Applying Data Analytics in Finance

Applying Data Analytics in Finance

Instructors: Sung Won Kim

Access provided by Interbank

28,403 already enrolled

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

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Assessments

21 assignments

Taught in English

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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.

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 videos Total 56 minutes

Coursera Course Introduction *** 2 minutes
Instructor Bio: Jose Rodriguez *** 3 minutes
Interview with Jose Rodriguez 6 minutes
Learn on Your Terms 1 minute
Module 1 Overview *** 1 minute
Jose Rodriguez: Forecasting in Practice 3 minutes
Lesson 1-1.1 Subjective Forecasting 7 minutes
Lesson 1-1.2 Business Forecasting and Time Series Data 7 minutes
Lesson 1-2.1 Introduction to Financial Analytics 11 minutes
Lesson 1-3.1 Forecasting Performance Measurements: Distance 6 minutes
Lesson 1-3.2 Forecasting Performance Measurements: Metrics 10 minutes

7 readings Total 180 minutes

Syllabus 30 minutes
Glossary 10 minutes
Resources for R 10 minutes
About the Discussion Forums 10 minutes
Online Education at Gies College of Business 10 minutes
Module 1 Overview 20 minutes
Module 1 Readings 90 minutes

6 assignments Total 100 minutes

Module 1 Quiz 30 minutes
Module 1 Lab Exercise Quiz 30 minutes
Orientation Quiz 10 minutes
Lesson 1-1 Practice Quiz 10 minutes
Lesson 1-2 Practice Quiz 10 minutes
Lesson 1-3 Practice Quiz 10 minutes

1 ungraded lab Total 60 minutes

Financial Analytics Lab 60 minutes

1 plugin Total 15 minutes

Demographic Survey 15 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 videos Total 87 minutes

Module 2 Overview *** 1 minute
Jose Rodriguez: Forecasting Models in Practice 3 minutes
Lesson 2-1.1 Introduction to Forecasting: Average Method 6 minutes
Lesson 2-1.2 Introduction to Forecasting: Naive Method 4 minutes
Lesson 2-1.3 Introduction to Forecasting: Linear Regression *** 14 minutes
Lesson 2-1.4 Introduction to Forecasting: R Example 4 minutes
Lesson 2-2.1 Moving Averages 8 minutes
Lesson 2-3.1 Introduction to Exponential Smoothing 5 minutes
Lesson 2-3.2 Simple Exponential Smoothing 8 minutes
Lesson 2-3.3 Simple Exponential Smoothing: R Example 6 minutes
Lesson 2-4.1 Holt's Exponential Smoothing 8 minutes
Lesson 2-4.2 Holt-Winter's Forecasting Model 5 minutes
Lesson 2-4.3 Holt-Winter's Model: R Example 7 minutes
Lesson 2-5.1 Autoregression 7 minutes
Lesson 2-5.2 Autoregression: R Example 3 minutes

2 readings Total 27 minutes

Module 2 Overview 20 minutes
Module 2 Readings 7 minutes

7 assignments Total 150 minutes

Module 2 Quiz 30 minutes
Module 2 Lab Exercise Quiz 30 minutes
Lesson 2-1 Practice Quiz 10 minutes
Lesson 2-2 Practice Quiz 10 minutes
Lesson 2-3 Practice Quiz 30 minutes
Lesson 2-4 Practice Quiz 30 minutes
Lesson 2-5 Practice Quiz 10 minutes

1 ungraded lab Total 60 minutes

Analytical Methods Lab 60 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 videos Total 55 minutes

Module 3 Overview *** 1 minute
Jose Rodriguez: ARIMA in Practice 2 minutes
Lesson 3-1.1 Stationarity: Introduction 6 minutes
Lesson 3-1.2 Stationarity: Differencing 11 minutes
Lesson 3-2.1 ARIMA: Introduction 7 minutes
Lesson 3-2.2 ARIMA: Components 8 minutes
Lesson 3-2.3 ARIMA: Model and R Example Part 1 8 minutes
Lesson 3-2.4 ARIMA: Model and R Example Part 2 4 minutes
Lesson 3-2.5 ARIMA: Model and R Example Part 3 2 minutes
Lesson 3-2.6 ARIMA: Model and R Example Part 4 3 minutes
Lesson 3-2.7 ARIMA: Model and R Example Part 5 4 minutes

2 readings Total 50 minutes

Module 3 Overview 20 minutes
Module 3 Readings 30 minutes

4 assignments Total 120 minutes

Module 3 Quiz 30 minutes
Module 3 Lab Exercise Quiz 30 minutes
Lesson 3-1 Practice Quiz 30 minutes
Lesson 3-2 Practice Quiz 30 minutes

1 ungraded lab Total 60 minutes

ARIMA Models Lab 60 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 videos Total 77 minutes

Module 4 Overview *** 1 minute
Jose Rodriguez: Portfolios in Practice 4 minutes
Lesson 4-1.1 Portfolio Theory: Introduction 3 minutes
Lesson 4-1.2 Portfolio Theory: Expected Returns 4 minutes
Lesson 4-1.3 Portfolio Theory: Risk of a Security 6 minutes
Lesson 4-1.4 Portfolio Theory: Efficient Frontier 7 minutes
Lesson 4-1.5 Portfolio Theory: Portfolio Weights 8 minutes
Lesson 4-1.6 Portfolio Theory: Capital Allocation Line 11 minutes
Lesson 4-1.7 Portfolio Theory: Diversification 3 minutes
Lesson 4-2.1 Introduction to Algorithmic Trading 8 minutes
Lesson 4-2.2 Introduction to Algorithmic Trading: Trend Following Strategy 4 minutes
Lesson 4-2.3 Introduction to Algorithmic Trading: Backtesting 6 minutes
Lesson 4-2.4 Introduction to Algorithmic Trading: R Example 9 minutes
Lesson 4-2.5 Introduction to Algorithmic Trading: Conclusion 2 minutes
Course Summary: Applying Data Analytics in Finance 1 minute