Employ advanced data structures. You are interested in building a more sophisticated set of examples for your code repository to support the implementation of grouping, filtering, and index based techniques upon frame data structures. In the interest of supporting extended modeling in the area of financial analytics, you are interested in creating examples of advanced techniques to perform side-by-side comparisons between the stock-based examples without having to manipulate the entire data structure. In this case you will apply an advanced grouping technique in order to reference stocks between each other without having to physically reconstruct the module. Using Python, take the Kaggle GAFA Stock prices. You will have to read in the four stocks and associated attributes into separate columns for each stock from a single data frame. Create an output file to be read in read in by R. Using R, use the Lattice or ggplot. Create plots of the stocks side-by-side. Additionally, calculate the mean and standard deviation of each stock between Amazon, Facebook, and Apple. Your specific deliverables are , including plots of each stock price (all in the same plot), and means and standard deviation of the stock. Data Sets https://learning.rasmussen.edu/bbcswebdav/pid-5842499-dt-content-rid-151631795_1/xid-151631795_1

In this assignment, the task is to employ advanced data structures to build a more sophisticated set of examples for the code repository. The goal is to support the implementation of grouping, filtering, and index-based techniques on frame data structures. The specific area of interest is financial analytics, where the objective is to create examples of advanced techniques for performing side-by-side comparisons between stock-based examples without manipulating the entire data structure.

To accomplish this task, we will use the Kaggle GAFA Stock prices dataset and implement the solution using Python and R. The dataset contains information about stocks from four companies: Google, Amazon, Facebook, and Apple. The dataset also includes associated attributes for each stock.

First, we will read in the four stocks and their associated attributes into separate columns for each stock from a single data frame. This can be done using Python by importing relevant libraries such as pandas and reading the dataset into a pandas DataFrame. We will then separate the stocks and their attributes into different columns based on their stock symbol.

Next, we will create an output file that can be read in R. This can be achieved by exporting the modified DataFrame into a CSV file format that R can easily import and work with.

Using R, we will utilize either the Lattice or ggplot package to create plots of the stocks side-by-side. These packages provide powerful visualization capabilities and will allow us to compare the stock prices visually.

Additionally, we need to calculate the mean and standard deviation of each stock between Amazon, Facebook, and Apple. This can be done using built-in functions in R such as mean() and sd(). By applying these functions to each stock individually, we can obtain the desired statistics for our analysis.

Finally, we need to deliver the requested outputs: plots of each stock price (all in the same plot) and the means and standard deviation of each stock. The plots can be saved as image files (e.g., in PNG or PDF format) for sharing and further analysis. The mean and standard deviation values can be printed or saved in a tabular format for easy interpretation.

In summary, this assignment requires utilizing advanced data structures, grouping techniques, and advanced visualization tools to create sophisticated examples for financial analytics. The datasets and tools mentioned, along with Python and R, will enable us to successfully complete this assignment and produce the desired deliverables.

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