Amazing innovation!

nteract is wonderful!

We know amazing innovation when we see it.  So here it is!

Use the nteract notebook instead of Jupyter.  Set the panda’s display option

pd.options.display.html.table_schema = True

and magic – there is now a data explorer available.  The graphics are pretty sweet.

Screenshot from 2018-08-17 19-19-29Screenshot from 2018-08-17 19-19-15Screenshot from 2018-08-17 19-18-45Screenshot from 2018-08-17 19-17-53

Go do something beautiful with this tool.  Innovate!

Ask Watson!

Continuing our text series we now wish to add a note about exploiting API (Application Programming Interfaces). We mentioned NLTK,  GENSIM,  spaCY previously and that really involves installing these packages either ‘on-premise’ or in the ‘cloud’ and dealing with dependencies, version changes, and any associated host and operating system issues.  The other option is to just ‘Ask Watson’.

So we did! The notebook is in our Github Repository

Here are some examples of the values returned by the API.  The job title was

‘Client Partner, eCommerce.’

Screen Shot 2018-08-13 at 22.12.19

The most relevant keywords (.97) is ‘Online Advertising experience’ which seems congruent.

Screen Shot 2018-08-13 at 22.12.09

The most relevant entity is ‘Facebook’ @ .91.   Our experiment was based on sending the entire text to Watson, and this represents our first interaction with the Watson NLU service.

 

 

 

 

 

 

 

 

 

 

 

ArrayFire with Python

A short note to follow up on a recent article about Julia.

At the time we wrote about a Julia wrapper for the ArrayFire library.  Now we have also evaluated the Python wrapper for the library.

This time we created a Gist on Github using  nteract

You can examine the entire Notebook utilising the Gist but here are some illustrative screenshots

Capture

Information about the default device

Capture1

Information about all the devices on the system

Capture2222222

Finally, a simple two-dimensional array,  sampled from the uniform distribution, with a matrix operation, computed on the Quadro_K1100M

 

Wall Street Data Science

Often times you might hear the term Finance Quant,  or Quant’s,  and then Data Scientist.

The widespread view of the ‘magical’ Data Scientist is usually presented using the image below.  From here

Screenshot from 2018-08-11 15-17-47

Finance Quantitative Analysts then surely must do Data Science.

Screenshot from 2018-08-11 15-24-11

As in all things we went investigating.  Now Wall Street guys are not likely to tell us their secrets so just sign up for a training course.  We did, and we really enjoyed it

https://www.datacamp.com/courses/intro-to-portfolio-risk-management-in-python

Screenshot from 2018-08-11 15-30-44

Take the course, and you will also see that the ‘Finance Quant’ world is not that scary. But I guess the instructor was also brilliant!