About this Course
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100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 25 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English

What you will learn

  • Check

    Understand the forecasting process

  • Check

    Describe time series data

  • Check

    Develop an ARIMA Model

  • Check

    Understand a basic trading algorithm

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 25 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English

Learners taking this Course are

  • Operations Managers
  • Accountants
  • Program Managers
  • Financial Analysts
  • Technical Solutions Engineers

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Course Introduction

10 videos (Total 48 min), 5 readings, 1 quiz
10 videos
Interview with Jose Rodriguez6m
Tour of R and RStudio5m
Projects3m
Math Function4m
Scalar Variables6m
Column Vectors9m
Data Frame6m
Data Frame Import2m
Help and Cheat Sheets2m
5 readings
Syllabus30m
Glossary10m
Update Your Profile10m
About the Discussion Forums10m
Data Download Tutorial10m
1 practice exercise
Orientation Quiz10m
4 hours to complete

Module 1: Introduction to Financial Analytics and Time Series Data

6 videos (Total 44 min), 2 readings, 4 quizzes
6 videos
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
4 practice exercises
Lesson 1-1 Practice Quiz10m
Lesson 1-2 Practice Quiz10m
Lesson 1-3 Practice Quiz10m
Module 1 Quiz30m
Week
2
5 hours to complete

Module 2: Performance Measures and Holt-Winters Model

15 videos (Total 92 min), 2 readings, 7 quizzes
15 videos
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 Regression13m
Lesson 2-1.4 Introduction to Forecasting: R Example4m
Lesson 2-2.1 Moving Averages6m
Lesson 2-2.2 Moving Averages: R Example6m
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
6 practice exercises
Lesson 2-1 Practice Quiz10m
Lesson 2-2 Practice Quiz10m
Lesson 2-3 Practice Quiz4m
Lesson 2-4 Practice Quiz8m
Lesson 2-5 Practice Quiz10m
Module 2 Quiz30m
Week
3
5 hours to complete

Module 3: Stationarity and ARIMA Model

10 videos (Total 54 min), 2 readings, 4 quizzes
10 videos
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
3 practice exercises
Lesson 3-1 Practice Quiz6m
Lesson 3-2 Practice Quiz12m
Module 3 Quiz30m
Week
4
7 hours to complete

Module 4: Modern Portfolio Theory and Intro to Algorithmic Trading

14 videos (Total 76 min), 2 readings, 4 quizzes
14 videos
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
3 practice exercises
Lesson 4-1 Practice Quiz30m
Lesson 4-2 Practice Quiz30m
Module 4 Quiz1h
4.5
5 ReviewsChevron Right

Top reviews from Applying Data Analytics in Finance

By SDSep 8th 2019

Great Course and excellent explanation by professor

Instructor

Avatar

Sung Won Kim

Associate Professor
Business Administration

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.

About 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. ...

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