May 2019

Bootstrap: Introduction to the world’s most popular CSS library

Bootstrap is the most popular HTML, CSS and JavaScript based framework for developing responsive, mobile-first websites.

With the successive growth of mobile devices in the world, it is becoming clearer that having a responsive website is a must, and by taking a mobile-first approach, this framework has been revealed as an indispensable tool and became more popular year after year, mostly because of its feature-rich nature and ease of use. One of the most essential aspects of this framework, which represents the foundation on which to build an organised, structured layout, is its grid. Bootstrap is built on a powerful 12-Column Grid System, which allows developers to arrange and align content in a fully customisable, responsive grid. The grid adjusts according to the device resolution or viewport size, making the website content interactable and pleasant for both mobile and desktop users.

Beyond this, Bootstrap offers a base style for most HTML elements, making the website look more polished, as well as an extensive list of pre-built, fully-responsive components that are easy to integrate and customise. In terms of customisation, Bootstrap lets you change the base style, including fonts, colours and sizes, as well as modifying the existing breakpoints used in grid layout by overriding the existing CSS rules with custom ones according to the project design.

For those who prefer to build a responsive website from scratch, without the assistance of any 3rd party libraries, and who use ready-made CSS code and components from previous projects to achieve this, or who may tend to have a more conservative approach towards accepting its framework features, Bootstrap can also offer great benefits.

So, what are these benefits of Bootstrap?

Well, where you have a project with a tight schedule and with multiple developers involved, Bootstrap offers consistency between projects and people (it represents a commonly known technology) as well as speed in development, thanks to its pre-styled classes, which require much less effort and time than when creating everything from scratch. It´s important to mention that Bootstrap has good cross-browser compatibility, being currently compatible with all the latest major browsers (Chrome, Firefox, Safari, Microsoft Edge and Internet Explorer 10+) and excellent support, thanks to the huge community behind it. And, most importantly, it´s completely free and open-source. Before looking at some examples, let´s see how easy is to get started with Bootstrap.

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Diogo CardanteBootstrap: Introduction to the world’s most popular CSS library
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Practical guide to installing Kotlin

Time passes by and the programming language Kotlin has more and more fans, especially when we talk about Android programming. However, Kotlin is not limited to Android mobile apps development. It is either a programming language for the JVM or a programming language for the Browser or Native, without having to run in a virtual machine.

Kotlin is 100% interoperable with Java, which allows you to add code in Kotlin to a project that has been started in Java.

One of the great advantages of this language is the absence of NullPointerExceptions.

In a direct comparison with Java, it is possible to create the same classes using fewer lines of code.

If you were convinced by all of these arguments, or if you got curious about this language, download a quick guide on how to install Kotlin and plus some basic concepts.

Download kotlin installation guide

If you want to know more about the Kotlin programming language, we recommend reading this blog post: Kotlin and a brighter future.

Bruno AzevedoPractical guide to installing Kotlin
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Advanced Analytics: learn how to elevate data analysis to a whole new level

Implementing a business intelligence model requires more than just gathering data; overall, it’s really about converting big data and valuable insights to add value to the business. However, if there’s no model available that allows you to analyse and understand this incoming data, all you’ll get is meaningless numbers with no added value.

In order to perform a correct data analysis, it is necessary to understand that there’s no unique valid method of analysis; the process depends on needs and requirements and the type of data collected in order to determine the most suitable analysis methodology.

However, there are some methods common to most advanced analytics that are capable of turning data into added valu, even when there aren’t established business rules, transforming data agglomerates into relevant insights, beneficial to the business and enabling well-founded decision-making.

Quantitative data and qualitative data

Before covering the various methods, let’s identify the precise type of data you want to analyse. For quantitative data, the focus is on raw number quantity, as the name suggests. Examples of this type of data include sales figures, marketing data, payroll data, revenue and expenses, etc. Basically, all the figures that are quantifiable and objectively measured.

Qualitative data, on the other hand, is fundamentally harder to interpret, considering its lack of structure, more subjective and of an interpretive nature. At this end of the spectrum you can find examples such as collected information from surveys or polls, employee interviews, customer satisfaction questionnaires and so on.

Measuring quantitative data

Looking at the analysis of quantitative data, there are four methods capable of taking that very same analysis to the next level.

  1. Regression analysis

The choice of the best type of statistics will always depend on the main goal of the research.

Regression analysis is capable of modelling the correlation between a dependent variable and one or more independent variables. In data mining, this technique is implemented to predict values on a particular dataset. For example, it can be used to foresee the price of a certain product, while considering other variables. It can also be useful to identify trends and correlations between different factors.

Regression is one of the commonest methods of data analysis in the market for management purposes, marketing planning, financial forecast and much more.

  1. Hypothesis testing/significance testing

This method, also called “T-testing”, is capable of determining if a certain premise is true for the relevant dataset. In data analysis and statistics, only a statistically significant result would be considered from a certain hypothesis, resultant of a non-random occurrence. This procedure makes predictions regarding a certain quantity of interests present in a certain population, from a studied sample, using the theory of probability.

  1. Monte Carlo simulation

One of the most popular methods for calculating the effect of unpredictable variables from a specific factor involves Monte Carlo simulations, using probability modelling to defend against risk and uncertainty. To test a scenario or hypothesis, this simulation uses random numbers and data to simulate a variety of possible outcomes. This tool is frequently used for project management, finance, engineering and logistics, amongst other areas. By testing a wide variety of hypothesis, it is possible do discover how a series of random variables can affect plans and projects.

  1. Artificial neural networks

This computational model replicates the human central nervous system (in this case, the brain), allowing the machine to learn by observing data (so-called ‘machine learning’). This type of information processing replicates the neural networks, using a model of biological inspiration to process information and learn through analysis, simultaneously performing predictions. In this model, the algorithms are based on sample inputs, while applying inductive reasoning – extracting rules and patterns from large sets of data.

Sílvia RaposoAdvanced Analytics: learn how to elevate data analysis to a whole new level
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