

More and more companies are employing data scientists. In fact, the number has nearly doubled in recent years, indicating the importance of this profession for the modern workplace.
Additionally, data science has become a highly lucrative career. Professionals easily make over $120,000 annually, which is why it’s one of the most popular occupations.
This article will cover all you need to know about data science. We’ll define the term, its main applications, and essential elements.
What Is Data Science?
Data science analyzes raw information to provide actionable insights. Data scientists who retrieve this data utilize cutting-edge equipment and algorithms. After the collection, they analyze and break down the findings to make them readable and understandable. This way, managers, owners, and stakeholders can make informed strategic decisions.
Data Science Meaning
Although most data science definitions are relatively straightforward, there’s a lot of confusion surrounding this topic. Some people believe the field is about developing and maintaining data storage structures, but that’s not the case. It’s about analyzing data storage solutions to solve business problems and anticipate trends.
Hence, it’s important to distinguish between data science projects and those related to other fields. You can do so by testing your projects for certain aspects.
For instance, one of the most significant differences between data engineering and data science is that data science requires programming. Data scientists typically rely on code. As such, they clean and reformat information to increase its visibility across all systems.
Furthermore, data science generally requires the use of math. Complex math operations enable professionals to process raw data and turn it into usable insights. For this reason, companies require their data scientists to have high mathematical expertise.
Finally, data science projects require interpretation. The most significant difference between data scientists and some other professionals is that they use their knowledge to visualize and interpret their findings. The most common interpretation techniques include charts and graphs.
Data Science Applications
Many questions arise when researching data science. In particular, what are the applications of data science? It can be implemented for a variety of purposes:
- Enhancing the relevance of search results – Search engines used to take forever to provide results. The wait time is minimal nowadays. One of the biggest factors responsible for this improvement is data science.
- Adding unique flair to your video games – All gaming areas can gain a lot from data science. High-end games based on data science can analyze your movements to anticipate and react to your decisions, making the experience more interactive.
- Risk reduction – Several financial giants, such as Deloitte, hire data scientists to extract key information that lets them reduce business risks.
- Driverless vehicles – Technology that powers self-driving vehicles identifies traffic jams, speed limits, and other information to make driving safer for all participants. Data science-based cars can also help you reach your destination sooner.
- Ad targeting – Billboards and other forms of traditional marketing can be effective. But considering the number of online consumers is over 2.6 billion, organizations need to shift their promotion activities online. Data science is the answer. It lets organizations improve ad targeting by offering insights into consumer behaviors.
- AR optimization – AR brands can take a number of approaches to refining their headsets. Data science is one of them. The algorithms involved in data science can improve AR machines, translating to a better user experience.
- Premium recognition features – Siri might be the most famous tool developed through data science methods.
Learn Data Science
If you want to learn data science, understanding each stage of the process is an excellent starting point.
Data Collection
Data scientists typically start their day with data collection – gathering relevant information that helps them anticipate trends and solve problems. There are several methods associated with collecting data.
Data Mining
Data mining is great for anticipating outcomes. The procedure correlates different bits of information and enables you to detect discrepancies.
Web Scraping
Web scraping is the process of collecting data from web pages. There are different web scraping techniques, but most professionals utilize computer bots. This technique is faster and less prone to error than manual data discovery.
Remember that while screen scraping and web scraping are often used interchangeably, they’re not the same. The former merely copies screen pixels after recognizing them from various user interface components. The latter is a more extensive procedure that recovers the HTML code and any information stored within it.
Data Acquisition
Data acquisition is a form of data collection that garners information before storing it on your cloud-based servers or other solutions. Companies can collect information with specialized sensors and other devices. This equipment makes up their data acquisition systems.
Data Cleaning
You only need usable and original information in your system. Duplicate and redundant data can be a major obstacle, which is why you should use data cleaning. It removes contradictory information and helps you separate the wheat from the chaff.
Data Preprocessing
Data preprocessing prepares your data sets for other processes. Once it’s done, you can move on to information transformation, normalization, and analysis.
Data Transformation
Data transformation turns one version of information into another. It transforms raw data into usable information.
Data Normalization
You can’t start your data analysis without normalizing the information. Data normalization helps ensure that your information has uniform organization and appearance. It makes data sets more cohesive by removing illogical or unnecessary details.
Data Analysis
The next step in the data science lifecycle is data analysis. Effective data analysis provides more accurate data, improves customer insights and targeting, reduces operational costs, and more. Following are the main types of data analysis:
Exploratory Data Analysis
Exploratory data analysis is typically the first analysis performed in the data science lifecycle. The aim is to discover and summarize key features of the information you want to discuss.
Predictive Analysis
Predictive analysis comes in handy when you wish to forecast a trend. Your system uses historical information as a basis.
Statistical Analysis
Statistical analysis evaluates information to discover useful trends. It uses numbers to plan studies, create models, and interpret research.
Machine Learning
Machine learning plays a pivotal role in data analysis. It processes enormous chunks of data quickly with minimal human involvement. The technology can even mimic a human brain, making it incredibly accurate.
Data Visualization
Preparing and analyzing information is important, but a lot more goes into data science. More specifically, you need to visualize information using different methods. Data visualization is essential when presenting your findings to a general audience because it makes the information easily digestible.
Data Visualization Tools
Many tools can help you expedite your data visualization and create insightful dashboards.
Here are some of the best data visualization tools:
- Zoho Analytics
- Datawrapper
- Tableau
- Google Charts
- Microsoft Excel
Data Visualization Techniques
The above tools contain a plethora of data visualization techniques:
- Line chart
- Histogram
- Pie chart
- Area plot
- Scatter plot
- Hexbin plots
- Word clouds
- Network diagrams
- Highlight tables
- Bullet graphs
Data Storytelling
You can’t have effective data presentation without next-level storytelling. It contextualizes your narrative and gives your audience a better understanding of the process. Data dashboards and other tools can be an excellent way to enhance your storytelling.
Data Interpretation
The success of your data science work depends on your ability to derive conclusions. That’s where data interpretation comes in. It features a variety of methods that let you review and categorize your information to solve critical problems.
Data Interpretation Tools
Rather than interpret data on your own, you can incorporate a host of data interpretation tools into your toolbox:
- Layer – You can easily step up your data interpretation game with Layer. You can send well-designed spreadsheets to all stakeholders for improved visibility. Plus, you can integrate the app with other platforms you use to elevate productivity.
- Power Bi – A vast majority of data scientists utilize Power BI. Its intuitive interface enables you to develop and set up customized interpretation tools, offering a tailored approach to data science.
- Tableau – If you’re looking for another straightforward yet powerful platform, Tableau is a fantastic choice. It features robust dashboards with useful insights and synchronizes well with other applications.
- R – Advanced users can develop exceptional data interpretation graphs with R. This programming language offers state-of-the-art interpretation tools to accelerate your projects and optimize your data architecture.
Data Interpretation Techniques
The two main data interpretation techniques are the qualitative method and the quantitative method.
The qualitative method helps you interpret qualitative information. You present your findings using text instead of figures.
By contrast, the quantitative method is a numerical data interpretation technique. It requires you to elaborate on your data with numbers.
Data Insights
The final phase of the data science process involves data insights. These give your organization a complete picture of the information you obtained and interpreted, allowing stakeholders to take action on company problems. That’s especially true with actionable insights, as they recommend solutions for increasing productivity and profits.
Climb the Data Science Career Ladder, Starting From the Basics
The first step to becoming a data scientist is understanding the essence of data science and its applications. We’ve given you the basics involved in this field – the rest is up to you. Master every stage of the data science lifecycle, and you’ll be ready for a rewarding career path.
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During the Open Institute of Technology’s (OPIT) 2025 graduation day, the OPIT team interviewed graduating student Irene about her experience with the MSc in Applied Data Science and AI. The interview focused on how Irene juggled working full-time with her study commitments and the value of the final Capstone project, which is part of all OPIT’s master’s programs.
Irene, a senior developer at ReActive, said she chose to study at OPIT to update her skills for the current and future job market.
OPIT’s MSc in Applied Data Science and AI
In her interview, Irene said she appreciated how OPIT’s course did not focus purely on the hard mathematics behind technologies such as AI and cloud computing, but also on how these technologies can be applied to real business challenges.
She said she appreciated how the course gave her the skills to explain to stakeholders with limited technical knowledge how technology can be leveraged to solve business problems, but it also equipped her to engage with technical teams using their language and jargon. These skills help graduates bridge the gap between management and technology to drive innovation and transformation.
Irene chose to continue working full-time while studying and appreciated how her course advisor helped her plan her study workload around her work commitments “down to the minute” so that she never missed a deadline or was overcome by excessive stress.
She said she would recommend the program to people at any stage in their career who want to adapt to the current job market. She also praised the international nature of the program, in terms of both the faculty and the cohort, as working beyond borders promises to be another major business trend in the coming years.
Capstone Project
Irene described the most fulfilling part of the program as the final Capstone project, which allowed her to apply what she had learned to a real-life challenge.
The Capstone Project and Dissertation, also called the MSc Thesis, is a significant project aimed at consolidating skills acquired during the program through a long-term research project.
Students, with the help of an OPIT supervisor, develop and realize a project proposal as part of the final term of their master’s journey, investigating methodological and practical aspects in program domains. Internships with industrial partners to deliver the project are encouraged and facilitated by OPIT’s staff.
The Capstone project allows students to demonstrate their mastery of their field and the skills they’ve learned when talking to employers as part of the hiring process.
Capstone Project: AI Meets Art
Irene’s Capstone project, “Call Me VasarAI: An AI-Powered Framework for Artwork Recognition and Storytelling,” focused on using AI to bridge the gap between art and artificial intelligence over time, enhancing meaning through contextualization. She developed an AI-powered platform that allows users to upload a work of art and discover the style (e.g. Expressionism), the name of the artist, and a description of the artwork within an art historical context.
Irene commented on how her supervisor helped her fine-tune her ideas into a stronger project and offered continuous guidance throughout the process with weekly progress updates. After defending her thesis in January, she noted how the examiners did not just assess her work but guided her on what could be next.
Other Example Capstone Projects
Irene’s success is just one example of a completed OPIT Capstone project. Below are further examples of both successful projects and projects currently underway.
Elina delivered her Capstone project on predictive modeling of natural disasters using data science and machine learning techniques to analyze global trends in natural disasters and their relationships with climate change-related and socio-economic factors.
According to Elina: “This hands-on experience has reinforced my theoretical and practical abilities in data science and AI. I appreciate the versatility of these skills, which are valuable across many domains. This project has been challenging yet rewarding, showcasing the real-world impact of my academic learning and the interdisciplinary nature of data science and AI.”
For his Capstone project, Musa worked on finding the optimal pipeline to fine-tune a language learning model (LLM) based on the specific language and model, considering EU laws on technological topics such as GDPR, DSA, DME, and the AI Act, which are translated into several languages.
Musa stated: “This Capstone project topic aligns perfectly with my initial interests when applying to OPIT. I am deeply committed to developing a pipeline in the field of EU law, an area that has not been extensively explored yet.”
Tamas worked with industry partner Solergy on his Capstone project, working with generative AI to supercharge lead generation, boost SEO performance, and deliver data-driven marketing insights in the realm of renewable energy.
OPIT’s Master’s Courses
All of OPIT’s master’s courses include a final Capstone project to be completed over one 13-week term in the 90 ECTS program and over two terms in the 120 ECTS program.
The MSc in Digital Business and Innovation is designed for professionals who want to drive digital innovation in both established companies and new digital-native contexts. It covers digital business foundations and the applications of new technologies in business contexts. It emphasizes the use of AI to drive innovation and covers digital entrepreneurship, digital product management, and growth hacking.
The MSc in Responsible Artificial Intelligence combines technical expertise with a focus on the ethical implications of modern AI. It focuses on real-world applications in areas like natural language processing and industry automation, with a focus on sustainable AI systems and environmental impact.
The MSc in Enterprise Cybersecurity prepares students to fulfill the market need for versatile cybersecurity solutions, emphasizing hands-on experience and soft-skills development.
The MSc in Applied Data Science and AI focuses on the intersection between management and technology. It covers the underlying fundamentals, methodologies and tools needed to solve real-life business problems that can be approached using data science and AI.

In May 2025, Greta Maiocchi, Head of Marketing and Administration at the Open Institute of Technology (OPIT), went online with Stefania Tabi, OPIT Career Services Counselor, to discuss how OPIT helps students translate their studies into a career.
You can access OPIT Career Services throughout your course of study to help with making the transition from student to professional. Stefania specifically discussed what companies and businesses are looking for and how OPIT Career Services can help you stand out and find a desirable career with your degree.
What Companies Want
OPIT degrees are tailored to a wide range of individuals, with bachelor’s degrees for those looking to establish a career and master’s degrees for experienced professionals hoping to elevate their skills to meet the current market demand.
OPIT’s degrees establish the foundation of the key technological skills that are set to reshape industries shortly, in particular artificial intelligence (AI), big data, cloud computing, and cybersecurity.
Stefania shared how companies recruiting tech talent are looking for three types of skills:
- Builders – These are the superstars of the industry today, capable of developing the technologies that will transform the industry. These roles include AI engineers, cloud architects, and web developers.
- Protectors – Cybercrime is expected to cost the world $10.5 trillion by the end of 2025, which means companies place a high value on cybersecurity professionals capable of protecting their investment, data, and intellectual property (IP).
- Decoders – Industry is producing more data than ever before, with global data storage projected to exceed 200 zettabytes this year. Businesses seek professionals who can extract value from that data, such as data scientists and data strategists.
Growing Demand
Stefania also shared statistics about the growing demand for these roles. According to the World Economic Forum, there will be a 30-35% greater demand for roles such as data analysts and scientists, big data specialists, business intelligence analysts, data engineers, and database and network professionals by 2027.
The U.S. Bureau of Labor Statistics, meanwhile, predicts that by 2032, the demand for information security will increase by 33.8%, by 21.5% for software developers, by 10.4% for computer network architects, and by 9.9% for computer system analysts. Finally, the McKinsey Global Institute predicts a similar 15-25% increase in demand for technology professionals in the business services sector.
How Career Support Makes a Difference
Next, Stefania explained that while learning essential skills is vital to accessing this growing job market, high demand does not guarantee entry. Today, professionals looking for jobs in the technology field must stand out from the hundreds of applicants for each position with high-level skills.
Applicants demonstrate technical expertise in relevant fields by completing OPIT’s courses. They also need to prove that they can deliver results, demonstrating not just what they know but how they have applied what they know to transform or benefit a business. Professionals also need adaptability, adaptive problem-solving skills, and a commitment to continuous learning. OPIT’s final Capstone projects can be an excellent way to demonstrate the value of newly acquired skills.
Each OPIT program prepares students for future careers by providing dedicated support and academic guidance at every step.
What Kind of Support Does Career Services Offer?
Career Services is specifically focused on assisting students in making the transition to the job market, and you can make an appointment with them at any time during your studies. Stefania gave some specific examples of how Career Services can support students on their journey into the career market.
Stefania said she begins by talking with students and discussing what they truly value to help them discover the type of career that aligns with their strengths. With students who are still undecided on how to start to build their careers, she helps them craft a tailored job and internship search plan.
Stefania has also worked with students who want to stand out during the job application process among the hundreds of applicants. This includes hands-on help in reframing resumes, tailoring LinkedIn profiles, and developing cover letters that tell a unique story.
Finally, Stefania has assisted students in preparing for interviews, helping them research the company, develop intelligent questions about the role to ask the interviewer and engage in mock interviews with an experienced recruiter.
Connecting With Employers
OPIT Career Services also offers students exposure to a wide range of employers and the opportunity to build relationships through masterclasses, career talks, and industry roundtables. The office also helps students build career-ready skills through interactive, hands-on workshops and hosts virtual career fairs with top recruiters.
Career Services also plays an integral role in connecting students with companies for their Capstone project in the final phase of their master’s program. So far, students have worked with companies including Sintica, Cosmica, Cisco, PayPal, Morgan Stanley, AWS, Dylog, and Accenture. Projects have included developing predictive modeling for natural disasters and fine-tuning AI to answer questions about EU tech laws in multiple languages.
What Kinds of Jobs Have OPIT Graduates Secured?
Stefania capped off her talk by sharing some of the positions that OPIT graduates have now fulfilled, including:
- Chief Information Security Officer at MOMO for MTN mobile services in Nigeria
- Data Analyst at ISX Financial in Cyprus
- Head of Sustainability Office at Banca Popolare di Sondrio in Italy
- Data Analyst at Numisma Group in Cyprus
- Senior Software Engineer at Neaform in Italy
OPIT Courses
OPIT offers both foundational bachelor’s degrees and advanced master’s courses, which are both accessible with any bachelor’s degree (it does not have to be in the field of computer science).
Choose between a BSc in Modern Computer Science for a strong technical base or a BSc in Digital Business to focus on applications.
Meanwhile, courses that involve a final Capstone project include an MSc in Applied Data Science and AI, Digital Business and Innovation, Enterprise Cybersecurity, and Responsible Artificial Intelligence.
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