Algorithms are the essence of data mining and machine learning – the two processes 60% of organizations utilize to streamline their operations. Businesses can choose from several algorithms to polish their workflows, but the decision tree algorithm might be the most common.
This algorithm is all about simplicity. It branches out in multiple directions, just like trees, and determines whether something is true or false. In turn, data scientists and machine learning professionals can further dissect the data and help key stakeholders answer various questions.
This only scratches the surface of this algorithm – but it’s time to delve deeper into the concept. Let’s take a closer look at the decision tree machine learning algorithm, its components, types, and applications.
What Is Decision Tree Machine Learning?
The decision tree algorithm in data mining and machine learning may sound relatively simple due to its similarities with standard trees. But like with conventional trees, which consist of leaves, branches, roots, and many other elements, there’s a lot to uncover with this algorithm. We’ll start by defining this concept and listing the main components.
Definition of Decision Tree
If you’re a college student, you learn in two ways – supervised and unsupervised. The same division can be found in algorithms, and the decision tree belongs to the former category. It’s a supervised algorithm you can use to regress or classify data. It relies on training data to predict values or outcomes.
Components of Decision Tree
What’s the first thing you notice when you look at a tree? If you’re like most people, it’s probably the leaves and branches.
The decision tree algorithm has the same elements. Add nodes to the equation, and you have the entire structure of this algorithm right in front of you.
- Nodes – There are several types of nodes in decision trees. The root node is the parent of all nodes, which represents the overriding message. Chance nodes tell you the probability of a certain outcome, whereas decision nodes determine the decisions you should make.
- Branches – Branches connect nodes. Like rivers flowing between two cities, they show your data flow from questions to answers.
- Leaves – Leaves are also known as end nodes. These elements indicate the outcome of your algorithm. No more nodes can spring out of these nodes. They are the cornerstone of effective decision-making.
Types of Decision Trees
When you go to a park, you may notice various tree species: birch, pine, oak, and acacia. By the same token, there are multiple types of decision tree algorithms:
- Classification Trees – These decision trees map observations about particular data by classifying them into smaller groups. The chunks allow machine learning specialists to predict certain values.
- Regression Trees – According to IBM, regression decision trees can help anticipate events by looking at input variables.
Decision Tree Algorithm in Data Mining
Knowing the definition, types, and components of decision trees is useful, but it doesn’t give you a complete picture of this concept. So, buckle your seatbelt and get ready for an in-depth overview of this algorithm.
Overview of Decision Tree Algorithms
Just as there are hierarchies in your family or business, there are hierarchies in any decision tree in data mining. Top-down arrangements start with a problem you need to solve and break it down into smaller chunks until you reach a solution. Bottom-up alternatives sort of wing it – they enable data to flow with some supervision and guide the user to results.
Popular Decision Tree Algorithms
- ID3 (Iterative Dichotomiser 3) – Developed by Ross Quinlan, the ID3 is a versatile algorithm that can solve a multitude of issues. It’s a greedy algorithm (yes, it’s OK to be greedy sometimes), meaning it selects attributes that maximize information output.
- 5 – This is another algorithm created by Ross Quinlan. It generates outcomes according to previously provided data samples. The best thing about this algorithm is that it works great with incomplete information.
- CART (Classification and Regression Trees) – This algorithm drills down on predictions. It describes how you can predict target values based on other, related information.
- CHAID (Chi-squared Automatic Interaction Detection) – If you want to check out how your variables interact with one another, you can use this algorithm. CHAID determines how variables mingle and explain particular outcomes.
Key Concepts in Decision Tree Algorithms
No discussion about decision tree algorithms is complete without looking at the most significant concept from this area:
Entropy
As previously mentioned, decision trees are like trees in many ways. Conventional trees branch out in random directions. Decision trees share this randomness, which is where entropy comes in.
Entropy tells you the degree of randomness (or surprise) of the information in your decision tree.
Information Gain
A decision tree isn’t the same before and after splitting a root node into other nodes. You can use information gain to determine how much it’s changed. This metric indicates how much your data has improved since your last split. It tells you what to do next to make better decisions.
Gini Index
Mistakes can happen, even in the most carefully designed decision tree algorithms. However, you might be able to prevent errors if you calculate their probability.
Enter the Gini index (Gini impurity). It establishes the likelihood of misclassifying an instance when choosing it randomly.
Pruning
You don’t need every branch on your apple or pear tree to get a great yield. Likewise, not all data is necessary for a decision tree algorithm. Pruning is a compression technique that allows you to get rid of this redundant information that keeps you from classifying useful data.
Building a Decision Tree in Data Mining
Growing a tree is straightforward – you plant a seed and water it until it is fully formed. Creating a decision tree is simpler than some other algorithms, but quite a few steps are involved nevertheless.
Data Preparation
Data preparation might be the most important step in creating a decision tree. It’s comprised of three critical operations:
Data Cleaning
Data cleaning is the process of removing unwanted or unnecessary information from your decision trees. It’s similar to pruning, but unlike pruning, it’s essential to the performance of your algorithm. It’s also comprised of several steps, such as normalization, standardization, and imputation.
Feature Selection
Time is money, which especially applies to decision trees. That’s why you need to incorporate feature selection into your building process. It boils down to choosing only those features that are relevant to your data set, depending on the original issue.
Data Splitting
The procedure of splitting your tree nodes into sub-nodes is known as data splitting. Once you split data, you get two data points. One evaluates your information, while the other trains it, which brings us to the next step.
Training the Decision Tree
Now it’s time to train your decision tree. In other words, you need to teach your model how to make predictions by selecting an algorithm, setting parameters, and fitting your model.
Selecting the Best Algorithm
There’s no one-size-fits-all solution when designing decision trees. Users select an algorithm that works best for their application. For example, the Random Forest algorithm is the go-to choice for many companies because it can combine multiple decision trees.
Setting Parameters
How far your tree goes is just one of the parameters you need to set. You also need to choose between entropy and Gini values, set the number of samples when splitting nodes, establish your randomness, and adjust many other aspects.
Fitting the Model
If you’ve fitted your model properly, your data will be more accurate. The outcomes need to match the labeled data closely (but not too close to avoid overfitting) if you want relevant insights to improve your decision-making.
Evaluating the Decision Tree
Don’t put your feet up just yet. Your decision tree might be up and running, but how well does it perform? There are two ways to answer this question: cross-validation and performance metrics.
Cross-Validation
Cross-validation is one of the most common ways of gauging the efficacy of your decision trees. It compares your model to training data, allowing you to determine how well your system generalizes.
Performance Metrics
Several metrics can be used to assess the performance of your decision trees:
Accuracy
This is the proximity of your measurements to the requested values. If your model is accurate, it matches the values established in the training data.
Precision
By contrast, precision tells you how close your output values are to each other. In other words, it shows you how harmonized individual values are.
Recall
Recall is the number of data samples in the desired class. This class is also known as the positive class. Naturally, you want your recall to be as high as possible.
F1 Score
F1 score is the median value of your precision and recall. Most professionals consider an F1 of over 0.9 a very good score. Scores between 0.8 and 0.5 are OK, but anything less than 0.5 is bad. If you get a poor score, it means your data sets are imprecise and imbalanced.
Visualizing the Decision Tree
The final step is to visualize your decision tree. In this stage, you shed light on your findings and make them digestible for non-technical team members using charts or other common methods.
Applications of Decision Tree Machine Learning in Data Mining
The interest in machine learning is on the rise. One of the reasons is that you can apply decision trees in virtually any field:
- Customer Segmentation – Decision trees let you divide customers according to age, gender, or other factors.
- Fraud Detection – Decision trees can easily find fraudulent transactions.
- Medical Diagnosis – This algorithm allows you to classify conditions and other medical data with ease using decision trees.
- Risk Assessment – You can use the system to figure out how much money you stand to lose if you pursue a certain path.
- Recommender Systems – Decision trees help customers find their next product through classification.
Advantages and Disadvantages of Decision Tree Machine Learning
Advantages:
- Easy to Understand and Interpret – Decision trees make decisions almost in the same manner as humans.
- Handles Both Numerical and Categorical Data – The ability to handle different types of data makes them highly versatile.
- Requires Minimal Data Preprocessing – Preparing data for your algorithms doesn’t take much.
Disadvantages:
- Prone to Overfitting – Decision trees often fail to generalize.
- Sensitive to Small Changes in Data – Changing one data point can wreak havoc on the rest of the algorithm.
- May Not Work Well with Large Datasets – Naïve Bayes and some other algorithms outperform decision trees when it comes to large datasets.
Possibilities are Endless With Decision Trees
The decision tree machine learning algorithm is a simple yet powerful algorithm for classifying or regressing data. The convenient structure is perfect for decision-making, as it organizes information in an accessible format. As such, it’s ideal for making data-driven decisions.
If you want to learn more about this fascinating topic, don’t stop your exploration here. Decision tree courses and other resources can bring you one step closer to applying decision trees to your work.
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From the same author
Read the articleOpen Institute of Technology (OPIT) masterclasses bring students face-to-face with real-world business challenges. In OPIT’s July masterclass, OPIT Professor Francesco Derchi and Ph.D. candidate Robert Mario de Stefano explained the principles of regenerative businesses and how regeneration goes hand in hand with growth.
Regenerative Business Models
Professor Derchi began by explaining what exactly is meant by regenerative business models, clearly differentiating them from sustainable or circular models.
Many companies pursue sustainable business models in which they offset their negative impact by investing elsewhere. For example, businesses that are big carbon consumers will support nature regeneration projects. Circular business models are similar but are more focused on their own product chain, aiming to minimize waste by keeping products in use as long as possible through recycling. Both models essentially aim to have a “net-zero” negative impact on the environment.
Regenerative models are different because they actively aim to have a “net-positive” impact on the environment, not just offsetting their own use but actively regenerating the planet.
Massive Transformative Purpose
While regenerative business models are often associated with philanthropic endeavors, Professor Derchi explained that they do not have to be, and that investment in regeneration can be a driver of growth.
He discussed the importance of corporate purpose in the modern business space. Having a strong and clearly stated corporate purpose is considered essential to drive business decision-making, encourage employee buy-in, and promote customer loyalty.
But today, simple corporate missions, such as “make good shoes,” don’t go far enough. People are looking for a Massive Transformational Purpose (MTP) that can take the business to the next level.
Take, for example, Ben & Jerry’s. The business’s initial corporate purpose may have been to make great ice cream and serve it up in a way that people will enjoy. But the business really began to grow when they embraced an MTP. As they announced in their mission statement, “We believe that ice cream can change the world.” Their business activities also have the aim of advancing human rights and dignity, supporting social and economic justice, and protecting and restoring the Earth’s natural systems. While these aims are philanthropic, they have also helped the business grow.
RePlanet
Professor Derchi next talked about RePlanet, a business he recently worked to develop their MTP. Founded in 2015, RePlanet designs and implements customized renewable energy solutions for businesses and projects. The company already operates in the renewable energy field and ranked as the 21st fastest-growing business in Italy in 2023. So while they were already enjoying great success, Derchi worked with them to see if actively embracing a regenerative business model could unlock additional growth.
Working together, RePlanet moved towards an MTP of building a greener future based on today’s choices, ensuring a cleaner world for generations. Meeting this goal started with the energy products that RePlanet sells, such as energy systems that recover heat from dairy farms. But as the business’s MTP, it goes beyond that. RePlanet doesn’t just engage suppliers; it chooses partners that share its specific values. It also influences the projects they choose to work on – they prioritize high-impact social projects, such as recently installing photovoltaic energy systems at a local hospital in Nigeria – and how RePlanet treats its talent, acknowledging that people are the true energy of the company.
Regenerative Business Strategies
Based on work with RePlanet and other businesses, Derchi has identified six archetypal regenerative business strategies for businesses that want to have both a regenerative impact and drive growth:
- Regenerative Leadership – Laying the foundation for regeneration in a broader sense throughout the company
- Nature Regeneration – Strategies to improve the health of the natural world
- Social Regeneration – Regenerating human ecosystems through things such as fair-trade practices
- Responsible Sourcing – Empowering and strengthening suppliers and their communities
- Health & Well-being – Creating products and services that have a positive effect on customers
- Employee Focus – Improve work conditions, lives, and well-being of employees.
Case Studies
Building on the concept of regenerative business models, Roberto Mario de Stefano shared other case studies of businesses that are having a positive impact and enjoying growth thanks to regenerative business models and strategies.
Biorfarm
Biorfarm is a digital platform that supports small-scale agriculture by creating a direct link between small farmers and consumers. Cutting out the middleman in modern supply chains means that farmers earn about 50% more for their produce. They set consumers up as “digital farmers” who actively support and learn about farming activities to promote more conscious food consumption.
Their vision is to create a food economy in which those who produce food and those who consume it are connected. This moves consumers from passive cash cows for large corporations that prioritize profits over the well-being of farmers to actively supporting natural production and a more sustainable system.
Rifo Lab
Rifo Lab is a circular clothing brand with the vision of addressing the problem of overproduction in the clothing industry. Established in Prato, Italy, a traditional textile-producing area, the company produces clothes made from textile waste and biodegradable materials. There are no physical stores, and all orders must be placed online; everything is made to order, reducing excess production.
With an eye on social regeneration, all production takes place within 30 kilometers of their offices, allowing the business to support ethical and local production. They also work with companies that actively integrate migrants into the local community, sharing their local artisan crafts with future generations.
Ogyre
Ogyre is a digital platform that allows you to pay fishermen to fish for waste. When fishermen are out conducting their livelihood, they also collect a significant amount of waste from the ocean, especially plastic waste. Ogyre arranges for fishermen to get paid for collecting that waste, which in turn supports the local fishing communities, and then transforms the waste collected into new sustainable products.
Moving Towards a Regenerative Future
The masterclass concluded with a Q&A session, where it explained that working in regenerative businesses requires the same skills as any other business. But it also requires you to embrace a mindset where value comes from giving and that growth is about working together for a better future, and not just competition.
Read the articleRiccardo Ocleppo’s vision for the Open Institute of Technology (OPIT) started when he realized that his own university-level training had not properly prepared him for the modern workplace. Technological innovation is moving quickly and changing the nature of work, while university curricula evolve slowly, in part due to systems in place designed to preserve the quality of courses.
Ocleppo was determined to create a higher learning institution that filled the gap between the two realities – delivering high-quality education while preparing professionals to work in dynamic environments that keep pace with technology. Thus, OPIT opened enrolments in 2023 with a curriculum that created a unique bridge between the present and the future.
This is the story of one student, Ania Jaca, whose time at OPIT gave her the skills to connect her knowledge of product design to full system deployment.
Meet Ania
Ania is an example of an active professional who was able to identify what was missing in her own skills that would be needed if she wanted to advance her career in the direction she desired.
Ania is a highly skilled professional who was working on product and industrial design at Deloitte. She has an MA in product design, speaks five languages, studied in China, and is an avid boxer. She had the intelligence and the temperament to succeed in her career, but felt that she lacked the skills to advance and move from determining how products look to how systems really work, scale, and evolve.
Ania taught herself skills such as Python, artificial intelligence (AI), and cloud infrastructure, but soon realized that she needed a more structured education to go deeper. Thus, the search for her next steps began, and her introduction to OPIT.
OPIT appealed to Ania because it offered a fully EU-accredited MSc that she could pursue at her own pace, thanks to remote delivery and flexible hours. But more than that, it filled exactly the knowledge gap she was looking to build upon, teaching her technical foundations, but always with a focus on applications in the real world. Part of the appeal was the faculty, which includes professionals who are leaders in their field and who deal with current professional challenges on a daily basis, which they can bring into the classroom.
Ania enrolled in OPIT’s MSc in Applied Data Science & AI.
MSc in Applied Data Science and AI
This is OPIT’s first master’s program, which also launched in 2023, and is now one of four on offer. The course is designed for graduates like Ania who want a career at the intersection of management and technology. It is attractive to professionals who are already working in this area but lack the technical training to step into certain roles. OPIT requires no computer science prerequisites, so it accepted Ania with her MA in product design.
It is an intensive program that starts with foundational application courses in business, data science, machine learning, artificial intelligence, and problem-solving. The program then moves towards applying data science and AI methodologies and tools to real-life business problems.
The course combines theoretical study with a capstone project that lets students apply what they learn in the real world, either at their existing company or through internship programs. Many of the projects developed by students go on to become fundamental to the businesses they work with.
Ania’s Path Forward
Ania is working on her capstone project with Neperia Group, an Italian-based IT systems development company that works mostly with financial, insurance, and industrial companies. They specialize in developing analysis tools for existing software to enhance insight, streamline management, minimize the impact of corrective and evolutionary interventions, and boost performance.
Ania is specifically working on tools for assessing vulnerabilities in codebases as an advanced cybersecurity tool.
Ania credits her studies at OPIT for helping her build solid foundations in data science, machine learning, and cloud workflows, giving her a thorough understanding of digital products from end to end. She feels this has prepared her for roles at the intersection between infrastructure, security, and deployment, which is exactly where she wants to be. OPIT is excited to see where Ania’s career takes her in the coming years.
Preparing for the Future of Work
Overall, studying at OPIT has helped Ania and others like her prepare for the future of work. According to the Visual Capitalist, the fastest-growing jobs between 2025 and 2030 will be in big data (up by 110%), Fintech engineers (up by 95%), AI and machine learning specialists (up by 85%), software application developers (up by 60%), and security management specialists (up by 55%).
However, while these industries are growing, entry-level opportunities are declining in areas such as software development and IT. This is because AI now performs many of the tasks associated with those roles. Instead, companies are looking for experienced professionals to take on roles that involve more strategic oversight and innovative problem-solving. But how do recent graduates leapfrog past experienced professionals when there is a lack of entry-level positions to make the transition?
This is another challenge that OPIT addresses in its course design. Students don’t just learn the theory, OPIT actively encourages them to focus on applications, allowing them to build experience while studying. The capstone project consolidates this, enabling students to demonstrate to future employers their expertise at deploying technology to solve problems.
OPIT also has a dynamic Career Services department that specifically works with students to prepare them for the types of roles they want. This focus on not only learning but building a career is one of the elements that makes OPIT stand out in preparing graduates for the workplace.
-
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Open Institute of Technology (OPIT) masterclasses bring students face-to-face with real-world business challenges. In OPIT’s July masterclass, OPIT Professor Francesco Derchi and Ph.D. candidate Robert Mario de Stefano explained the principles of regenerative businesses and how regeneration goes hand in hand with growth.
Regenerative Business Models
Professor Derchi began by explaining what exactly is meant by regenerative business models, clearly differentiating them from sustainable or circular models.
Many companies pursue sustainable business models in which they offset their negative impact by investing elsewhere. For example, businesses that are big carbon consumers will support nature regeneration projects. Circular business models are similar but are more focused on their own product chain, aiming to minimize waste by keeping products in use as long as possible through recycling. Both models essentially aim to have a “net-zero” negative impact on the environment.
Regenerative models are different because they actively aim to have a “net-positive” impact on the environment, not just offsetting their own use but actively regenerating the planet.
Massive Transformative Purpose
While regenerative business models are often associated with philanthropic endeavors, Professor Derchi explained that they do not have to be, and that investment in regeneration can be a driver of growth.
He discussed the importance of corporate purpose in the modern business space. Having a strong and clearly stated corporate purpose is considered essential to drive business decision-making, encourage employee buy-in, and promote customer loyalty.
But today, simple corporate missions, such as “make good shoes,” don’t go far enough. People are looking for a Massive Transformational Purpose (MTP) that can take the business to the next level.
Take, for example, Ben & Jerry’s. The business’s initial corporate purpose may have been to make great ice cream and serve it up in a way that people will enjoy. But the business really began to grow when they embraced an MTP. As they announced in their mission statement, “We believe that ice cream can change the world.” Their business activities also have the aim of advancing human rights and dignity, supporting social and economic justice, and protecting and restoring the Earth’s natural systems. While these aims are philanthropic, they have also helped the business grow.
RePlanet
Professor Derchi next talked about RePlanet, a business he recently worked to develop their MTP. Founded in 2015, RePlanet designs and implements customized renewable energy solutions for businesses and projects. The company already operates in the renewable energy field and ranked as the 21st fastest-growing business in Italy in 2023. So while they were already enjoying great success, Derchi worked with them to see if actively embracing a regenerative business model could unlock additional growth.
Working together, RePlanet moved towards an MTP of building a greener future based on today’s choices, ensuring a cleaner world for generations. Meeting this goal started with the energy products that RePlanet sells, such as energy systems that recover heat from dairy farms. But as the business’s MTP, it goes beyond that. RePlanet doesn’t just engage suppliers; it chooses partners that share its specific values. It also influences the projects they choose to work on – they prioritize high-impact social projects, such as recently installing photovoltaic energy systems at a local hospital in Nigeria – and how RePlanet treats its talent, acknowledging that people are the true energy of the company.
Regenerative Business Strategies
Based on work with RePlanet and other businesses, Derchi has identified six archetypal regenerative business strategies for businesses that want to have both a regenerative impact and drive growth:
- Regenerative Leadership – Laying the foundation for regeneration in a broader sense throughout the company
- Nature Regeneration – Strategies to improve the health of the natural world
- Social Regeneration – Regenerating human ecosystems through things such as fair-trade practices
- Responsible Sourcing – Empowering and strengthening suppliers and their communities
- Health & Well-being – Creating products and services that have a positive effect on customers
- Employee Focus – Improve work conditions, lives, and well-being of employees.
Case Studies
Building on the concept of regenerative business models, Roberto Mario de Stefano shared other case studies of businesses that are having a positive impact and enjoying growth thanks to regenerative business models and strategies.
Biorfarm
Biorfarm is a digital platform that supports small-scale agriculture by creating a direct link between small farmers and consumers. Cutting out the middleman in modern supply chains means that farmers earn about 50% more for their produce. They set consumers up as “digital farmers” who actively support and learn about farming activities to promote more conscious food consumption.
Their vision is to create a food economy in which those who produce food and those who consume it are connected. This moves consumers from passive cash cows for large corporations that prioritize profits over the well-being of farmers to actively supporting natural production and a more sustainable system.
Rifo Lab
Rifo Lab is a circular clothing brand with the vision of addressing the problem of overproduction in the clothing industry. Established in Prato, Italy, a traditional textile-producing area, the company produces clothes made from textile waste and biodegradable materials. There are no physical stores, and all orders must be placed online; everything is made to order, reducing excess production.
With an eye on social regeneration, all production takes place within 30 kilometers of their offices, allowing the business to support ethical and local production. They also work with companies that actively integrate migrants into the local community, sharing their local artisan crafts with future generations.
Ogyre
Ogyre is a digital platform that allows you to pay fishermen to fish for waste. When fishermen are out conducting their livelihood, they also collect a significant amount of waste from the ocean, especially plastic waste. Ogyre arranges for fishermen to get paid for collecting that waste, which in turn supports the local fishing communities, and then transforms the waste collected into new sustainable products.
Moving Towards a Regenerative Future
The masterclass concluded with a Q&A session, where it explained that working in regenerative businesses requires the same skills as any other business. But it also requires you to embrace a mindset where value comes from giving and that growth is about working together for a better future, and not just competition.
Riccardo Ocleppo’s vision for the Open Institute of Technology (OPIT) started when he realized that his own university-level training had not properly prepared him for the modern workplace. Technological innovation is moving quickly and changing the nature of work, while university curricula evolve slowly, in part due to systems in place designed to preserve the quality of courses.
Ocleppo was determined to create a higher learning institution that filled the gap between the two realities – delivering high-quality education while preparing professionals to work in dynamic environments that keep pace with technology. Thus, OPIT opened enrolments in 2023 with a curriculum that created a unique bridge between the present and the future.
This is the story of one student, Ania Jaca, whose time at OPIT gave her the skills to connect her knowledge of product design to full system deployment.
Meet Ania
Ania is an example of an active professional who was able to identify what was missing in her own skills that would be needed if she wanted to advance her career in the direction she desired.
Ania is a highly skilled professional who was working on product and industrial design at Deloitte. She has an MA in product design, speaks five languages, studied in China, and is an avid boxer. She had the intelligence and the temperament to succeed in her career, but felt that she lacked the skills to advance and move from determining how products look to how systems really work, scale, and evolve.
Ania taught herself skills such as Python, artificial intelligence (AI), and cloud infrastructure, but soon realized that she needed a more structured education to go deeper. Thus, the search for her next steps began, and her introduction to OPIT.
OPIT appealed to Ania because it offered a fully EU-accredited MSc that she could pursue at her own pace, thanks to remote delivery and flexible hours. But more than that, it filled exactly the knowledge gap she was looking to build upon, teaching her technical foundations, but always with a focus on applications in the real world. Part of the appeal was the faculty, which includes professionals who are leaders in their field and who deal with current professional challenges on a daily basis, which they can bring into the classroom.
Ania enrolled in OPIT’s MSc in Applied Data Science & AI.
MSc in Applied Data Science and AI
This is OPIT’s first master’s program, which also launched in 2023, and is now one of four on offer. The course is designed for graduates like Ania who want a career at the intersection of management and technology. It is attractive to professionals who are already working in this area but lack the technical training to step into certain roles. OPIT requires no computer science prerequisites, so it accepted Ania with her MA in product design.
It is an intensive program that starts with foundational application courses in business, data science, machine learning, artificial intelligence, and problem-solving. The program then moves towards applying data science and AI methodologies and tools to real-life business problems.
The course combines theoretical study with a capstone project that lets students apply what they learn in the real world, either at their existing company or through internship programs. Many of the projects developed by students go on to become fundamental to the businesses they work with.
Ania’s Path Forward
Ania is working on her capstone project with Neperia Group, an Italian-based IT systems development company that works mostly with financial, insurance, and industrial companies. They specialize in developing analysis tools for existing software to enhance insight, streamline management, minimize the impact of corrective and evolutionary interventions, and boost performance.
Ania is specifically working on tools for assessing vulnerabilities in codebases as an advanced cybersecurity tool.
Ania credits her studies at OPIT for helping her build solid foundations in data science, machine learning, and cloud workflows, giving her a thorough understanding of digital products from end to end. She feels this has prepared her for roles at the intersection between infrastructure, security, and deployment, which is exactly where she wants to be. OPIT is excited to see where Ania’s career takes her in the coming years.
Preparing for the Future of Work
Overall, studying at OPIT has helped Ania and others like her prepare for the future of work. According to the Visual Capitalist, the fastest-growing jobs between 2025 and 2030 will be in big data (up by 110%), Fintech engineers (up by 95%), AI and machine learning specialists (up by 85%), software application developers (up by 60%), and security management specialists (up by 55%).
However, while these industries are growing, entry-level opportunities are declining in areas such as software development and IT. This is because AI now performs many of the tasks associated with those roles. Instead, companies are looking for experienced professionals to take on roles that involve more strategic oversight and innovative problem-solving. But how do recent graduates leapfrog past experienced professionals when there is a lack of entry-level positions to make the transition?
This is another challenge that OPIT addresses in its course design. Students don’t just learn the theory, OPIT actively encourages them to focus on applications, allowing them to build experience while studying. The capstone project consolidates this, enabling students to demonstrate to future employers their expertise at deploying technology to solve problems.
OPIT also has a dynamic Career Services department that specifically works with students to prepare them for the types of roles they want. This focus on not only learning but building a career is one of the elements that makes OPIT stand out in preparing graduates for the workplace.
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