To the Stars
Here are some links to useful documentation on Databases in Java (based on NetBeans IDE with GlassFish Server).
How We Find Out Things is important. Intelligence is the ability to find things out about a situation or subject, and to apply what we have previously found out to a new or novel situations or subjects.
Machine Learning does this through methods of Computational Statistics. It is powerful, but we still need to be there applying our own Intelligence & Sense-making abilities to ensure the results are logical & consistent with what we would expect.
As Ramon y Cajal, Spanish biologist and father of modern neuroscience, noted in his excellent book of advice for novice scientific researchers (1999), the key stages of a scientific investigation are (1) observation, (2) experimentation, (3) working hypotheses, and (4) proof. [Read more…] about How We Find Out Things
3e Project: Model evaluation & validation
Project details
DESCRIPTION:
PROCESS:
[Read more…] about MLND Project: Model Evaluation & Validation
goal = 'career-change'
reason = 'https://twitter.com/Qwiery/status/727849124138192896'
# PTP: Programming Stream (url = 'http://deborahroberts.info/2016/02/diving-into-data-syllabus-2/')
longlist = ['Java', 'Python', 'SQL', 'VBA', 'R']
start_with_end_in_mind = {'datascience': ['R', 'Python'], 'machinelearning': ['Python', 'R']}
choice1 = start_with_end_in_mind.get('datascience')
choice2 = start_with_end_in_mind.get('machinelearning')
shortlist = list()
for item in choice2 :
if item in choice1 : shortlist.append(item)
quit()
# Due to unorthodoxy I executed the last line just after line 02
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
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
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)