data Analysis

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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

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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

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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

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Show all six activities

Create a Visual Guide to Portfolio Optimization Techniques

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Create a visual guide to portfolio optimization techniques, reinforcing understanding and providing a valuable resource for future reference.

Browse courses on Portfolio Optimization

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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

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Engage in algorithmic trading practice problems to refine problem-solving skills and gain proficiency in applying analytical techniques.

Browse courses on Algorithmic Trading

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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

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Contribute to an open-source financial analytics library, gaining practical experience and enhancing understanding through collaboration.

Browse courses on Open Source

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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 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.

See salaries and explore the career path for Financial Analyst

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.

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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.

See salaries and explore the career path for Portfolio 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.

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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.

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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.

See salaries and explore the career path for Data Scientist

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Business Analyst

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.

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