AI For Beginners



If you want to explore the world of artificial intelligence but feel it’s full of technical terms that sound a little like wizardry, this is the resource for you. “AI For Beginners” teaches you everything you need to know to feel comfortable working with AI.


Learn how to use typical AI terms like a pro with easy-to-understand explanations and practical working examples.


Understand the roles and responsibilities in every AI team and know who’s working on what in any given project.


See real-world examples of how employees and businesses benefit from implementing artificial intelligence.

AI buzzwords - definitions

Artificial Intelligence

What is artificial intelligence?

The term was given in 1956 by John McCarthy, who is recognized as the father of Artificial Intelligence. He defines artificial intelligence as “the science and engineering of making intelligent machines.” 

The definition itself has evolved over the years. According to PWC, “AI is a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they’re sensing and their objectives.”

In other words, AI is trying to build artificial systems that will work better on tasks that humans currently can do.

It refers to the Turing Test, named after Alan Turing. He wanted to answer the question, “Can machines think?”. In his test, one person talks with another person and a machine simultaneously. 

If the first person cannot say which of the two conversations involves another human, then we are talking about AI.

Types of artificial intelligence

We can divide artificial intelligence into Artificial Narrow Intelligence (ANI), also known as weak AI, and Artificial General Intelligence (AGI), also called strong AI. The first one is trained and focused on performing specific tasks. ANI drives most of the AI that surrounds us today. AGI is an AI that more fully replicates the human brain’s autonomy—AI that can solve many types or classes of problems and even choose the issues it wants to solve without human intervention. Strong AI is still entirely theoretical, with no practical examples in use today. AI researchers are also exploring (warily) artificial superintelligence (ASI), which is artificial intelligence superior to human intelligence or ability.

Artificial intelligence applications

Artificial intelligence allows machines and computers to mimic the perception, learning, problem-solving, and decision-making abilities of the human mind.

These are just a few of the most common examples of AI you can notice every day:

  • Speech recognition
  • Natural language processing (NLP)
  • Image recognition (computer vision or machine vision)
  • Real-time recommendations
  • Virus and spam prevention
  • Ecological solutions 
  • Automated stock trading
  • Ride-share services
  • Household robots
  • Improvements in healthcare
  • Autopilot technology

There are many others. If you would like to know more about AI’s applications, check these 10 AI trends to watch in 2021.


Machine Learning

What is machine learning?

The easiest way to explain the difference between artificial intelligence and machine learning is by saying, ‘All ML is AI, but not all AI is ML.’ But what exactly is machine learning? 

According to the “godfather” of modern AI, Dr. Yoshua Bengio, “Machine learning is a part of research on artificial intelligence, seeking to provide knowledge to computers through data, observations, and interacting with the world. The acquired knowledge allows computers to correctly generalize to new settings.” 

Machine learning is basically making programs that can learn independently through training without a programmer first codifying rules for the program to follow. 

An ML algorithm’s main task is to find patterns and features in a large amount of data. The better the algorithm, the more valid the decisions and predictions will become as it processes more data. Machine learning uses many algorithms, among which are neural networks with only one hidden layer. 

The most basic neural network consists of:

  • An input level
  • At least one hidden level
  • An output layer

If you’re interested in one application of machine learning, check out how ML can transform digital marketing.

How machine learning works

There are seven fundamental steps for building a machine learning application (or model):

  1. Gathering data
  2. Data preparation
  3. Data Wrangling
  4. Analyse Data
  5. Train model
  6. Test model
  7. Deployment

During your work, you can also use ‘off-the-shelf’ models

Machine learning methods

Machine learning methods consist of four categories.

  1. Supervised machine learning
  2. Unsupervised machine learning
  3. Semi-supervised learning
  4. Reinforcement learning

The type of algorithm a data scientist chooses to use depends on what kind of problem they want to solve.


Data Science

What is data science?

According to Oracle, data science “combines multiple fields including statistics, scientific methods, and data analysis to extract value from data. In other words: data science refers to the multi-disciplinary area that extracts knowledge and insights from the ever-increasing volumes of data.  

DS includes statistics, machine learning, and data analysis, using each to process data, analyze the inputs, and present results. Data science provides a way of finding patterns and making predictions to understand a more connected world better than ever.

Data scientists use AI algorithms and data to find valuable insights from available information and make more informed business decisions.

How data science is conducted

The process of analyzing and acting upon data is iterative rather than linear, but this is how the data science lifecycle typically flows for a data modeling project:

  • Planning 
  • Building a data model
  • Evaluating a model (mandatory)
  • Explaining models (optional)
  • Deploying a model
  • Monitoring models

Data science tools

If you want to begin with data science, it’s worth knowing the most in-demand skills for data scientists. Besides these, there are many sources to find which languages are the most desirable for data scientists to know. The following three are the most important and most commonly used:

  • Python: considered the most wanted programming language among developers. It is an object-oriented, open-source, flexible, and easy-to-learn programming language and has a rich set of libraries and tools designed for data science. It also supports multiple paradigms, from functional to structured and procedural programming. Python holds an essential place among the top tools for Data Science and is often the go-to choice for various tasks, especially among beginners, because it’s commonly known as easier to learn. 
  • R: a high-level programming language created by statisticians. Thanks to the multiple useful libraries, data scientists can use it. R can come in handy for exploring data sets and conducting ad hoc analysis. 
  • JavaScript: an object-oriented programming language. It is capable of handling multiple tasks at once. It is also helpful in embedding everything from electronics to desktop and web applications. JavaScript is also very useful in machine learning.

If you’re not sure which one is better to learn at the beginning of your journey, read this comparison


Big Data

What is big data?

As the name suggests, big data refers to the process of collecting and analyzing large volumes of data sets to discover useful hidden patterns. 

According to the Cambridge dictionary, big data is “very large sets of data that are produced by people using the internet, and that can only be stored, understood, and used with the help of special tools and methods.”

7 V’s

These are the seven key aspects of big data to understand it correctly.

  1. Volume – is the amount of data we have. Now it’s measured in Zettabytes (ZB) or even Yottabytes (YB). 
  2. Velocity – is the speed at which data is processed and becomes accessible.
  3. Variety – refers to so many different types of available data. 
  4. Variability – refers to data whose meaning is constantly changing.
  5. Veracity – refers to the trustworthiness and importance of the data source, the reliability of the data, and its relevance to your business case.
  6. Visualization – is nothing more than using charts and graphs to visualize large amounts of complex data to make it easier to use.
  7. Value – data needs to be important and useful; in other words, worth processing.

If you’re not sure if you have enough data or if it’s good quality, read this article

How big data works

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

  • Integrate
  • Manage
  • Analyze

Find more differences between AI, machine learning, data science, and big data in this article


Neural Networks

What are neural networks?

Neural networks, also known as artificial neural networks (ANNs), are a subset of ML and are at the heart of Deep Learning (DL) algorithms. They reflect the behavior of the human brain, mimicking the way that biological neurons signal to one another, enabling computer programs to identify patterns and solve common issues in the fields of AI, machine learning, and deep learning.

How do neural networks work?

Neural networks consist of artificial neurons, which are divided into: 

  • Input units (red) – are designed to receive various forms of data from the outside world that the network will attempt to learn about, recognize, or otherwise process; 
  • Output units (yellow) – are on the opposite side of the network and signal how it responds to the information it’s learned;
  • Hidden units (blue) – sit between the two.


A richer structure that consists of many different layers between the input and the output is called a deep neural network (DNN). It’s used for tackling more complex issues.  

The input layer receives various forms of data from the outside world. Based on that, the network aims to learn. Inputs are fed in, activate the hidden units, and make outputs feed out. The weight of the connection between any two units is gradually adjusted as the network learns.

Neural networks learn things by backpropagation, which is a feedback process. It means it compares the intended output a network produces with the output it actually produces. It uses the difference between them to modify the weights of the connections between the units in the network, then backward-processes from the output to the input units. In time, backpropagation causes the network to reduce the difference between actual and desired output to where the two synchronize, so the network works as it should.

Once the network has been trained with enough examples, it reaches a point where it can be presented with a new set of inputs it’s never seen before to see how it replies. 


Deep Learning

What is deep learning?

Deep learning (DL), or deep neural learning, is a subset of machine learning, which uses neural networks (with more complex architecture than ML applications) to analyze different factors with a structure similar to the human neural system. While a neural network with a single layer can make predictions, additional hidden layers can help to optimize and refine for accuracy. DL deals with structured, semi-structured, and unstructured data.

How deep learning works

Deep learning neural networks attempt to mimic the human brain through a combination of data inputs, weights, and biases. These elements cooperate to recognize, classify, and describe items within the data.

Deep neural networks consist of numerous layers of connected points, each building upon the former layer to optimize the prediction or categorization. The progression of computations through the network is called forward propagation. The opposite process, called backpropagation, uses algorithms to calculate errors in predictions and then adjust the biases and weights of the function by moving backward through the layers to train the model. Both methods enable a NN to make predictions and correct any errors accordingly. Over time, the algorithm becomes more accurate.

The described process is the simplest type of deep neural network. However, deep learning algorithms are very complex, and different neural networks address specific queries or datasets. 

For instance:

  • Convolutional neural networks (CNNs), used primarily in computer vision and image classification applications, can detect patterns and features within a picture or video, enabling tasks like object detection or recognition. 
  • Recurrent neural networks (RNNs) are used in natural language and speech recognition applications to leverage sequential or time-series data.


Computer Vision

What is computer vision?

Computer vision is a field of study that trains computers and systems to interpret and understand essential information from images, videos, and other visual data — and take actions or make recommendations based on that information. Computer vision allows machines to observe, see and understand their surroundings.

How does computer vision work?

Computer vision runs data analysis over and over until it discerns distinctions and ultimately recognizes images. Two fundamental technologies are used to accomplish this: 

  • Machine learning (mainly deep learning) uses algorithmic models that enable computers to teach themselves about the context of visual data to differentiate one picture from another. Algorithms allow the machine to learn by itself, rather than someone programming it to recognize an image.
  • Convolutional neural networks (CNN) are a subset of deep learning. They help a model “look” by learning the characteristic features of images. Through this approach, a model can build a hierarchical understanding of photos (e.g., it can learn what eyes look like and identify a human in the picture because it detects eyes). The NN runs convolutions and checks the accuracy of its predictions in a series of iterations until the projections start recognizing or seeing images in a way similar to people.


Many types of computer vision are used in different ways: 

  • Object tracking tracks an object once it is detected. 
  • Image classification groups images into different categories.
  • Object verification determines the thing in the photograph.
  • Object segmentation classifies which pixels belong to the object in the picture.
  • Object detection identifies objects in the picture.
  • Object landmark detection verifies the key points for the object in the image.
  • Object recognition recognizes items in the image (for instance, facial recognition).
  • Pattern detection is a process of identifying repeated shapes, colors, and other visual indicators in images.


Natural Language Processing

What is natural language processing?

Natural language processing is a field of study whose primary purpose is to enable computer software to understand human language as it is spoken and written. NLP combines computational linguistics—rule-based modeling of human language —with statistical, machine learning, and deep learning models. These technologies allow computers to process human language in text or voice data and ‘understand’ its whole meaning, complete with the speaker or writer’s intent and sentiment.

How does natural language processing work?

NLP takes real-world input and processes it into something a computer can understand, whether the language is spoken or written. Computers use programs to read, microphones to collect audio, and a program to process collected data. The information is then converted to code that the computer can understand during processing.

What does NLP do?

NLP is used in:

  • Speech recognition
  • Speech tagging
  • Word sense disambiguation 
  • Named entity recognition (NER), identifying words or phrases as valuable entities
  • Sentiment analysis 
  • Natural language generation, putting structured information into human language


Robotic Processing Automation

What is robotic processing automation?

Robotic Process Automation (RPA) is a set of algorithms that integrate different applications, simplifying repetitive and monotonous tasks. These include logging into a system, downloading files, switching between applications, and copying data. 

Where is it used?

RPS is useful in factory settings, where activities are often repetitive, requiring little intellectual thought. We simply show a machine how to complete a task, and it will mindlessly repeat it. But Robotic Process Automation also works well in the office environment. Mostly in business processes that use software to analyze giant data sets (mainly data sheets); or applications and ERP systems that update CRM data. 

A single RPA bot may be as productive as up to thirty full-time employees. It can increase the efficiency of internal processes, relieve employees from tedious tasks or reduce human error. 

The main reasons companies use RPA:

  • Increase the efficiency of internal processes
  • Improve core business processes
  • Increase customer satisfaction
  • Increase employee satisfaction and engagement
  • Relieve employees from tedious, repetitive tasks
  • Reduce human error
  • Reduce process execution costs

RPA focuses on taking on simple activities that people typically perform. That said, a human will often undertake the final decision because it requires professional knowledge, which robots don’t have. But there is also RPA 2.0, which replaces people with machine learning in the decision-making process. 

RPA 2.0 leaves a robot to make the final choice, with humans just verifying it, and only when necessary.

Find more information about RPA 2.0 applications in this article


*Interesting fact*

A robot is an autonomous machine that can sense its surroundings, use data to make decisions, and perform activities in the real world.

Many people consider robots as human-looking devices, and on the one hand, it’s true: that’s called a humanoid robot. They have a human shape, a ‘face,’ and sometimes the ability to talk. But the truth is: humanoid robots only make up a tiny percentage of all robots. Most real-world robots currently in use look very different as they are designed according to the application. 

It may not be evident at first sight, but any vehicle with some autonomy level, sensors, and actuators is counted as robotics. On the other hand, software-based solutions (such as a customer service chatbot), even if sometimes called “software robots,” aren’t counted as (real) robotics.


Predictive Modeling

What is predictive modeling? 

Predictive modeling is a statistical method that uses machine learning and data mining to predict and forecast future outcomes based on historical and current data. It is validated or revised regularly to incorporate changes in the underlying data. In other words, it’s not a one-and-done prediction. If new data shows changes in what’s happening now, the likely future outcome’s result must also be recalculated. Most predictive models work quickly and often complete their calculations in real-time. 

Types of Predictive Model

  1. Classification model: Considered the simplest model, it classifies data for direct query response. 
  2. Clustering model: This model nests data together by common characteristics or behaviors and plans strategies for each group at a larger scale. 
  3. Forecast model: This is a prevalent model, and it works on anything with a numerical value based on learning from historical data. 
  4. Outliers model: This model works by analyzing unusual or outlying data points.
  5. Time series model: This model evaluates a sequence of data points based on time. 


Cognitive Computing

What is cognitive computing?

Cognitive computing is a system that can learn at scale, reason with purpose, and interact with people. It combines computer science and cognitive science: understanding the human brain and how it works. 

The computer can solve problems and optimize human processes by using self-teaching algorithms that use data mining, visual recognition, and natural language processing. It aims to solve complex situations characterized by uncertainty and ambiguity, which means issues typically only solved by human cognitive thought.


Internet of Things

What is the internet of things?

The Internet of Things (IoT) is a set of devices, vehicles, appliances with sensors, software, and other technologies that can connect, collect and exchange data over a wireless network, with little or no human-to-human or human-to-computer intervention. 

IoT allows devices on private internet connections to communicate with others. Combining these connected devices with automated systems enables information gathering, data analysis, and the instigation of an action to help someone with a specific task or learn from a process. 

How does IoT work?

IoT exists thanks to the compilation of several technologies:

  • Sensors: which are now low-cost and have low power demands
  • Connectivity: it is now easy to connect sensors to cloud computing platforms thanks to a rise in the availability of cloud platforms, which allows them to access the infrastructure needed to scale up without having to manage it all
  • Machine learning and analytics: thanks to advances in ML and analytics and access to varied and large amounts of data collected in the cloud, businesses can now gather insights faster and easier
  • Conversational artificial intelligence: advances in neural networks have brought NLP to IoT devices (such as digital personal assistants Alexa or Siri)


Key benefits of AI

Reduction in Human Error

People sometimes make mistakes. Machines, however, do not make these mistakes if they are correctly programmed. So faults are decreased, and the chance of reaching accuracy with a higher degree of precision is possible.

Faster Decisions

We can make machines take decisions faster than humans and carry out actions quicker using AI alongside other technologies. While making a decision, humans will analyze many factors both emotionally and practically, but AI-powered machines work on what is programmed and deliver the results faster.

Easier Daily Life

Daily applications such as Apple’s Siri, Window’s Cortana, Google’s OK Google are frequently used in our daily routine, whether for searching a location, taking a selfie, making a phone call, or replying to a mail, among other routine tasks.


Automation has large impacts on the transportation, communications, consumer products, and service industries. Automation leads to higher production rates and boosted productivity in these sectors and allows more effective use of raw materials, improved product quality, or reduced lead times.

Improved Customer Experience

AI-powered solutions can help companies respond to client queries and grievances quickly and address the situations efficiently. The use of chatbots based on AI with Natural Language Processing technology can generate highly personalized messages for customers who will not know if they’re talking to a human being or a machine.

Increased Business Efficiency

AI can help to ensure 24-hour service availability and deliver the same performance and consistency throughout the day. Moreover, AI can productively automate monotonous tasks, remove “boring” tasks for humans, reduce the stress on employees, and free them to take care of more critical and creative tasks that require manual intervention.

Research and Data Analysis

AI and Machine Learning can analyze data much more efficiently than a human. These technologies can help create predictive models and algorithms to process data and understand the possible outcomes of various trends and scenarios.

Accurately Diagnosing Diseases

AI, especially deep learning, can potentially reduce costs and improve diagnosing acute disease on radiographic imaging. This benefit is pronounced for cancer patients when early detection can be the difference between life and death. 

Preserving Environmental Resources

AI has the potential to benefit conservation and environmental efforts, from combatting the effects of climate change to developing recycling systems. AI, coupled with robotics, can modify the recycling industry, allowing for better sorting of recyclable materials. 

AI positively impacts climate change, including managing renewable energy for maximum efficiency, making agricultural practices more efficient and eco-friendly, and forecasting energy demand in large cities.

Predicting Natural Disasters

Natural disasters can strike suddenly, leaving citizens with little time to prepare. Artificial intelligence doesn’t have the power to prevent them, but it can help experts predict when and where disasters may strike, giving people more time to keep themselves and their homes safe.

Improving Education

AI can teach efficiently 24 hours-a-day, and it has the potential to provide one-on-one tutoring to all students. It can allow all students to get regular, personalized tutoring based on their needs.

There’s also the potential to create highly personalized lesson plans for students and reduce teachers’ time focusing on administrative tasks.

Preventing Acts of Violence

Experts use AI to develop solutions to keep innocent people safe from acts of violence. Institutions and individual homeowners can’t always hire security personnel to keep their environment safe. AI can provide an immediate alert by recognizing when someone is carrying a firearm.

Typical roles on an AI development team

Machine Learning Engineer

Who is a Machine Learning Engineer?

Machine learning engineers are responsible for creating self-running AI software to automate predictive models for suggested quests, chatbots, virtual assistants, translation apps, or driverless cars. They design ML systems, apply algorithms to generate accurate predictions, and resolve data set problems.

What does a Machine Learning Engineer do?

  • Designing and developing machine learning and deep learning systems
  • Running machine learning tests 
  • Implementing appropriate ML algorithms
  • Studing and transform data science prototypes
  • Selecting relevant datasets and data representation methods
  • Performing statistical analysis and fine-tuning test results
  • Training and retrain systems when necessary
  • Extending existing ML libraries and frameworks

AI Software Engineer

Who is an AI Software Engineer?

AI software engineers are responsible for being up-to-date with all the breakthrough artificial intelligence technologies that can transform business, the workforce, or consumer experience and how the data science team can leverage that.

The AI engineer brings software engineering experience into the data science process. 

What does an AI Software Engineer do?

  • Building infrastructure as code
  • Implementing tests
  • Continuous integration and versioning control
  • Developing pilots and MVP applications
  • API development

Head of AI

Who is a Head of AI?

The head of AI goal is to build AI strategy for products and services, management at the emergency level, acquire new customers, and the care and supervision of AI and research teams.

What does a Head of AI do?

  • AI roadmap vis-a-vis business vision/goals/initiatives (VGIs)
  • Ideas & insights into newer AI-powered business models
  • Projects/products implementation methodologies/processes (agile etc.)
  • AI algorithms, research & related roadmap
  • AI platform/products architecture/design/implementation vis-a-vis cloud AI services
  • AI automation projects
  • AI/ML models quality assurance strategy
  • AI/ML models continuous delivery/deployment strategies
  • Communication with customers/partners/media/internal

Data Scientist

Who is a Data Scientist?

A data scientist is a person responsible for turning raw data into relevant insights that a company needs to develop and compete. The results of DS’s work have an impact on the decision-making process.

What does a Data Scientist do?

  • Identifying relevant data sources for business needs
  • Collecting structured and unstructured data
  • Sourcing missing data
  • Organizing data into usable formats
  • Building predictive models
  • Building machine learning algorithms
  • Enhancing the data collection process
  • Processing, cleansing, and verifying data
  • Analyzing data
  • Setting up data infrastructure
  • Developing and maintaining databases
  • Preparing visualizations of data

Product Owner

Who is a Product Owner?

The product owner is an IT professional responsible for setting, prioritizing, and evaluating the work generated by a software team to ensure faultless features and functionality of the product.

What does a Product Owner do?

  • Owning the scrum team’s backlog
  • Defining product vision, roadmap, and growth opportunities 
  • Providing vision and path to the development team and stakeholders throughout the project
  • Organizing and prioritize product feature backlog and development for the product
  • Working closely with product management to build and maintain a product backlog
  • Planning product releases and set the expectation for delivery of new functions
  • Researching and market analysing

Project Manager

Who is a Project Manager?

The project manager is responsible for applying processes and techniques to initiate, plan, manage, and deliver specific projects to achieve their goals on schedule and budget. Project management personnel will typically utilize various methodologies and tools as part of the process.

What does a Project Manager do?

  • Defining project objectives, project scope, roles, and responsibilities
  • Defining resource requirements and managing resource availability & allocation
  • Outlining a budget based on needs and tracking costs to deliver the project on budget
  • Preparing a detailed project plan to schedule key project milestones, workstreams & activities
  • Tracking tasks and providing regular reports
  • Identifying and mitigating potential risks
  • Managing the communication with the client and stakeholders

Front-end Developer

Who is a Front-end Developer?

Front-end developers are programmers who specialize in website and application design. This role is responsible for client-side development. 

What does a Front-end Developer do?

  • Defining the structure and layout of web pages
  • Building reusable code for future use
  • Optimizing web pages for maximum speed and scalability
  • Developing features to enhance the user experience
  • Keeping a balance between functionality and aesthetic 
  • Ensuring web design is optimized for smartphones

Back-end Developer

Who is a Back-end Developer?

Back-end developers take care of server-side web application logic and integration of front-end developers’ work. They are usually writing the web services and APIs used by front-end developers and mobile application developers.

What does a Back-end Developer do?

  • Writing high-quality code
  • Building and maintaining web applications
  • Managing hosting environments
  • Assessing the efficiency and speed of current applications
  • QA testing
  • Troubleshooting and debugging
  • Improving the server, server-side applications, and databases 

Data Engineer

Who is a Data Engineer?

Data engineers are responsible for cleaning, collecting, and organizing data from different sources and transferring it to data warehouses. Based on the data, they find trends and develop algorithms to help make raw data more beneficial to the company. 

What does a Data Engineer do?

  • Developing, constructing, testing, and maintaining architectures
  • Data acquisition
  • Developing data set processes
  • Identify ways to enhance data reliability, efficiency, and quality
  • Researching industry and business questions
  • Using large data sets to address business issues
  • Finding hidden patterns using data

Data Analyst

Who is a Data Analyst?

Data analysts take care of gathering data from various sources and then interpret it to provide meaningful insights to help businesses make better-informed decisions. 

What does a Data Analyst do?

  • Identifying data sources
  • Collecting data
  • Organizing data into usable formats
  • Setting up data infrastructure
  • Developing, implementing, and maintaining databases
  • Assessing the quality of data and cleaning data
  • Generating information and insights from data sets and identifying trends and patterns
  • Creating visualizations of data

Business Analyst

Who is a Business Analyst?

Business analysts help companies improve their processes and systems. They conduct research and analysis to develop solutions to business problems and help introduce these systems to businesses and their clients.

What does a Business Analyst do?

  • Gathering, validating, and documenting business requirements​
  • Analyzing budgets, sales results, and forecasts
  • Modeling business processes 
  • Identifying opportunities for process improvements​
  • Creating functional specifications for solutions​
  • Estimating costs and identifying business savings
  • Recognizing possible issues and risks


Who is a Statistician?

A statistician is responsible for gathering data and then displaying it, helping businesses make sense of quantitative data. Their insight is meaningful in the decision-making process. 

What does a Statistician do?

  • Designing data acquisition trials
  • Analyzing trends
  • Applying statistical methodology to complex data
  • Acting in a consultancy capacity
  • Designing and implementing data gathering
  • Managing computer software and systems
  • Making forecasts and providing projected figures


Who is a Mathematician?

Mathematicians are responsible for collecting data, analyzing it, and presenting their findings to solve practical business, government, engineering, and science problems. They usually work with other experts to interpret numerical data to determine project outcomes and needs, whether statistically or mathematically.

What does a Mathematician do?

  • Recognizing unknown relationships between mathematical principles
  • Creating models to resolve real business problems
  • Developing computational techniques and computer codes
  • Comparing results derived from models with observations or tests

Social Scientist

Who is a Social Scientist?

Social scientists research and collect sociology, demographics data, and opinions from interviews and questionnaires, based on which they extract crucial information.

What does a Social Scientist do?

  • Observing links between society and human behavior
  • Formulating research questions
  • Providing analysis of collected information
  • Planning, designing, and authorizing highly complex research projects to provide a framework for collection and analysis
  • Giving input on public opinion surveys and focus groups
  • Doing numerical and trend analysis
  • Utilizing all study design elements to manage data

Data Collection Specialist

Who is a Data Collection Specialist?

Data collection specialists gather and collect data through the creation and administration of surveys, research, and interviews. They work closely with the data analyst. 

What does a Data Collection Specialist do?

  • Determine areas of research
  • Collect and analyze data
  • Interpret data analysis results
  • Use results to write papers, reports, and reviews
  • Present research results
  • Collaborate with research teams

Graphic Designer

Who is a Graphic Designer?

Graphic designers create graphics for uses. Even though they design alone, the whole process is based on collaboration with many people, including copywriters and creative directors.

What does a Graphic Designer do?

  • Conceptualizing visuals based on requirements
  • Creating images, layouts, illustrations, logos, and other designs 
  • Preparing rough drafts and present ideas
  • Testing graphics across various media
  • Studying design briefs and determining requirements
  • Scheduling projects and defining budget constraints

QA Specialist

Who is a QA Specialist?

Quality assurance (QA) specialists ensure that all production processes are controlled and monitored for quality and compliance. Their role is about delivering a product that meets all the standards and requirements. 

What does a QA Specialist do?

  • Providing management and control of processes
  • Maintaining the quality of products
  • Processing quality audits and quality assurance reviews
  • Documenting new and existing processes
  • Ensuring compliance with laws and regulations
  • Training and mentoring the quality assurance team

Software Tester

Who is a Software Tester?

Software Testers check the quality of software development and deployment. They perform automated and manual tests to ensure the software created by developers is fit for purpose. 

What does a Software Tester do?

  • Reviewing software requirements
  • Designing and running test scenarios for usability 
  • Analyzing results on database impacts, errors or bugs, and usability
  • Preparing statements and reporting to the software design team

AI - use cases

Business Management

  • Spam filters
  • Smart email categorization
  • Voice-to-text features
  • Smart personal assistants ( Siri, Cortana and Google Now)
  • Automated responders and online customer support
  • Process automation
  • Sales and business forecasting
  • Security surveillance
  • Smart devices that adjust according to behaviour
  • Automated insights, especially for data-driven industries (e.g. financial services or e-commerce)


  • Smart searches and relevance features
  • Personalization-as-a-service
  • Product recommendations and purchase predictions
  • Fraud detection and prevention for online transactions
  • Dynamic price optimization 
  • Drop-off detection


  • Recommendations and content curation
  • Personalization of news feeds
  • Pattern and image recognition
  • Language recognition (to digest unstructured data from customers and sales prospects)
  • Ad targeting and optimized, real-time bidding
  • Customer segmentation
  • Social semantics and sentiment analysis
  • Automated web design
  • Predictive customer service
  • Intelligent lead scoring
  • Automated competitive analysis
  • Campaign analysis
  • Conversion attribution
  • Image recognition 
  • Content matching
  • SEO image optimization

Finance & Banking

  • Automated invoice processing
  • Automated tax allocation and returns
  • Personalized and differentiated mobile banking experiences
  • Secure, user-friendly, and lean methodologies
  • Performance-oriented microservices architecture
  • Compliance safety apps
  • NLP to detect the risk of insurance
  • Fraud detection


  • Personalized medicine
  • Machine learning to diagnose infectious disease
  • Predictive analytics to verify the need for surgery
  • Healthcare apps as medical assistants
  • AI-powered wearables to track health conditions
  • Early detection of dementia
  • Intelligent robots in surgery
  • AI in drug discovery and production
  • Converting doctors’ unstructured notes with NLP
  • Image analysis for medical diagnostics
  • Automating administrative tasks


  • Workforce management
  • Supply chain management
  • Inventory management
  • Facility management
  • Predictive maintenance


  • eLearning AI apps
  • Learner’s forums
  • Reference guides/tutorials
  • Performance tracking
  • Personalized learning
  • Increased accessibility

Autonomous Vehicles

  • Leading a vehicle to a gas or recharge station when low on fuel or charge
  • Incorporating speech recognition for advanced communication with travellers
  • Adjusting directions based on general traffic conditions to find the quickest route

AI Autonomy Level

What is AI Autonomy Level?

AI Autonomy Level is a concept that describes the degree of independence that an artificial intelligence system can exhibit in making decisions or performing tasks. These levels range from fully manual systems that require constant human intervention, to completely autonomous systems that can operate independently without any human guidance.

Here is a general breakdown of levels of autonomy in AI:

Level 0 – No Autonomy

The AI system has no autonomous functionality at this stage. It may perform basic operations under direct human control but doesn’t make decisions or take actions independently.

Level 1 – Assisted Autonomy

This stage involves systems that can help human operators by performing specific tasks under their supervision. For example, AI might suggest actions or offer predictive analysis but requires human input.

Level 2 – Partial Autonomy

The AI system at this level can take over some tasks without human intervention, but overall control is still mainly in human hands. It would be like automated braking or lane-keeping systems in a self-driving car context.

Level 3 – Conditional Autonomy

Systems at this level can handle most tasks autonomously under certain conditions. They can make decisions based on their programming and the data they process, but they still require human oversight and may need human intervention in scenarios they’re not programmed to handle.

Level 4 – High Autonomy

The AI system can perform all tasks autonomously under most conditions at this stage. They can adapt to new situations and handle emergencies, although they may fail in rare or highly complex scenarios. A human operator might still be necessary but isn’t required to monitor the system at all times.

Level 5 – Full Autonomy

This is the highest level of AI autonomy. Systems at this level can perform any task that a human could under any condition. They can learn and adapt to new situations independently and don’t require human intervention or oversight.