MapReduce, Linear Regression

 

Describe what MapReduce, Linear Regression are in detail, and explain where you would use them. 2. Build and implement neural network models – CNN and RNN – using 3.TensorFlow, then compare the two with your examples. List and compare statistical software packages.

 

Sample Solution

Map-Reduce, Linear Regression

Sometimes, with big data, matrices are too big to handle, and it is possible to use tricks to numerically still do the map. Map-reduce is one of those. With several cores, it is possible to split the problem, to map on each machine, and then to aggregate it back at the end. In statistics, linear regression is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables. Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable being predicted is called the dependent variable. Statistical software packages such as Microsoft excel and Statistical Package for the Social Sciences [S.P.S.S], are collections of software designed to aid in statistical analysis and data exploration. The vast majority of quantitative and statistical analysis relies upon statistical packages for its execution.

achers are under increasing pressure to compile data, which detracts from teaching itself, and teach to the test. National boards, like OFSTED in the UK, are now moving away from the statistical ‘evidence of progress’ towards inspections focussed on the quality of teaching and learning in the classroom and we are seeing similar trends worldwide.
In summary – for centralised and developing education systems the next three to five years will see:
1. The need for standardised and central administrative control over resources, content and the implementation of technology in the classroom.
2. An increasing focus on Technical and Vocational Education and a growing demand for effective systems to assess complex skills and competencies.
3. A focus on the need to train and support teachers with content, online communities and ‘how to teach’ guides.
4. A pressing need for the standardisation of assessment in the classroom, both summative and formative.
De-centralised Education Systems and trends in pedagogy
In de-centralised education systems, and in developed economies, we are seeing a continuing move towards enquiry-based learning and the notion of self-aware students and self-aware practitioners. This refers to the emphasis on teaching students how they learn, and how to plan and organise their own learning (a good example is the work of the highly influential educator John Hattie and his Visible Learning programme which is being increasingly adopted worldwide).
This means that future technology will need to support students who are developing their own portfolio of skills and competencies, and who will be learning through projects that encompass a range of subjects. We are also seeing a shift towards Blended Learning, combining experiential education with technology, so that the latter becomes one tool among many, and to ensure that physical experience (making things, doing experiments with laboratory equipment) and social interaction continues to be the core focus of classrooms.
De-centralised education systems tend to encourage pockets of excellence and innovative practice. In reality these can end up being isolated, even within schools where one or more ‘super teachers’ experiment with new technologies and pedagogies and the rest of the staff carry on as before. Over the next three to five years, Change Management and teacher training and support will continue to be a priority to ensure that all staff are brought to the same level. Online teacher communities and support networks (e.g. Edmodo) are and will be a vital part of this.
From a technology perspective the rise of mobile devices and apps has led to

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