Categories
Data Engineering

Explain what gradient-based optimization is about.

In few sentences, explain what gradient-based optimization is about.
Describe what makes gradient-based optimization problematic.

Categories
Data Engineering

Explain how would you use the skewness

This project reflects the basics of data distribution. The project topics relate to the definitions of variance and skewness.
Files needed for the project are attached.
Cover in the project the following:
1.Explain the variance and skewness
a.Show a simple example of how to calculate variance and then explain the meaning of it.
b.Show a simple example of how to calculate skewness and then explain the meaning of it.
2.After loading dataG2.csv into R (or software of your choice), explain the meaning of each column or what the attributes explain. Columns are for skewness, median, mean, standard deviation, and the last price (each row describes with the numbers the distribution of the stock prices):
3.Draw your own conclusions based on what you learned under 1. and 2.
a.Explain the meaning of variables ‘I_1’ and ‘I_2’ after you execute (after dataG2.csv is loaded in R or Octave)
imported_data <- read.csv(“dataG2.csv”)
S=imported_data[,5]-imported_data[,3]
I_1 =which.min(S) # use figure I_1 (see attached)
I_2 = which.max(S) # use figure I_2 (see attached)
b.Based on the results in a., which row (stock) would you buy and sell and why (if you believe history repeats)?
c.Explain how would you use the skewness (first column attribute) to decide about buying or selling a stock.
4.If you want to decide, based on the historical data, which row (stock) to buy or sell, would you base your decision on skewness attribute (1st column) or the differences between the last prices with mean (differences between 5th attribute and 3rd attribute)? Explain.