University of Illinois at Urbana-Champaign
Applying Data Analytics in Finance
University of Illinois at Urbana-Champaign

Applying Data Analytics in Finance

Taught in English

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23,796 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Sung Won Kim
Jose Luis Rodriguez

Instructors: Sung Won Kim

4.5

(208 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 (48 ratings)
Sung Won Kim
University of Illinois at Urbana-Champaign
1 Course23,796 learners
Jose Luis Rodriguez
University of Illinois at Urbana-Champaign
2 Courses24,315 learners

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4.5

208 reviews

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5

Reviewed on May 13, 2020

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4

Reviewed on May 2, 2020

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5

Reviewed on May 3, 2020

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