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.
Applying Data Analytics in Finance
イリノイ大学アーバナ・シャンペーン校（University of Illinois at Urbana-Champaign）このコースについて
59,205 最近の表示
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約23時間で修了
英語
字幕：英語
学習内容
Understand the forecasting process
Describe time series data
Develop an ARIMA Model
Understand a basic trading algorithm
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約23時間で修了
英語
字幕：英語
提供：
イリノイ大学アーバナ・シャンペーン校（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.
修士号の取得を目指しましょう
この コース は イリノイ大学アーバナ・シャンペーン校（University of Illinois at Urbana-Champaign） の100%オンラインの Master of Science in Accountancy (iMSA) の一部です。
プログラムのすべてで認定されれば、それらのコースが学位学習に加算されます。
シラバス - 本コースの学習内容
2時間で修了
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時間で修了
10件のビデオ （合計48分）, 5 readings, 1 quiz
10件のビデオ
Interview with Jose Rodriguez6 分
Tour of R and RStudio5 分
Projects3 分
Math Function4 分
Scalar Variables6 分
Column Vectors9 分
Data Frame6 分
Data Frame Import2 分
Help and Cheat Sheets2 分
5件の学習用教材
Syllabus30 分
Glossary10 分
Update Your Profile10 分
About the Discussion Forums10 分
Data Download Tutorial10 分
1の練習問題
Orientation Quiz10 分
4時間で修了
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.
4時間で修了
6件のビデオ （合計44分）, 2 readings, 4 quizzes
6件のビデオ
Lesson 1-1.1 Subjective Forecasting6 分
Lesson 1-1.2 Business Forecasting and Time Series Data7 分
Lesson 1-2.1 Introduction to Financial Analytics10 分
Lesson 1-3.1 Forecasting Performance Measurements: Distance6 分
Lesson 1-3.2 Forecasting Performance Measurements: Metrics10 分
2件の学習用教材
Module 1 Overview20 分
Module 1 Readings1 時間 30 分
4の練習問題
Lesson 1-1 Practice Quiz10 分
Lesson 1-2 Practice Quiz10 分
Lesson 1-3 Practice Quiz10 分
Module 1 Quiz30 分
5時間で修了
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時間で修了
15件のビデオ （合計92分）, 2 readings, 7 quizzes
15件のビデオ
Lesson 2-1.1 Introduction to Forecasting: Average Method6 分
Lesson 2-1.2 Introduction to Forecasting: Naive Method3 分
Lesson 2-1.3 Introduction to Forecasting: Linear Regression13 分
Lesson 2-1.4 Introduction to Forecasting: R Example4 分
Lesson 2-2.1 Moving Averages6 分
Lesson 2-2.2 Moving Averages: R Example6 分
Lesson 2-3.1 Introduction to Exponential Smoothing5 分
Lesson 2-3.2 Simple Exponential Smoothing8 分
Lesson 2-3.3 Simple Exponential Smoothing: R Example5 分
Lesson 2-4.1 Holt's Exponential Smoothing7 分
Lesson 2-4.2 Holt-Winter's Forecasting Model4 分
Lesson 2-4.3 Holt-Winter's Model: R Example7 分
Lesson 2-5.1 Autoregression6 分
Lesson 2-5.2 Autoregression: R Example2 分
2件の学習用教材
Module 2 Overview20 分
Module 2 Readings7 分
6の練習問題
Lesson 2-1 Practice Quiz10 分
Lesson 2-2 Practice Quiz10 分
Lesson 2-3 Practice Quiz30 分
Lesson 2-4 Practice Quiz30 分
Lesson 2-5 Practice Quiz10 分
Module 2 Quiz30 分
5時間で修了
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時間で修了
10件のビデオ （合計54分）, 2 readings, 4 quizzes
10件のビデオ
Lesson 3-1.1 Stationarity: Introduction5 分
Lesson 3-1.2 Stationarity: Differencing11 分
Lesson 3-2.1 ARIMA: Introduction6 分
Lesson 3-2.2 ARIMA: Components7 分
Lesson 3-2.3 ARIMA: Model and R Example Part 17 分
Lesson 3-2.4 ARIMA: Model and R Example Part 24 分
Lesson 3-2.5 ARIMA: Model and R Example Part 31 分
Lesson 3-2.6 ARIMA: Model and R Example Part 43 分
Lesson 3-2.7 ARIMA: Model and R Example Part 54 分
2件の学習用教材
Module 3 Overview20 分
Module 3 Readings30 分
3の練習問題
Lesson 3-1 Practice Quiz30 分
Lesson 3-2 Practice Quiz30 分
Module 3 Quiz30 分
7時間で修了
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時間で修了
14件のビデオ （合計76分）, 2 readings, 4 quizzes
14件のビデオ
Lesson 4-1.1 Portfolio Theory: Introduction3 分
Lesson 4-1.2 Portfolio Theory: Expected Returns4 分
Lesson 4-1.3 Portfolio Theory: Risk of a Security6 分
Lesson 4-1.4 Portfolio Theory: Efficient Frontier6 分
Lesson 4-1.5 Portfolio Theory: Portfolio Weights7 分
Lesson 4-1.6 Portfolio Theory: Capital Allocation Line10 分
Lesson 4-1.7 Portfolio Theory: Diversification3 分
Lesson 4-2.1 Introduction to Algorithmic Trading7 分
Lesson 4-2.2 Introduction to Algorithmic Trading: Trend Following Strategy3 分
Lesson 4-2.3 Introduction to Algorithmic Trading: Backtesting6 分
Lesson 4-2.4 Introduction to Algorithmic Trading: R Example9 分
Lesson 4-2.5 Introduction to Algorithmic Trading: Conclusion1 分
Course Summary: Applying Data Analytics in Finance1 分
2件の学習用教材
Module 4 Overview20 分
Module 4 Readings1 時間
3の練習問題
Lesson 4-1 Practice Quiz30 分
Lesson 4-2 Practice Quiz30 分
Module 4 Quiz1 時間
レビュー
APPLYING DATA ANALYTICS IN FINANCE からの人気レビュー
Rにより2020年5月31日
Basics of R and other sub-topics need to be introduced before the topic is taken up in a video. Despite several ID verifications, I have not received the Course certificate till date.
MMにより2020年4月21日
I really enjoyed this course because I was able went deeper in topics that I reviewed in university but in a very slight way and now I have a more solid knowledge about that.
MKにより2020年4月16日
It is a very nice course. Very useful for learning basics of Financial Analytics. Prof. Kim's sessions were very nice.
VGにより2020年5月2日
Covers basics of time series modeling techniques and applications in R. Doesn't go deep into statistics behind models
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