Book Image

Developing Financial Analysis Tools [Video]

By : Atmajitsinh Gohil
Book Image

Developing Financial Analysis Tools [Video]

By: Atmajitsinh Gohil

Overview of this book

<p><span id="description" class="sugar_field">As most of the data on the web or residing in a database is not structured in the right way, the course will assist viewers in developing skills to manipulate, transform, and evaluate raw input data. Through the concept of tidy data and visualization tools, viewers will be able to analyze trends and study the financial markets. </span></p> <p><span id="description" class="sugar_field">Once users have developed a good understanding of financial markets and financial data, the next three sections (3, 4, and 5) will introduces users to the concepts of basic statistics, time series analysis, and forecasting. Viewers will use a variety of basic R functions and forecast package to understand statistics and perform time series analysis.</span></p> <p><span id="description" class="sugar_field">By the end of this volume users will be able to use R, learn the use of Shiny apps, understand the concept of tidy data, and generate R markdown files for sharing information.</span></p> <h2><span class="sugar_field">Style and Approach</span></h2> <p><span class="sugar_field"><span id="trade_selling_points_c" class="sugar_field">This course is shows how to build finance models and helps analyst solve their quantitative finance problems.</span></span></p>
Table of Contents (5 chapters)
Chapter 3
Basic Statistics Using R
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Section 1
Summarizing Financial Data in R
In this video, we will understand the importance of Exploratory Data Analysis and learn to use some of the important functions from the dplyr package. - Understand the importance of exploratory data analysis as well as data visualization techniques for summarizing financial data - Explain how different statistical measures can be used to gain an insight into our data using descriptive statistics - Learn to perform grouping and calculate descriptive statistics at a portfolio level using functions from the dplyr package