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.
Applying Data Analytics in Finance
Applying Data Analytics in Finance
Taught in English
Some content may not be translated
24,977 already enrolled
4.4
(211 reviews)
Intermediate level
Some related experience required
23 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the forecasting process
Describe time series data
Develop an ARIMA Model
Understand a basic trading algorithm
Details to know
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Assessments
21 quizzes
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There are 5 modules in this course
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.
What's included
4 videos5 readings1 quiz
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.
What's included
7 videos2 readings5 quizzes1 ungraded lab
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.
What's included
15 videos2 readings7 quizzes1 ungraded lab
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.
What's included
11 videos2 readings4 quizzes1 ungraded lab
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.
What's included
15 videos4 readings4 quizzes1 ungraded lab1 plugin
Instructors
Instructor ratings
4.6 (49 ratings)
Offered by
Recommended if you're interested in Leadership and Management
University of Illinois Urbana-Champaign
University of Maryland, College Park
University of Maryland, College Park
Duke University
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