As I continue on my #datascience, #bigdata and #ai journey, I am pleased to have just completed my 6th course on the Microsoft Professional Program for Big Data with 84%: Delivering a Data Warehouse in the Cloud.
There are 4 more courses left on the program, and I am still on track to complete the program by my target of the end of January 2019.
As I continue on my #datascience, #bigdata and #ai journey, I am very pleased to have just completed my 5th course on the Microsoft Professional Program for Big Data with 98%: Introduction to NoSQL Data Solutions.
There are 5 more courses left on the program, and this means I am still on track to complete the program by my target of the end of January 2019.
I am delighted to have received a President’s Award for input on data science from outgoing Institute and Faculty of Actuaries President Marjorie Ngwenya, FIA at yesterday’s AGM at Staple Inn in London.
The IFoA is a tremendously vibrant organisation and I believe IFoA and other actuaries have an important role to play in helping businesses and organisations make the most from the torrents of data becoming available, whilst also helping protect consumers from unethical use of such data. In particular, I am very pleased that the IFoA is collaborating with the Royal Statistical Society in the vital area of the ethical use of data in data science. (For example a joint event was held earlier this month on the Industrialisation and Professionalisation of Data Science)
While looking at a (relatively small, 1.7 million records) big data example of New York Yellow Cab taxi trips, I am coming to the conclusion that the best place (if as we do you are using Microsoft tools) for initial analysis, including the all important first step of finding outliers/errors, is Azure Machine Learning Studio (Azure ML, as opposed to Excel, Power BI or bespoke analysis using e.g. Kendo UI).
Why Azure ML for initial analysis?
- It loads data quite quickly (e.g. just over a minute to import almost 2 million records from an Azure SQL database). This is currently much quicker than Power BI.
- It automatically produces histograms and box plots of numeric fields (see the images below, and above, where the field FareAmount has been selected). We can tell immediately from the box plot that there are several outliers (and in fact probable errors that will need to be either corrected or removed, in that FareAmount should not have negative values!).
The above screenshot shows an initial analysis (in Microsoft Power BI) of 1,723,099 records of New York taxi trip records uploaded to the cloud. The top chart shows a scatter plot of Trip Distance in miles against the Total Fare Amount (in US $). This useful chart shows straightaway that there are some outliers in the data (e.g. some trips cost over $1,000 despite being only for short distances). These records are almost certainly errors (where e.g. the fare was entered with the decimal point in the wrong place, e.g. $1000.00 instead of $10.00) and should be corrected or removed. Similar errors in the Trip Distance fields had already been removed in that 2 records had implausible distance values (e.g. 300,833 miles for a total fare of $14.16, and 1,666 miles for a total fare of $10.30).
In order to analyse big data, it often needs to be moved from its original sources (e.g. separate csv or txt files, or a stream) to somewhere where it can be collated and processed (e.g. an online database, or Microsoft PowerBI, or an xdf, extensible data format, file that can be analysed by Microsoft R Server).