About this Course

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100% online
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Flexible deadlines
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Intermediate Level
Approx. 23 hours to complete
English

What you will learn

  • Understand the forecasting process

  • Describe time series data

  • Develop an ARIMA Model

  • Understand a basic trading algorithm

Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 23 hours to complete
English

Offered by

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University of Illinois at Urbana-Champaign

Start working towards your Master's degree

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

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

Course Introduction

1 hour to complete
3 videos (Total 11 min), 4 readings, 1 quiz
3 videos
Instructor Bio: Jose Rodriguez ***2m
Interview with Jose Rodriguez6m
4 readings
Syllabus30m
Glossary10m
Resources for R10m
About the Discussion Forums10m
1 practice exercise
Orientation Quiz10m
5 hours to complete

Module 1: Introduction to Financial Analytics and Time Series Data

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

Week 2

5 hours to complete

Module 2: Performance Measures and Holt-Winters Model

5 hours to complete
15 videos (Total 87 min), 2 readings, 7 quizzes
15 videos
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
3

Week 3

5 hours to complete

Module 3: Stationarity and ARIMA Model

5 hours to complete
11 videos (Total 55 min), 2 readings, 4 quizzes
11 videos
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
4

Week 4

6 hours to complete

Module 4: Modern Portfolio Theory and Intro to Algorithmic Trading

6 hours to complete
15 videos (Total 77 min), 2 readings, 4 quizzes
15 videos
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
2 readings
Module 4 Overview20m
Module 4 Readings1h
4 practice exercises
Lesson 4-1 Practice Quiz30m
Lesson 4-2 Practice Quiz30m
Module 4 Quiz1h
Module 4 Lab Exercise Quiz30m

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