The Future of Work in the Era of Computer Science and AI, Study Guides, Projects, Research of Artificial Intelligence

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The Future of Work in the Era

of Computer Science and AI

A Practical Guide to Thrive in Today’s Job Market

Powered by AI and Computer Science

Index

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Introduction

Understanding the Era of Computer Science and the Looming Presence of AI

Charting Your Course Through Computer Science

The Skills Employers Want to See

Acquiring Your Skills – The Many Ways to Master Computer Science

Computer Science and AI in the Real World

1.1 All terms you need to know related to AI

It’s in the rise of AI that we see the next big shift in the way people work. In the past, nobody used a computer. Today, everybody has a computer at work, a laptop they carry around, and a smart device in their pocket that is essentially a mini-computer itself. In the future, the AI industry is expected to achieve 20-fold growth between 2021 and 2030, rising from $100 billion in value (approx. €93.23 billion) to over $1.8 trillion (approx. €1.68 trillion) over the next decade.

AI will be everywhere. Mentions of a “brand new era of work” may feel intimidating at first, which is why it’s crucial to define what that era will look like. To do so requires an understanding of some of the key terms in computer science.

Algorithm The set of rules, created by a developer, that a computerized system follows to execute its task and learn from the data it intakes.

Artificial Intelligence By combining Big Data and computer science practices, artificial intel- ligence simulates human intelligence based on specific rulesets.

Big Data Big Data refers to the massive datasets many companies generate in the modern data age, with these datasets requiring special computa- tional techniques to analyze.

Understanding the Era of

Computer Science and the

Looming Presence of AI

Bug An unintentional outcome, such as a glitch, a crash, or incorrect re- sults generated, resulting from errors in the code lying behind a pro- gram or application.

Cloud Computing Cloud computing involves the use of remote servers and the internet to store and process data, deliver services, and offer storage solu- tions to users.

Code Used in programming, the term “code” refers to the string of com- mands a developer writes in order to tell a computer what a program or application needs to do.

Computer Science The term used to denote the study of the principles, theory, and practical use of both computers and computer-based systems.

Cyber Security

Cyber security covers the use of various technologies, including software, physical hardware, and various processes, to protect com- puterized systems from attacks over the internet.

Data Science

The use of various computer science, statistical, and mathematical techniques to analyze datasets with the goal of solving problems or creating insight.

Dataset A collection of data, typically with some link or pattern, that a data scientist can analyze for patterns and trends.

2.1 Key Roles in the Tech Industry

Given that it’s clear to see the direction the trends in the computer science industry point (all major sectors have amazing predicted growth), it’s a good idea to familiarize yourself with the types of roles you’re likely to take on should you enter the industry. In doing so, you can identify your potential pathway into a career. And by virtue of identifying that pathway, you also make it easier to select an educational route that brings you closer to your preferred career.

The following are all crucial roles in the tech sector, with each leading you into one of the growing industries discussed in the previous chapter.

Charting Your Course Through

Computer Science

2.2 Data Scientist and Data Analyst

Think of a data scientist as the mind behind the machine learning “brain” used to extract and analyze enormous datasets. These pro- fessionals are responsible for choosing (or creating) the machine learning and artificial intelligence tools used to extract, analyze, and draw insight from large datasets.

Data analysts are similar to data scientists in the sense that they collect and analyze the data that a company generates. The key difference is that analysts draw insight from this data with a view to solving business problems (or making predictions of future trends), whereas a data scientist is more focused on creating the models used to draw these insights.

Salaries for these roles vary depending on the nature of the work, with data scientists and analysts in the corporate world being likely to earn more than those in research fields. Expect a salary ranging between €32,000 and €55,000.

€32,000 year

2.4 AI Specialist

€91,000 year

Though the name implies that AI specialists are people who create AI-based solutions, that’s not always the case. Yes, an AI specialist may know how to develop bespoke tools. But they’re far more likely to work in consultancy roles, advising businesses on the best AI solutions to incorporate based on currently available tools and their own experience with said tools.

So, AI specialists wear several hats. They have technical skills, ro- oted in computer science, that enable them to make viable recom- mendations to business owners. But they also have soft skills – strong communication, creativity, and the ability to work as part of a team. This combination of skills leads to high earning potential, in the €90,000 region.

The second any device connects to the internet, the possibility for a cyberattack to occur is created. Cybersecurity analysts are the buf- fers between their employers and these attacks, as they specialize in analyzing networks, spotting security gaps , and filling those gaps through the implementation of infrastructural security solutions.

The analyst aspect of the role comes from the professional being able to examine existing protective measures. But cybersecurity analysts can usually recommend upgrades to hardware, software, and sy- stems, in addition to implementing those upgrades on behalf of their clients. Though salaries average out at €29,000, it’s possible to make near-six figures with the right cybersecurity role.

2.5 Cybersecurity Analyst

€29,000 year

Each of the roles described in this chapter requires different skills, with some focusing on data interpretation and analysis while others require technical coding skills. Use these quick tips to assess yourself and ensure you take your first steps in the right direction:

- Focus on what drives you , what you’re passionate about, ahead of what you think may offer the best career prospects.

• Reflect on your skills, strengths, and knowledge gaps to ask your- self what you’re suited for right now and whether your knowledge gaps can be filled for other roles. - Ask people who know you well which career path they believe would suit you based on your skills and their personal knowledge of you.
• Take advantage of online tools to run an “audit” on yourself to see where your current skills lie. - Determine your personal values (both ethical and professional) and focus on the career path that aligns best with those values.

2.7 Quick Self-Assessment Tips to Help You

Identify Your Best Role

Start your career

Bachelor in Modern Computer Science

Master in Applied Data Science & AI STUDY LEVEL Undergraduate

STUDY LEVEL Postgraduate CREDITS 180 ECTS - EQF > / MQF > Level 6

CREDITS 90 ECTS - EQF > / MQF > Level 7 DURATION 2 (fast-track) or 3 years

DURATION 12 or 18 months LOCATION Online / Fully remote

LOCATION Online / Fully remote

Go to Bachelor Go to Master

Focus on your education. Formal education in computer science serves as the foundation on which you can build a strong career

2.8 Take Your First Step into a Computer Science

Career

Python Programming

Understanding of Algorithms

Any computer science role comes down to the skills you bring to the table. However, many make the mistake of thinking that it’s only the technical side of things that matters when it comes to computers. That isn’t the case. While technical skills are crucial, of course, there are many soft skills that also matter in computer science , along with the general skill of being able to learn and adapt in a constantly changing industry. This chapter looks at the key technical, soft, and emerging skills you need to develop to increase your chances of lan- ding a computer science job. Starting with technical skills, these are three that prove essential to computer scientists.

An enormous 69% of people involved in data science and machine learning model development use Python , as compared to just 24% who say they use R regularly. Simply put, you need to tame Python if you want to integrate into either field.

In machine learning alone, there are four key algorithms (supervised learning, unsupervised learning, semi-supervised learning, and rein- forcement), with each having specific purposes for implementation. You need to know what these algorithms are and, more importantly, which are relevant to specific situations.

The Skills Employers Want

to See

Data Analysis

Soft Skills

Problem-Solving

Creativity

According to Statista, global data volumes will climb to a staggering 180 zettabytes by 2025. Given that a single zettabyte is equivalent to 1 billion terabytes of data, it’s clear that “Big Data” is far more than a marketing buzzword. That data is practically useless without analysts who can separate the wheat from the chaff.

Soft skills are essentially the skills that complement your tech skills that make you more able to work as part of (or in the leadership of) a team.

Computer scientists solve problems. Software developers solve pro- blems by creating code. Product managers use data to solve pro- blems and data scientists figure out how to extract useful information from massive datasets. You need to have an eye for the solution to a problem to succeed in computer science.

It’s often difficult to think of computer science as a “creative” subject, at least in the traditional sense of the word. Working within rules is what you do. But within those rules and functions lies the opportunity to create almost anything that you can imagine. A dash of creativity ensures that the programs and algorithms you create don’t end up being also-rans compared to others.

Over two-thirds (69%) of data science and machine learning professionals use Python.

Zettabytes will be the amount of data generated across the globe.

Of employers (86%) say poor communication is the leading cause of workplace mistakes.

Of respondents (82%) in an ISACA survey say that there’ll be a rise in demand for cybersecurity professionals in 2023.

Bilions spending on public cloud services by businesses in 2023.

Milion of new jobs related to AI will be available in 2025

3.11 Cybersecurity Principles

A forecast of the trend of the AI

In ISACA’s “State of Cybersecurity 2022” report, the organization hi- ghlights a survey in which 82% of respondents believe they’ll see an increase in the demand for technical cybersecurity roles in 2023. Those figures are in line with the growth numbers mentioned ear- lier (13.8% CAGR to 2030). In a world of Big Data, the people who can fight back against hackers are in demand.

180

86%

97

559

82%

How do you acquire computer science skills?

Formal courses, online courses, and even bootcamps can provide the answer, though each approach has pros and cons.

Acquiring Your Skills –

The Many Ways to Master

Computer Science