UCI machine learning

The UCI maintains 413 datasets, as of 1/10/2018, for machine learning: http://archive.ics.uci.edu/ml/index.php. The following screenshot shows the top three downloaded datasets:

For the number one downloaded dataset called Iris, we have the following information:

The beauty of these datasets is that they give quite detailed information such as the source, the creator or donator, a description, and even citations.

The following table shows several potential public data sources for users in the area of data science and business analytics:

Table 3.1: Potential sources of open data for data science and business analytics

After we go to https://www.data.gov/, we can see the following choices related to Agriculture, Climate, Consumer, Ecosystems, Education, and the like:

The next table shows the potential sources of open data for users in the area of economics:

Table 3.2: Potential sources of open data for economics

After going to the Federal Reserve economic data and clicking Data on the menu, we can see the following entries:

The following table offers free data for users in the areas of finance and accounting:

Table 3.3: Potential sources of open data for finance and accounting

From Professor French's data library, we could download the famous Fama/French's three-factor time series:

> infile<-"http://canisius.edu/~yany/data/ff3monthly.csv" 
> x<-read.csv(infile,skip=3) 
> head(x,2) 
    Date Mkt.RF  SMB   HML   RF 
1 192607   2.96 -2.3 -2.87 0.22 
2 192608   2.64 -1.4  4.19 0.25 
> tail(x,2) 
       Date Mkt.RF   SMB   HML   RF 
1095 201709   2.51  4.53  3.02 0.09 
1096 201710   2.25 -1.94 -0.09 0.09 

In the previous code, the input file called ff3monthly.csv is a modified copy of F-F_Research_Data_Factor.csv made by removing the second part of the annual data and adding a Date as the header. Note that F-F_Research_Data_Factor.csv is from the ZIP file called F-F_Research_Data_Factor_CSV.zip.