4.5
stars
190 ratings

Sung Won Kim +1 more instructor

17,580 already enrolled

Offered By

Part of the Master of Business Administration (iMBA) degree

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Applying Data Analytics in Finance

University of Illinois at Urbana-Champaign

About this Course

14,932 recent views

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 23 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

What you will learn

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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 23 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish

Instructors

Instructor rating

4.6/5 (47 Ratings)

Sung Won Kim

Associate Professor

Business Administration

17,580 Learners

1 Course

Jose Luis Rodriguez

Director of Margolis Market Information Lab

Department of Finance - Gies College of Business

17,929 Learners

2 Courses

Offered by

University of Illinois at Urbana-Champaign

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.

Start working towards your Master's degree

This course is part of the 100% online Master of Business Administration (iMBA) from University of Illinois at Urbana-Champaign. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week

Week 1

2 hours to complete

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.

2 hours to complete

4 videos (Total 12 min), 5 readings, 1 quiz

5 hours to complete

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.

5 hours to complete

7 videos (Total 45 min), 2 readings, 5 quizzes

7 videos

Module 1 Overview ***54s

Jose Rodriguez: Forecasting in Practice2m

Lesson 1-1.1 Subjective Forecasting6m

Lesson 1-1.2 Business Forecasting and Time Series Data7m

Lesson 1-2.1 Introduction to Financial Analytics10m

Lesson 1-3.1 Forecasting Performance Measurements: Distance6m

Lesson 1-3.2 Forecasting Performance Measurements: Metrics10m

2 readings

Module 1 Overview20m

Module 1 Readings1h 30m

5 practice exercises

Lesson 1-1 Practice Quiz10m

Lesson 1-2 Practice Quiz10m

Lesson 1-3 Practice Quiz10m

Module 1 Quiz30m

Module 1 Lab Exercise Quiz30m

Week

Week 2

5 hours to complete

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.

5 hours to complete

15 videos (Total 87 min), 2 readings, 7 quizzes

15 videos

Module 2 Overview ***37s

Jose Rodriguez: Forecasting Models in Practice2m

Lesson 2-1.1 Introduction to Forecasting: Average Method6m

Lesson 2-1.2 Introduction to Forecasting: Naive Method3m

Lesson 2-1.3 Introduction to Forecasting: Linear Regression ***13m

Lesson 2-1.4 Introduction to Forecasting: R Example4m

Lesson 2-2.1 Moving Averages7m

Lesson 2-3.1 Introduction to Exponential Smoothing5m

Lesson 2-3.2 Simple Exponential Smoothing8m

Lesson 2-3.3 Simple Exponential Smoothing: R Example5m

Lesson 2-4.1 Holt's Exponential Smoothing7m

Lesson 2-4.2 Holt-Winter's Forecasting Model4m

Lesson 2-4.3 Holt-Winter's Model: R Example7m

Lesson 2-5.1 Autoregression6m

Lesson 2-5.2 Autoregression: R Example2m

2 readings

Module 2 Overview20m

Module 2 Readings7m

7 practice exercises

Lesson 2-1 Practice Quiz10m

Lesson 2-2 Practice Quiz10m

Lesson 2-3 Practice Quiz30m

Lesson 2-4 Practice Quiz30m

Lesson 2-5 Practice Quiz10m

Module 2 Quiz30m

Module 2 Lab Exercise Quiz30m

Week

Week 3

5 hours to complete

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.

5 hours to complete

11 videos (Total 55 min), 2 readings, 4 quizzes

11 videos

Module 3 Overview ***43s

Jose Rodriguez: ARIMA in Practice2m

Lesson 3-1.1 Stationarity: Introduction5m

Lesson 3-1.2 Stationarity: Differencing11m

Lesson 3-2.1 ARIMA: Introduction6m

Lesson 3-2.2 ARIMA: Components7m

Lesson 3-2.3 ARIMA: Model and R Example Part 17m

Lesson 3-2.4 ARIMA: Model and R Example Part 24m

Lesson 3-2.5 ARIMA: Model and R Example Part 31m

Lesson 3-2.6 ARIMA: Model and R Example Part 43m

Lesson 3-2.7 ARIMA: Model and R Example Part 54m

2 readings

Module 3 Overview20m

Module 3 Readings30m

4 practice exercises

Lesson 3-1 Practice Quiz30m

Lesson 3-2 Practice Quiz30m

Module 3 Quiz30m

Module 3 Lab Exercise Quiz30m

Week

Week 4

7 hours to complete

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.

7 hours to complete

16 videos (Total 81 min), 4 readings, 4 quizzes

16 videos

Module 4 Overview ***49s

Jose Rodriguez: Portfolios in Practice4m

Lesson 4-1.1 Portfolio Theory: Introduction3m

Lesson 4-1.2 Portfolio Theory: Expected Returns4m

Lesson 4-1.3 Portfolio Theory: Risk of a Security6m

Lesson 4-1.4 Portfolio Theory: Efficient Frontier6m

Lesson 4-1.5 Portfolio Theory: Portfolio Weights7m

Lesson 4-1.6 Portfolio Theory: Capital Allocation Line10m

Lesson 4-1.7 Portfolio Theory: Diversification3m

Lesson 4-2.1 Introduction to Algorithmic Trading7m

Lesson 4-2.2 Introduction to Algorithmic Trading: Trend Following Strategy3m

Lesson 4-2.3 Introduction to Algorithmic Trading: Backtesting6m

Lesson 4-2.4 Introduction to Algorithmic Trading: R Example9m

Lesson 4-2.5 Introduction to Algorithmic Trading: Conclusion1m

Course Summary: Applying Data Analytics in Finance1m

Gies Online Programs3m

4 readings

Module 4 Overview20m

Module 4 Readings1h

Congratulations!10m

Get Your Course Certificate10m

4 practice exercises

Lesson 4-1 Practice Quiz30m

Lesson 4-2 Practice Quiz30m

Module 4 Quiz1h

Module 4 Lab Exercise Quiz30m

Reviews

4.5

36 reviews

5 stars
66.14%
4 stars
24.47%
3 stars
3.12%
2 stars
3.12%
1 star
3.12%

TOP REVIEWS FROM APPLYING DATA ANALYTICS IN FINANCE

by KKJan 8, 2021

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

by SDSep 8, 2019

Great Course and excellent explanation by professor

by JSJun 27, 2020

The study has detailed information of analytics in finance

by SGMay 14, 2020

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

View all reviews

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Applying Data Analytics in Finance

Applying Data Analytics in Finance