To the Stars
My simple Python Tagging Engine has now been updated to incorporate learnings about tuples, and incorporates simplified ways of sorting and returning counted values from a dictionary. Code and simple documentation available on GitHub: https://github.com/debkr/blog_tagger.
Lists work great but they leave something on the table:I got round this problem by having two lists, one for the words and another for the word counts. I could then manipulate the data as needed. This did work fine in the simple program I wrote, but it was my usual unwieldy, sledgehammer approach again. I knew there was a way I could handle that pair of data points better – using Python’s Dictionaries functionality – but I didn’t want to rush ahead of the curve. Well now I get the chance to learn all about dictionaries. [Read more…] about Coding 101 (part 7)
When strings become spaghetti:
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
This post follows on from earlier Coding 101 posts and records my responses and learnings from the highly-recommended Python programming book and Coursera specialisation by Charles Severance (see References below).
Opening data files:
In all our previous examples we used data as a constant (i.e. hard-coded into the program some way) or we prompted the user to enter some data which was then manipulated by the program in some way. We want to be able to read data from a variety of sources though – either from files, or from the web – and we know these are going to be much larger data sources, so we’ll need to be able to access and save files on our hard drive somewhere. [Read more…] about Coding 101 (part 5)
This post follows on from earlier posts (Coding 101 (part 1) ~ (part 2) ~ (part 3)) and records my responses and learnings from the highly-recommended Python programming book and Coursera specialisation by Charles Severance (see References below).
A quick recap on strings:
Strings are computer-speak for characters, specifically where some object or value has the ‘type’ string. Type is an attribute Python applies to any given object or value so it knows how to handle that object or value, i.e. what kinds of operations can and cannot be applied to it. String, and two numeric types – integer and float – are the most common types within Python.
A string may contain one or more characters, so ‘a’ and ‘0’ are strings, just as ‘abcdefghij’ and ‘Hello world. I am Python.’ are. [Read more…] about Coding 101 (part 4)
A quick refresher on the three kinds of analytics:2. Predictive: takes the historical descriptive data and patterns contained within it and uses that to try to predict what those customer might do in the future;
3. Prescriptive: takes a more strategic approach and uses data, trends and predictions about customer behaviours to help inform recommendations on actions (such as process changes, changes in marketing tactics) which might help change the customers’ behaviours and move the company closer to achieving it’s business goals. [Read more…] about Prescriptive Marketing Analytics: Strategy into Action