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Recently many of our Year Six students have been involved in projects that require them to utilise the brain of a maker. Facing challenges involving the exploration of how everyday objects are manufactured and while responding to their 'Genius Hour' ambitions they are facing a new set of problems and discovering the joy that comes from solving these with their hands as much as their brains.

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How to use Python and Data Science libraries? Who are beginners and intermediate learners can get benefitted with helpful examples and exercises, in this article. As Python has very rich libraries, which is used broad ways in Data Science industry. Moreover, all of the libraries are open sourced. I wanted to outline some of its most useful libraries for data scientists and engineers, based on my recent experience.

Chatbots are conversation based applications that can be coordinated into informing stages to perform computerized tasks, such as, gathering data from clients or helping customers locate the correct products.

This unavoidable and irreducible complexity means that schools are challenging place to study, to understand and to manage change within. Even for the teacher who spends everyday inside the school there is so much going on that unguided observations and the plans based upon them come with no guarantee of success. - We need a lens and a lever to manage this complexity. -  Such a lens is offered by the 'cultural forces'.

Portaportal is a web based bookmarking utility that lets you store links to your favorite websites online. Now your bookmarks are no longer limited to any one machine.

Java.util package provides a PriorityQueue class where an unbounded priority queue based on a priority heap. The elements of the priority queue are ordered according to their natural ordering, or by a Comparator provided at queue construction time, depending on which constructor is used. A priority queue does not permit null elements. A priority queue relying on natural ordering also does not permit insertion of non-comparable objects.

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Hasty generalization is all about concluding something based on a very less number of evidence in academic writing. In our daily, we see every person has his own taste for everything. If a large number of people have the same taste, then you can conclude something accurate from that. And based on single evidence concluding anything as general is wrong. For example, I, as a teenager, prefer sneakers on joggers. Now, if I state all teenagers like to wear sneak

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The second type of data is known as categorical data. This is a data type that can be stored and identified based on the numbers given to them. This kind of data is mostly stored in the form of groups and categories. Each group is categorised using its label. The grouping or categorisation is usually done based on the data characteristics.