Financial Forecasting Using Big Data

Name: Financial Forecasting Using Big Data
Author: Beth Baligrosky

Learn how to analyze big data and use it for forecasting

Preview the first lesson free — get full access to all 8 lessons.

Course: On-Demand

(upbeat music) Let's go over two of the more common ways of using statistical methods to analyze some of the big data sets collected. These methods help businesses make decisions that affect the organization's activities. They're called linear and multiple regressions. Here are some reasons why you should choose to use regression analysis. It's a forecasting tool used to predict future outcomes. Regression analysis helps identify trends and relationships between at least two variables, but often more than two. It also helps you evaluate the variance made when one variable changes, compared to other variable changes. There are several types of regression analysis, but we will focus on linear and multiple. Linear regression is the simplest regression model to use. It involves comparing two variables using a straight line, hence the name linear. Multiple regression is a what-if approach that identifies relationships between at least two variables in your data. There will always be one variable that is dependent, and this variable does not change. The independent variables are the ones that change, as well as the correlations between the dependent and independent variables. Let's look at an example to understand these regression methods. Say you want to analyze the relationship between a company's daily stock prices and the daily trading volume. Using these two variables, you can complete a linear regression analysis. The daily stock price would be the dependent variable, and the daily trading volume, would be the independent variable. If you now add another variable, such as a daily change in market returns, your analysis now becomes a multiple regression analysis. Here are a few takeaways about regression analysis. Regression analysis is a common tool used in accounting, finance, and investing. Linear is the simplest form of regression analysis and is commonly used. Multiple regression is less common and requires a deeper understanding of dependent and independent variables. If you need expert help, contact a financial advisor. You've just learned two statistical methods you can use to analyze your data, and you understand how both linear and multiple regression can affect your business decisions.

Intermediate Provider : Beth Baligrosky 8 Lessons · 23m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

These lessons introduce best practices for processing and storing big data. They explore both forecasting for the future and analyzing earlier data. They also cover how to spot data inconsistencies, measure financial problems, and interpret your results. Finally, they introduce ways of testing for and correcting data variables.

Learning Objectives

What You'll Learn

Identify the advantages of qualitative and quantitative data modeling
Apply forecasting techniques to project future outcomes and analyze earlier data
Detect and correct inconsistent or anomalous data to maintain data integrity
Quantify financial problems and interpret your analysis results
Compare the five different data models used in data analysis
Perform financial regression analysis and process big data using best practices

Key Takeaways

The course introduces best practices for processing and storing big data.
It covers both forecasting for the future and analyzing earlier data.
Learners explore how to spot data inconsistencies, measure financial problems, and interpret results.
The lessons introduce ways of testing for and correcting data variables.
The course examines five different data models and the advantages of qualitative and quantitative data modeling.

Frequently Asked Questions

What does this course cover?

It introduces best practices for processing and storing big data, covers forecasting for the future and analyzing earlier data, and explains how to spot data inconsistencies, measure financial problems, and interpret results.

What skills will I gain from this course?

You will build skills in Data Analysis, Data Systems, Process Modeling, and Quantitative Modeling.

What topics are included in the lessons?

Lessons cover Financial Regression Analysis, Applied Forecasting with Data, Types of Data Analysis, Detecting Anomalies, Quantifying Financial Problems, Interpreting Results, and Data Integrity, followed by a Test Your Knowledge assessment.

What will I be able to do after completing this course?

You will be able to identify the advantages of qualitative and quantitative data modeling, understand the five different data models, and identify and correct inconsistent data.