To the Stars
Independent, UK: Man accidentally ‘deletes his entire company’ with one line of bad code. Let this be a warning to us all.
(Hat tip to @JamesGrundner and @Chuck_Moeller‘s DevOps Daily.)
PS: A sensible disaster recovery plan should always include non-connected back-up copies held offsite (certainly where it’s business-critical like it was in this case).
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
This post follows on from my earlier posts Coding 101 (part 1) and (part 2), and is my responses and learnings from the highly-recommended Python programming book and course by Charles Severance (see References below).
Functions:
Functions are sections of code (a sequence of executable steps) which we want to be able to use and re-use at many points in our program. It may be that we want to read and process a whole range of data over and over (but the process done to all the data is the same) or maybe there are a number of inputs required from the user which all need to be processed the same way. Rather than rewriting the same lines of code again and again in our program, we can give that section of code a name (known as ‘defining the function’). We can then ‘call’ that named function, that is, ask Python to execute the defined sequence of steps, at any future point within our program, and as many times as we want. (In other programming languages this same functionality may be referred to as sub-programs or sub-routines.) [Read more…] about Coding 101 (part 3)
In an earlier post we took a look at how we can measure and track customer behaviour with the descriptive branch of customer analytics. Now let’s turn our attention to predictive analytics, where we use that data to generate insights and infer possible future outcomes, e.g. what ways are customers likely to have behave in future, based on what they did in the past.
Source data:
The wide array of data which could possibly be collected from a consumer includes a whole variety of different things. Firstly, the ads or other marketing/promotional pieces they viewed (including where and when). Second, the promotional offers and other communications they received (including the media by which that communication was made e.g. online, by email, by mail). Third, any actions the customer took – including the [Read more…] about Predictive Marketing Analytics: Insights and Inferences
This post follows on from my earlier post Coding 101, and is my responses and learnings from the highly-recommended Python programming book and course by Charles Severance (see References below).
Jargon:
I’m working on a glossary here, but still very unstructured and massively incomplete so I suggest staying with Google for now .
Some necessary concepts:
Programming consists of sentences or statements, which may include reserved words, which tell Python what we want it to do, but will also include some values in both numerical and text formats. These take the form of either constants, whose values don’t change, and variables, [Read more…] about Coding 101 (part 2)