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Applying Data Analytics in Finance

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Applying Data Analytics in Finance

Instructors: Sung Won Kim

28,667 already enrolled

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

223 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

93%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

223 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

93%

Most learners liked this course

What you'll learn

Understand the forecasting process
Describe time series data
Develop an ARIMA Model
Understand a basic trading algorithm

Skills you'll gain

Tools you'll learn

R Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

21 assignments

Taught in English

91%

of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

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There are 4 modules in this course

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.

Module details

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

11 videos7 readings6 assignments1 ungraded lab1 plugin

11 videosTotal 56 minutes

Coursera Course Introduction ***2 minutes
Instructor Bio: Jose Rodriguez ***3 minutes
Interview with Jose Rodriguez6 minutes
Learn on Your Terms1 minute
Module 1 Overview ***1 minute
Jose Rodriguez: Forecasting in Practice3 minutes
Lesson 1-1.1 Subjective Forecasting7 minutes
Lesson 1-1.2 Business Forecasting and Time Series Data7 minutes
Lesson 1-2.1 Introduction to Financial Analytics11 minutes
Lesson 1-3.1 Forecasting Performance Measurements: Distance6 minutes
Lesson 1-3.2 Forecasting Performance Measurements: Metrics10 minutes

7 readingsTotal 180 minutes

Syllabus30 minutes
Glossary10 minutes
Resources for R10 minutes
About the Discussion Forums10 minutes
Online Education at Gies College of Business10 minutes
Module 1 Overview20 minutes
Module 1 Readings90 minutes

6 assignmentsTotal 100 minutes

Orientation Quiz10 minutes
Lesson 1-1 Practice Quiz10 minutes
Lesson 1-2 Practice Quiz10 minutes
Lesson 1-3 Practice Quiz10 minutes
Module 1 Quiz30 minutes
Module 1 Lab Exercise Quiz30 minutes

1 ungraded labTotal 60 minutes

Financial Analytics Lab60 minutes

1 pluginTotal 15 minutes

Demographic Survey15 minutes

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 assignments1 ungraded lab

15 videosTotal 87 minutes

Module 2 Overview ***1 minute
Jose Rodriguez: Forecasting Models in Practice3 minutes
Lesson 2-1.1 Introduction to Forecasting: Average Method6 minutes
Lesson 2-1.2 Introduction to Forecasting: Naive Method4 minutes
Lesson 2-1.3 Introduction to Forecasting: Linear Regression ***14 minutes
Lesson 2-1.4 Introduction to Forecasting: R Example4 minutes
Lesson 2-2.1 Moving Averages8 minutes
Lesson 2-3.1 Introduction to Exponential Smoothing5 minutes
Lesson 2-3.2 Simple Exponential Smoothing8 minutes
Lesson 2-3.3 Simple Exponential Smoothing: R Example6 minutes
Lesson 2-4.1 Holt's Exponential Smoothing8 minutes
Lesson 2-4.2 Holt-Winter's Forecasting Model5 minutes
Lesson 2-4.3 Holt-Winter's Model: R Example7 minutes
Lesson 2-5.1 Autoregression7 minutes
Lesson 2-5.2 Autoregression: R Example3 minutes

2 readingsTotal 27 minutes

Module 2 Overview20 minutes
Module 2 Readings7 minutes

7 assignmentsTotal 150 minutes

Lesson 2-1 Practice Quiz10 minutes
Lesson 2-2 Practice Quiz10 minutes
Lesson 2-3 Practice Quiz30 minutes
Lesson 2-4 Practice Quiz30 minutes
Lesson 2-5 Practice Quiz10 minutes
Module 2 Quiz30 minutes
Module 2 Lab Exercise Quiz30 minutes

1 ungraded labTotal 60 minutes

Analytical Methods Lab60 minutes

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 assignments1 ungraded lab

11 videosTotal 55 minutes

Module 3 Overview ***1 minute
Jose Rodriguez: ARIMA in Practice2 minutes
Lesson 3-1.1 Stationarity: Introduction6 minutes
Lesson 3-1.2 Stationarity: Differencing11 minutes
Lesson 3-2.1 ARIMA: Introduction7 minutes
Lesson 3-2.2 ARIMA: Components8 minutes
Lesson 3-2.3 ARIMA: Model and R Example Part 18 minutes
Lesson 3-2.4 ARIMA: Model and R Example Part 24 minutes
Lesson 3-2.5 ARIMA: Model and R Example Part 32 minutes
Lesson 3-2.6 ARIMA: Model and R Example Part 43 minutes
Lesson 3-2.7 ARIMA: Model and R Example Part 54 minutes

2 readingsTotal 50 minutes

Module 3 Overview20 minutes
Module 3 Readings30 minutes

4 assignmentsTotal 120 minutes

Lesson 3-1 Practice Quiz30 minutes
Lesson 3-2 Practice Quiz30 minutes
Module 3 Quiz30 minutes
Module 3 Lab Exercise Quiz30 minutes

1 ungraded labTotal 60 minutes

ARIMA Models Lab60 minutes

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 assignments1 ungraded lab

15 videosTotal 77 minutes

Module 4 Overview ***1 minute
Jose Rodriguez: Portfolios in Practice4 minutes
Lesson 4-1.1 Portfolio Theory: Introduction3 minutes
Lesson 4-1.2 Portfolio Theory: Expected Returns4 minutes
Lesson 4-1.3 Portfolio Theory: Risk of a Security6 minutes
Lesson 4-1.4 Portfolio Theory: Efficient Frontier7 minutes
Lesson 4-1.5 Portfolio Theory: Portfolio Weights8 minutes
Lesson 4-1.6 Portfolio Theory: Capital Allocation Line11 minutes
Lesson 4-1.7 Portfolio Theory: Diversification3 minutes
Lesson 4-2.1 Introduction to Algorithmic Trading8 minutes
Lesson 4-2.2 Introduction to Algorithmic Trading: Trend Following Strategy4 minutes
Lesson 4-2.3 Introduction to Algorithmic Trading: Backtesting6 minutes
Lesson 4-2.4 Introduction to Algorithmic Trading: R Example9 minutes
Lesson 4-2.5 Introduction to Algorithmic Trading: Conclusion2 minutes
Course Summary: Applying Data Analytics in Finance1 minute

4 readingsTotal 100 minutes

Module 4 Overview20 minutes
Module 4 Readings60 minutes
Congratulations on completing the course!10 minutes
Get Your Course Certificate10 minutes

4 assignmentsTotal 150 minutes

Lesson 4-1 Practice Quiz30 minutes
Lesson 4-2 Practice Quiz30 minutes
Module 4 Quiz60 minutes
Module 4 Lab Exercise Quiz30 minutes

1 ungraded labTotal 60 minutes

Modern Portfolio Theory & Algorithmic Trading Lab60 minutes

Instructors

Instructor ratings

(55 ratings)

Sung Won Kim

University of Illinois Urbana-Champaign

1 Course28,667 learners

Offered by

University of Illinois Urbana-Champaign

Learner reviews

5 stars
65.47%
4 stars
23.76%
3 stars
3.58%
2 stars
4.03%
1 star
3.13%

Showing 3 of 223

Reviewed on Apr 16, 2020

It is a very nice course. Very useful for learning basics of Financial Analytics. Prof. Kim's sessions were very nice.

Reviewed on Jul 3, 2023

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.

Reviewed on May 13, 2020

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

View more reviews