Computing, Data Science and Other Skills For Managers

Computing, Data Science and Other Skills For Managers

DOI: 10.4018/978-1-7998-2036-9.ch003


Managers are required to have different skills and mindset in order to be successful nowadays. Business schools around the world contributed to the standardisation of knowledge and practices in the managerial environment, with MBA programmes being a must in every leader's curriculum. However, times are changing rapidly, and traditional knowledge around accounting or strategy models and tools need to be updated to consider the innovation brought in by technology. Concepts related to data, such as data science and big data, have intrigued a huge number of people around the world. Numbers are said to be an extensive source of knowledge, invaluable to those who want to improve processes, experiences, and efficiency in general. In this chapter, the authors discuss the new skills managers should learn to try have a better understanding of the subjects, fields, or abilities recruiters are looking for. Thus, we should still read this required expertise in conjunction with other soft skills linked to emotional intelligence and leadership techniques.
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Computational Thinking Or Being Logical

You might have heard of the phrase “computational thinking” in one of the fancy conventions for entrepreneurs and start-ups, maybe. Well, computational thinking defines the art of thinking computationally, or logically, in a way that each problem can be defined as a series of steps, with an input that generates an output through the use of algorithms for example2. When trying to figure out how computers work, we need to remind ourselves we are dealing with the binary system, rather than in scale 10. Computers understand 0 and 1 in a specific series. So for example, we have learned in school that we can separate numbers in units, tens, hundreds and so on, which means that we have 100 first (10 to the 2), then 10 (10 to the 1), and finally 1 (10 to the 0). Furthermore, if we use the binary system, we will have 4 (2 to the 2), then 2 (2 to the 1), and 1 (2 to the 0). If we wanted more numbers, we could move on after 4 to 8 (2 to the 3), 16 (2 to the 4), 32 (2 to the 5), and we could keep going. It seems easy enough, thus we will try to calculate how a computer would understand number 1. Using three digits, the number 1 will be written as 001 in the binary code, where the first 0 from the left equals to 4 times 0, the one in the middle is calculated as 2 times 0, and 1 is the result of 1 times 1. If that is correct, number 2 will be represented as 010 (4 times 0, plus 2 times 1, and 1 times 0). Number 3 will become 011 (again, 4 times 0, plus 2 times 1, plus 1 times 1). If we keep going on with this system and three digits, we can reach number 7 as a maximum, which will be represented as 111. How can we move on and write number 8? We will need to add another value, so that the computer reads 1000 (8 times 1, plus 4 times 0, plus 2 times 0, plus 1 times 0) and so on. Because the binary system implies the use of only two states (0 and 1), we can conventionally say that 0 corresponds to a switched off situation, while 1 represents a switched-on state. A very clear example of this concept can be found in lightbulbs or lamps, when our 0 will correspond to no light, and 1 to having light turned on in the room. Such switches are better known as transistors inside a computer’s CPU (central processing unit).

Table 1.
Example of numbers in binary code
Example 16110
Example 2121100
Example 31810010
Example 42511001

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