Applying Data Analytics in Finance

University of Illinois at Urbana-Champaign

This comprehensive course, "Applying Data Analytics in Finance," offered by the University of Illinois at Urbana-Champaign, delves into the realm of financial analytics, providing a detailed understanding of its application in real-world scenarios. The course encompasses various modules, each delving into crucial aspects of financial analytics and data interpretation, empowering learners to harness these skills effectively.

Through this course, participants will gain insights into the forecasting process, time series data analysis, and the development of ARIMA models. Furthermore, the course explores performance measures, Holt-Winters models, stationarity, and introduces learners to modern portfolio theory and algorithmic trading. The comprehensive curriculum is designed to enhance analytical skills, enabling participants to understand, interpret, and apply financial data effectively in their roles as analysts, managers, or consultants.

  • Gain a deep understanding of financial analytics and its real-world applications.
  • Master time series data analysis and forecasting techniques.
  • Develop ARIMA models and understand their components.
  • Explore performance measures, Holt-Winters models, and stationarity.
  • Learn about modern portfolio theory and algorithmic trading.

This course is ideal for individuals seeking to enhance their analytical capabilities in finance and data interpretation, with a focus on practical applications in various domains.

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Applying Data Analytics in Finance
Course Modules

Explore the course modules, which cover a range of topics including forecasting, time series data analysis, ARIMA models, performance measures, modern portfolio theory, and algorithmic trading, providing a comprehensive understanding of financial analytics.

Course Introduction

This introductory module provides an overview of the course, along with essential information about the instructor and resources available for learning. Participants will gain insights into the course structure and expectations, preparing them for the learning journey.

Module 1: Introduction to Financial Analytics and Time Series Data

Module 1 delves into financial analytics and time series data, providing a foundational understanding of subjective forecasting, business forecasting, and forecasting performance measurements. Participants will also engage in lab exercises to reinforce their learning.

Module 2: Performance Measures and Holt-Winters Model

Module 2 focuses on performance measures and the Holt-Winters model, covering topics such as moving averages, exponential smoothing, and autoregression. Participants will engage in practical exercises to apply these concepts.

Module 3: Stationarity and ARIMA Model

Module 3 explores stationarity and the ARIMA model, delving into the components of ARIMA and its application. Participants will engage in lab exercises to enhance their understanding and application of ARIMA models.

Module 4: Modern Portfolio Theory and Intro to Algorithmic Trading

Module 4 provides an in-depth understanding of modern portfolio theory and introduces learners to algorithmic trading, covering topics such as portfolio theory, risk assessment, efficient frontier, and trend following strategies. Participants will also engage in practical exercises to reinforce their learning.

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