debkr – Page 10

Coding 101 (part 4)

10/04/2016 By debkr

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)

Prescriptive Marketing Analytics: Strategy into Action

06/04/2016 By debkr

A quick refresher on the three kinds of analytics:
1. Descriptive: collects data which describes customers’ past actions and behaviours, and analyses it to try to find patterns which explain that behaviour;

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

Coding 101 (part 3)

04/04/2016 By debkr

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)

Predictive Marketing Analytics: Insights and Inferences

02/04/2016 By debkr

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

Coding 101 (part 2)

21/03/2016 By debkr

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)

Data Visualisation: Network Graphs (work in progress)

20/03/2016 By debkr

The following is inspired by (and based loosely on) a tutorial by data journalist Clara Guibourg: Network analysis of a Twitter hashtag using Gephi and NodeXL (hat-tip @KirkDBorne), worked up in a ‘Heath Robinson’ fashion since I don’t currently have Java or MS Excel installed on this laptop. (Java v.7+ is required to run Gephi, “the leading visualization and exploration software for all kinds of graphs and networks”; and, without Excel, not much use in trying to run the NodeXL Excel 2007+ template “that makes it easy to explore network graphs”.)

Graphs don’t just come in curves:
A standard Cartesian graph consists of a set of (x,y) co-ordinates (the points or vertices on the graph) and the relationship (the edges, arcs or lines) between them. The result is the graphed line, which may be also expressed as some algebraic function specifying the relationship (for example, in its simplest form: y = x). [Read more…] about Data Visualisation: Network Graphs (work in progress)

Most Influential Data Scientists (Two Lists)

20/03/2016 By debkr

While I was busy playing around with how to do a data visualisation on Twitter’s most influential data scientists, @kdnuggets went and posted this blog post about the exact same subject (dated 2012). But that’s cool, cos it saved me a bit of a job… (although I still need to do the learning exercise anyway, and update it to the list of influencers today, so that project’s still a work in progress).

The Twitter list (2012):
But in the meantime, here’s the list as produced by Gilad Lotan (@gilgul) based on Twitter bios containing relevant keywords (original post here) – the bios shown are taken from current Twitter bios: [Read more…] about Most Influential Data Scientists (Two Lists)

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