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 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 is great for anticipating outcomes. The procedure correlates different bits of information and enables you to detect discrepancies.
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 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.
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 prepares your data sets for other processes. Once it’s done, you can move on to information transformation, normalization, and analysis.
Data transformation turns one version of information into another. It transforms raw data into usable information.
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.
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 comes in handy when you wish to forecast a trend. Your system uses historical information as a basis.
Statistical analysis evaluates information to discover useful trends. It uses numbers to plan studies, create models, and interpret research.
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.
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
- Google Charts
- Microsoft Excel
Data Visualization Techniques
The above tools contain a plethora of data visualization techniques:
- Line chart
- Pie chart
- Area plot
- Scatter plot
- Hexbin plots
- Word clouds
- Network diagrams
- Highlight tables
- Bullet graphs
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.
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.
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.
Soon, we will be launching four new Degrees for AY24-25 at OPIT – Open Institute of Technology
I want to offer a behind-the-scenes look at the Product Definition process that has shaped these upcoming programs.
🚀 Phase 1: Discovery (Late May – End of July)
Our journey began with intensive brainstorming sessions with OPIT’s Academic Board (Francesco Profumo, Lorenzo Livi, Alexiei Dingli, Andrea Pescino, Rosario Maccarrone) . We also conducted 50+ interviews with tech and digital entrepreneurs (both from startups and established firms), academics and students. Finally, we deep-dived into the “Future of Jobs 2023” report by the World Economic Forum and other valuable research.
🔍 Phase 2: Selection – Crafting Our Roadmap (July – August)
Our focus? Introducing new degrees addressing critical workforce shortages and upskilling/reskilling needs for the next 5-10 years, promising significant societal impact and a broad market reach.
Our decision? To channel our energies on full BScs and MScs, and steer away from shorter courses or corporate-focused offerings. This aligns perfectly with our core mission.
💡 Focus Areas Unveiled!
We’re thrilled to concentrate on pivotal fields like:
- Advanced AI
- Digital Business
- Metaverse & Gaming
- Cloud Computing (less “glamorous”, but market demand is undeniable).
🎓 Phase 3: Definition – Shaping the Degrees (August – November)
With an expert in each of the above fields, and with the strong collaboration of our Academic Director, Prof. Lorenzo Livi , we embarked on a rigorous “drill-down process”. Our goal? To meld modern theoretical knowledge with cutting-edge competencies and skills. This phase included interviewing over 60+ top academics, industry professionals, and students and get valuable, program-specific, insights from our Marketing department.
🌟 Phase 4: Accreditation and Launch – The Final Stretch
We’re currently in the accreditation process, gearing up for the launch. The focus is now shifting towards marketing, working closely with Greta Maiocchi and her Marketing and Admissions team. Together, we’re translating our new academic offering into a compelling value proposition for the market.
Stay tuned for more updates!
Far from being a temporary educational measure that came into its own during the pandemic, online education is providing students from all over the world with new ways to learn. That’s proven by statistics from Oxford Learning College, which point out that over 100 million students are now enrolled in some form of online course.
The demand for these types of courses clearly exists.
In fact, the same organization indicates that educational facilities that introduce online learning see a 42% increase in income – on average – suggesting that the demand is there.
Enter the Open Institute of Technology (OPIT).
Delivering three online courses – a Bachelor’s degree in computer science and two Master’s degrees – with more to come, OPIT is positioning itself as a leader in the online education space. But why is that? After all, many institutions are making the jump to e-learning, so what separates OPIT from the pack?
Here, you’ll discover the answers as you delve into the five reasons why you should trust OPIT for your online education.
Reason 1 – A Practical Approach
OPIT focuses on computer science education – a field in which theory often dominates the educational landscape. The organization’s Rector, Professor Francesco Profumo, makes this clear in a press release from June 2023. He points to a misalignment between what educators are teaching computer science students and what the labor market actually needs from those students as a key problem.
“The starting point is the awareness of the misalignment,” he says when talking about how OPIT structures its online courses. “That so-called mismatch is generated by too much theory and too little practical approach.” In other words, students in many classes spend far too much time learning the “hows” and “whys” behind computerized systems without actually getting their hands dirty with real work that gives them practical experience in using those systems.
OPIT takes a different approach.
It has developed a didactic approach that focuses far more on the practical element than other courses. That approach is delivered through a combination of classroom sessions – such as live lessons and masterclasses – and practical work offered through quizzes and exercises that mimic real-world situations.
An OPIT student doesn’t simply learn how computers work. They put their skills into practice through direct programming and application, equipping them with skills that are extremely attractive to major employers in the tech field and beyond.
Reason 2 – Flexibility Combined With Support
Flexibility in how you study is one of the main benefits of any online course.
You control when you learn and how you do it, creating an environment that’s beneficial to your education rather than being forced into a classroom setting with which you may not feel comfortable. This is hardly new ground. Any online educational platform can claim that it offers “flexibility” simply because it provides courses via the web.
Where OPIT differs is that it combines that flexibility with unparalleled support bolstered by the experiences of teachers employed from all over the world. The founder and director of OPIT, Riccardo Ocleppo, sheds more light on this difference in approach when he says, “We believe that education, even if it takes place physically at a distance, must guarantee closeness on all other aspects.” That closeness starts with the support offered to students throughout their entire study period.
Tutors are accessible to students at all times. Plus, every participant benefits from weekly professor interactions, ensuring they aren’t left feeling stuck on an educational “island” and have to rely solely on themselves for their education. OPIT further counters the potential isolation that comes with online learning with a Student Support team to guide students through any difficulties they may have with their courses.
In this focus on support, OPIT showcases one of its main differences from other online platforms.
You don’t simply receive course material before being told to “get on with it.” You have the flexibility to learn at your own pace while also having a support structure that serves as a foundation for that learning.
Reason 3 – OPIT Can Adapt to Change Quickly
The field of computer science is constantly evolving.
In the 2020s alone, we’ve seen the rise of generative AI – spurred on by the explosive success of services like ChatGPT – and how those new technologies have changed the way that people use computers.
Riccardo Ocleppo has seen the impact that these constant evolutions have had on students. Before founding OPIT, he was an entrepreneur who received first-hand experience of the fact that many traditional educational institutions struggle to adapt to change.
“Traditional educational institutions are very slow to adapt to this wave of new technologies and trends within the educational sector,” he says. He points to computer science as a particular issue, highlighting the example of a board in Italy of which he is a member. That board – packed with some of the country’s most prestigious tech universities – spent three years eventually deciding to add just two modules on new and emerging technologies to their study programs.
That left Ocleppo feeling frustrated.
When he founded OPIT, he did so intending to make it an adaptable institution in which courses were informed by what the industry needs. Every member of its faculty is not only a superb teacher but also somebody with experience working in industry. Speaking of industry, OPIT collaborates with major companies in the tech field to ensure its courses deliver the skills that those organizations expect from new candidates.
This confronts frustration on both sides. For companies, an OPIT graduate is one for which they don’t need to bridge a “skill gap” between what they’ve learned and what the company needs. For you, as a student, it means that you’re developing skills that make you a more desirable prospect once you have your degree.
Reason 4 – OPIT Delivers Tier One Education
Despite their popularity, online courses can still carry a stigma of not being “legitimate” in the face of more traditional degrees. Ocleppo is acutely aware of this fact, which is why he’s quick to point out that OPIT always aims to deliver a Tier One education in the computer science field.
“That means putting together the best professors who create superb learning material, all brought together with a teaching methodology that leverages the advancements made in online teaching,” he says.
OPIT’s degrees are all accredited by the European Union to support this approach, ensuring they carry as much weight as any other European degree. It’s accredited by both the European Qualification Framework (EQF) and the Malta Qualification Framework (MQF), with all of its courses having full legal value throughout Europe.
It’s also here where we see OPIT’s approach to practicality come into play via its course structuring.
Take its Bachelor’s degree in computer science as an example.
Yes, that course starts with a focus on theoretical and foundational knowledge. Building a computer and understanding how the device processes instructions is vital information from a programming perspective. But once those foundations are in place, OPIT delivers on its promises of covering the most current topics in the field.
Machine learning, cloud computing, data science, artificial intelligence, and cybersecurity – all valuable to employers – are taught at the undergraduate level. Students benefit from a broader approach to computer science than most institutions are capable of, rather than bogging them down in theory that serves little practical purpose.
Reason 5 – The Learning Experience
Let’s wrap up by honing in on what it’s actually like for students to learn with OPIT.
After all, as Ocleppo points out, one of the main challenges with online education is that students rarely have defined checkpoints to follow. They can start feeling lost in the process, confronted with a metaphorical ocean of information they need to learn, all in service of one big exam at the end.
Alternatively, some students may feel the temptation to not work through the materials thoroughly, focusing instead on passing a final exam. The result is that those students may pass, but they do so without a full grasp of what they’ve learned – a nightmare for employers who already have skill gaps to handle.
OPIT confronts both challenges by focusing on a continuous learning methodology. Assessments – primarily practical – take place throughout the course, serving as much-needed checkpoints for evaluating progress. When combined with the previously mentioned support that OPIT offers, this approach has led to courses that are created from scratch in service of the student’s actual needs.
Choose OPIT for Your Computer Science Education
At OPIT, the focus lies as much on helping students to achieve their dream careers as it does on teaching them. All courses are built collaboratively. With a dedicated faculty combined with major industry players, such as Google and Microsoft, it delivers materials that bridge the skill gap seen in the computer science field today.
There’s also more to come.
Beyond the three degrees OPIT offers, the institution plans to add more. Game development, data science, and cloud computing, to name a few, will receive dedicated degrees in the coming months, accentuating OPIT’s dedication to adapting to the continuous evolution of the computer science industry. Discover OPIT today – your journey into computing starts with the best online education institution available.