Today i had the opportunity to attend the SQL Saturday Session in Charlotte today. It was a very well organized event with lot of topics ranging from SQL Development to Predictive Analytics. The interest in data science, big data and Predictive analytics seems to be growing rapidly. I attended some great sessions, first session i attended was R programming for SQL Developers presented by Kiran Math (Data Analytics Expert) from Greenville, SC. He currently works for a start up firm in Greenville, SC where he is working as a Data Analyst. He covered topics ranging from how to download R and R-Studio, did some comparisons between R and SQL Server in terms of commonalities around how data can be retrieved, filtered and aggregated. There was some coverage on the RODBC driver as well, this is the ODBC driver that can be used in R to connect to SQL Server Databases. In this blog post i would like to cover certain functions and packages in R that can be used for shaping the data and also removing bad values.The power of R language comes from the packages that are available. One of the packages is called dplyr, this can be installed by using the following commands:
Once the above package is downloaded and installed, the following commands are available:
To Select the data from a dataframe in R:
select - select(df,product,vendor) - selects the columns product,vendor from data frame df.
filter - filter(df,product=="cars") - selects the rows where the product is equal to cars
mutate - mutate(df, saleprice= qty * price) - here the column saleprice is created by calculating using qty and price. These commands are useful for data profiling and creating columns as categorical variables in the data frame. Categorical variables are really useful while doing the modeling process. There are other functions within the dplyr package which can be accessed in this link: https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html.
There is an another package called tidyr package which has lot of very useful functions, one such useful function is called gather: gather function takes multiple columns and collapses into key-value pairs, this really helps in shaping up the data.For example: let us say you want to compare sales prices of houses in different zip codes.
One of the other important packages is called ggplot2, it is a very important package that helps one to do advanced visualizations, this package relies on the concept of grammar of graphics where the visualizations are built by adding layers to enhance the plots. Please refer to the documentation here:
The session on R was very informative and there was a demo on how to determine the sale price of a house that is 2500 sqaure foot in a particular zip code.
The next session i attended was on PowerBI architecture, implementation and usage. Melissa Coates, a SQL Server BI Professional/Expert provided an excellent overview of the PowerBI architecture and how the product has evovled with different features available in the On-Premise/Desktop and the cloud versions. There were really neat features that were demonstrated and how the reports can be shared within the organization/group of users. There are a lot of options available within the product that can be leveraged very effectively within the organization..
The session on Data Mining in SSAS was very effectively presented by Mark Hudson , Data Analytics/Data Mining expert from Captech. The terminology related to data mining was clearly explained to the audience so that we could take forward the concepts into the actual data mining models. The concepts related to continuous and discrete variables, does cor-related variables really cause causation were effectively discussed. Predictive modeling/Data Mining aim to produce predictions and not guarantee's. Next a baseball data set that pretty sizeable was used to demonstrate the data mining models. Here the data mining model was built directly using a query on a relational table in sql server database. One of the requirements for Data mining models is that there has to be a unique key per table, no composite keys allowed. He used the baseball data in a table as a source for the data mining models. For the demo he touched upon Decision Tree, Clustering and Naive Bayes algorithms. Currently SSAS data mining comes with 9 algorithms and is available in MultiDimensional SSAS only. Once the data is pulled into a data source view, the mining structure is built based on the columns pulled in and the attribute that needs to be predicted is selected along with other input variables. The variables that are not needed for the models can be removed at this stage. Once these were completed 3 mining models were built based on the algorithms (Decision Tree, Clustering and Naive Bayes) and executed. Once the models were built the results can be analyzed in the Mining Model viewer within SSAS and those can be used to validate the data set. The difference between SSAS and R is that while SSAS is more graphical and UI driven, R Language provides more control on how the models can be constructed from ground up and does involve more coding.
Overall the event was a great success in terms of learning, sharing and meeting with other SQL Server Experts.