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Software Development Goody Bag

Software Development Goody Bag

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This post collates the main podcasts, blogs and other resources I have identified to allow me to Track the Leading Edge of Software Development (and associated subjects) throughout my study of the M813 module.

As someone completely new to this professional practice (albeit with related experience from recent work roles, in business analysis and ERP/business/finance systems development) I admit to feeling a bit overwhelmed by this idea of tracking the leading edge of software development, since I’m struggling just to keep up with the trailing edge at this moment in time! Nevertheless, I understand and appreciate the need to keep up to speed with the latest developments in the industry, and am looking forward to dedicating some time to reading/listening to more about the subject. [Read more…] about Software Development Goody Bag

Coding 101 (part 15) Databases and beyond

Coding 101 (part 15) Databases and beyond

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SQL-databases-with-many-to-many-relationshipsGetting even more complex – many-to-many relationships:
We’ve learnt that there are several different kinds of relationships in a database (dependent on what data we’re trying to model). When we map out our data model, we should try to capture each the relationships between each of the tables in the model. In database terminology, the nature of each table-to-table relationship is referred to as the cardinality of the relationship.

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

Coding 101 (part 14) Relational databases

Coding 101 (part 14) Relational databases

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relational-databasesStarting out with databases:
This section moves on to working with SQL databases (focussing on SQLite3) as well as delving into some data gathering, analysis and visualisation in Python. Why store the data? Well we probably want to build up data over time – maybe it’s coming from reviews of customer activity, or PR hits, or perhaps we’re scraping data from the web and the web crawler is continually replenishing its list of target URLs (hence going crawling some more). Or maybe we’re getting our data from an API which restricts our access on a rate-limiting basis so we can only run x queries today, then have to wait a while before we can make more requests. [Read more…] about Coding 101 (part 14) Relational databases

Marketing Analytics: Applications & Innovations

Marketing Analytics: Applications & Innovations

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rubiks-cubeCustomer analytics – a recap:
As we’ve seen in previous posts, there are five important elements to the process of marketing/customer analytics:

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

Data Visualisation: Network Graphs (work in progress)

Data Visualisation: Network Graphs (work in progress)

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networks-and-nodesThe 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)

Digital Marketing (Web) Metrics

Digital Marketing (Web) Metrics

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web-metricsLet’s take a first look at the branch of Marketing Analytics known as Digital Marketing Metrics (or Web Metrics), which sit within the category of Sales Metrics (see earlier posts here and here). That is, they relate specifically to sales, and their measurement and improvement leads to increased sales revenue for the business. They are also a kind of Dynamic Metric: one which is likely to change significantly in a short timeframe (within one month or less), and one which can be significantly impacted in the short term by actions the company can take now. For example, making small tweaks to the customer journey through the website may increase additional purchases, or shortening the number of steps, and hence time to checkout reducing cart abandonments, and so on. [Read more…] about Digital Marketing (Web) Metrics