To the Stars
Here are some links to useful documentation on Databases in Java (based on NetBeans IDE with GlassFish Server).
I was excited to find the following Data Literacy Skills self-assessment framework, for use as part of a continuing professional development programme, available via the Open University’s library services (OU login required). This URL links to the Masters level skillset; other lower levels are also available on the same page. Examples and links to further resources are also given.
While this framework is geared towards data literacy for research students, it’s still highly relevant and can be mapped over to finance- or IT-based roles. It’s a shame it appeared too late for inclusion in my last assignment (on Data Literacy for Accounting Professionals) but it will prove useful for projects which will come out of that piece of assessed work.
The data literacy link was buried within the continuing personal and professional development section of the post-grad preparatory material (MD500, OU login required). This free module has lots of really important study skills, research skills, advice on becoming an active learner and longer term careers resources too. (Yet more things for me to go through when I find a spare block of time!)
I’m trying to find time to set up automated tasks on twitter (not too successfully as I’m trying to do too many things at once). Here’s my original post on twitter mining: http://deborahroberts.info/2016/05/coding-101-part-12-extracting-data-with-json/
And here’s some useful links worth bookmarking:
Using Twitter lists: https://support.twitter.com/articles/76460#
Getting Tweets (REST API): https://dev.twitter.com/rest/reference/get/search/tweets
API Console: https://dev.twitter.com/rest/tools/console
Data analysis process:The process we’ve been using during the capstone course – and in line with the original Page/Brin search engine process – is to first collect the raw data and store it – unprocessed – into a holding database. From here we’ve gone on to clean up the data and save it in a more structured way in a new, relational database. This results in a smaller database which is quicker to search and retrieve data from. As I found when writing my own search engine application, these first two databases take a long time to retrieve the data, especially when the search engine’s reach is set widely. [Read more…] about D3.js and Data Visualisation
Getting even more complex – many-to-many relationships:Previously we looked at databases with one-to-many (and their converse, many-to-one) relationships using a Primary Key as the unique, auto-incrementing ID number in the One-Table (e.g. Recipe Type) and linking this through to the Foreign Key ID number in the Many-Table (e.g. Recipes). [Read more…] about Coding 101 (part 15) Databases and beyond
Starting out with databases:
Customer analytics – a recap:1. Start with the data: we need to ensure we have the right data, but we should make sure this is individual-level data – data held for each customer. Without it we can’t do effective customer analytics. So a big question for the company is: how to build the right infrastructure to collect that level of data, with that level of granularity?
2. Exploring the data: we shouldn’t start with big complex models, but look always to start simply with a basic exploration of the data. Even when we go on to use complex mathematical or statistical models, we still need to be able to understand the underlying data, to be able to sense check our explorations and predictions against that data which allows us confidence to assert, yes the underlying data does suggest/show that our modelled effects are real or reasonable. [Read more…] about Marketing Analytics: Applications & Innovations