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Data Analytics In Education: 6 Strategies For Success

Data analytics in education: 6 strategies for success

Do you want to learn about data analytics in education? This article reviews this topic and discusses how to implement the innovation in real-life scenarios.

I. Introduction

A. Data Analytics: A rising trend

A proper review of education-centric data analytics requires a look at the general context. We live in a post-industrial society, where information and services are more important than manufacturing. How did it appear? In our opinion, post-industrial society came out of the computer revolution. It allowed humanity to automate many processes between the 1970s and the 2000s. In this article, we review a consequence of this computer revolution, the rise of advanced data analytics. Computers enable humans to collect tremendous amounts of information about their activities. Now, we have Big Data that can transform our approach to education, governance, finance, and healthcare.

B. Data Analytics and Education: An Overview

In education, the use of Big Data allows you to rethink methodology and adapt the learning experience to boost its overall efficiency. What does this mean for education? Firstly, computer skills should be a central part of educational curricula. A person without computer knowledge can’t do anything in the modern world. Secondly, we now know how to analyze the smallest areas in education. Data analytics in education helps us refine teaching strategies and reinvent learning. Today, humans can approach learning as a science and maximize their potential. In this article, you’ll learn how to capitalize on the benefits of analytics.

II. The Need for Data Analytics in Education

Data analytics is transforming education. It’s time to review the reasons why it’s crucial in detail:

A. The Digital Shift in Education

We live in an increasingly computerized world. Let’s look at the most portable computing device: the smartphone. Zippia, a service that assists in career building, provides interesting statistics on this technology

  • 6.92 million people own smartphones as of 2023. This information means 86% of the global population can access computing devices.
  • An average American spends 5 hours and 24 minutes on their smartphone daily. They check their phone every ten minutes, or 96 times a day. Smartphones are an integral part of our lifestyle. Today, one-fifth of our lives is digital. Obviously, this has massive implications for all areas of human existence.

For education, the smartphone revolution is the largest transformation since the rise of printing. Why? Here are the key reasons:

1. Smartphones and computers are now indispensable

Firstly, it’s impossible to live in our society without a smartphone or at least a feature phone. Finance and communication rely on mobile technologies. The Guardian reports that using banking services without smartphones is becoming increasingly difficult. Many banks focus their security on smartphone authentication. One of its readers complains that they can’t buy or sell anything because transactions require smartphone codes. What does the Guardian recommend? They tell their technophobic reader to at least consider a cheap handset. Soon, students without smartphone and computer literacy won’t be able to become functioning adults. In this light, we have to fully transform all our curricula to include digital tools.

2. Smartphones and computers enhance learning

Secondly, smartphones and computers are critical as learning devices. They create new approaches to learning that were impossible in the past. Here are some interesting uses:

  • Video-based learning: smartphones and computers enable access to video-on-demand services. There, a student can find lectures and short videos on almost every topic imaginable.
  • Learning Management Systems and distance learning: a teacher can put an entire course online using an LMS. Delivering lectures and reading materials without any classrooms is easy. In this way, schools and higher education facilities can target people worldwide. This software saved education during the COVID-19 pandemic. Today, it’s making education accessible to everyone. Platforms such as edX and Coursera offer thousands of courses. Some of them, like CS50, make Harvard-grade education accessible everywhere.
  • Accessibility tools: computers and smartphones are great for people with impairments. For instance, visually impaired people with PCs and smartphones may be more proactive in their learning. They can use audio assistants to browse the web or read books. Screen readers help them consume more information without outside help. 
  • Virtual reality for teaching: Google Cardboard VR features products between $9 and $40. Most students can now access this technology. Why is this important? VR helps explain many spatial concepts. For example, South Korean universities use VR to teach anatomy. Game is Hard web journal reports that the results are outstanding.

Without smartphones and computers, education will regress. The disappearance of MATLAB will decrease the quality of economic analysis. Many doctors will have difficulty memorizing body parts without Anki, according to KenHub, a service for learning anatomy. Education can’t return to the past. The smartphone revolution is a paradigm shift.

B. Using data for informed decision-making in educational institutions

Educators can reconsider many dogmas thanks to smartphones and their data collection tools. In the past, education strategy management relied on teacher experience or isolated experiments. We used to extrapolate specific cases to the general population. As a result, many sweeping generalizations occurred. 

With smartphones collecting vital data, modern teachers have tools that enable two processes:

  • one can get advanced statistics on a particular region, school, or even student;
  • one can have general insights into student behavior across an entire country.

What does this mean for educators? They can transform general teaching strategies and adapt learning to personal student needs. Some outcomes of data analytics are already visible. Long-term student observations have disproved the learning style theory. According to Prof. Phil Newton from Swansea University, humans don’t have any particular learning styles. Efficient learning must combine visual, audial, and kinesthetic elements to guarantee success. 

Major advances also involve personal learning programs. Data analytics and AI allow for the creation of automated student-centric learning curricula, as Aditi Bhutoria from Indian Institute of Management Calcutta, reports. Modern technologies can adapt learning to the needs of students independently. Educator input is still necessary, but it only involves small adjustments. Data analytics tools enable personal programs for everyone (and not only special needs children). In short, data analytics for schools assists with informed decision-making.

III. Benefits of Data Analytics in Education

Benefits of data analytics in education

Why is data analytics important in education? Here are the four key benefits that explain its importance:

A. Personalized learning experiences

As we’ve already mentioned, Big Data is behind accessible personalized learning programs. Let’s review this benefit in depth. In general, creating a personalized program is quite difficult because all students have different IQs and psychological profiles. A successful individual learning program must consider speed and motivation. It’s possible to do general personalization in a traditional class. For example, gifted students can receive harder tasks. However, small elements, such as interests or temperament, require deeper observations. 

Problems of the Traditional System

One requires many hours to create a truly personalized learning program. As a result, our learning system has evolved to meet the average student’s needs. Only the most extreme cases, such as dyslexia, receive special attention from experts. This approach works: the majority of students get high-quality education. Still, the system has many outliers. Usually, these are children who have non-conventional interests. They may have no learning disabilities or psychological pathologies. What our system doesn’t target are their unique talents. Personalized learning solves this problem once and for all.

Automatic Personalization

We can collect a lot of data about students. Smartphones and computers make activity monitoring simple. More importantly, tools for analyzing this data without human input exist. What are those? Artificial intelligence and machine learning. Using them, learning software can adjust learning speed automatically. More importantly, adjusting to a psychological profile is easy, too. A choleric individual with ADHD will get small learning sessions. Phlegmatic people will receive access to a slower but more in-depth program. 

A good example of personalized learning is Duolingo. It uses various algorithms to create custom lessons for students. Our experience with the app shows that it has tracking for grammar and vocabulary difficulties. The app creates personalized lessons to iron out various weaknesses. For instance, if you can’t understand Passato Prossimo in the Italian language, Duolingo will give you more exercises for it. 

Data-driven innovations

In the future, we’ll likely see this customization in all schools. What are some interesting innovations? Here are some ideas: 

  • AI-generated tasks that target the favorite topics of the students. References to pop culture can make math and physics problems appealing.
  • Book suggestions based on the learners’ preferences. Students who like science will get some science fiction recommendations. In turn, those who like theater can read more about its history.
  • Personalized flashcards for memorizing hard concepts. We’ve all faced unbeatable concepts. For the author of this article, metrical systems in poetry are a major issue. With personalized learning, Big Data will specifically target our weaknesses.

B. Improved student outcomes

Obviously, better data analysis improves outcomes for the average student. Still, greater transformations are about to come into being. Big Data allows us to test our entire philosophy of learning. With information from thousands of schools worldwide, experts will be able to change how we learn. 

For example, using Big Data, MIT researchers have found that language learning is easy until young adulthood. Previous data showed less optimistic results. 

Another example is the modern education system in certain Asian countries. To enhance the effectiveness of their education systems, they analyzed international experiences. Today, Chinese students win the majority of math competitions. Data analytics enables all countries to make lasting changes based on massive information sets.

C. Efficient administrative decisions

The benefits of data analytics are undeniable on a district and school level. With Big Data, administrators can track school ratings in real time. Data analytics frameworks allow one to see school, class, and student daily progress. In this way, the administrators detect various problems with individual teaching methods. Such an approach enables efficient administrative decisions. Tracking cause-and-effect connections between different practices becomes easier. This data helps with evidence-based school consolidation and other high-value strategies. For example, Shanghai administrators use a commissioned education approach. In it, strong schools are assigned to weaker ones and then control their progress. This allows the best practices to be spread across the entire education system.

D. Enhanced engagement and curriculum development

Engagement is the key element in efficient learning. If teachers don’t find a way to attract students’ attention, they’ll likely fail in adulthood regardless of their potential. We know from longitudinal studies that IQ alone isn’t a guarantee of success. It’s a great predictor of good life outcomes, but it doesn’t predict everything. What matters beyond intelligence is student motivation. 

Personal Computer Metaphor: IQ in Context

A personal computer metaphor works well here. On the one hand, one can use a high-performance PC to play online games all day. On the other hand, writing a ground-breaking novel is possible on a 40-year-old ZX Spectrum. In fact, G. R. Martin, the author of the “A Song of Ice and Fire” book series, still uses a DOS computer and WordStar 4.0 editor (released in 1987). IQ works in the same way. A person with an average IQ who puts it to good use will achieve more than a talented but lazy individual.

Data Analytics and Motivation

In this situation, data analytics in education is an essential process. Why? The reason is simple: it permits us to understand what students truly want. A student who likes cars will find texts about animals boring. In turn, this can disrupt their willingness to acquire reading skills at all. Bad reading skills will later lead to problems in all subjects. Projects like Choice Texts by eSpark offer a solution to this issue. In this case, Choice Texts uses AI to generate texts based on student preferences. 

Tools that analyze larger trends in learning interests are even more impactful. Armed with this data, teachers can recommend curricula that target the unique needs of all learners. Obviously, this aspect doesn’t touch on personalization alone. Data analytics tools enable us to track whole groups of students. Integrating memes and popular topics into curricula becomes easier with such information. You can now personalize education not only for one student but for entire generations.

IV. 6 Strategies for Success in Implementing Data Analytics in Education

6 strategies for success in implementing data analytics in education

Analyzing data in education is among the best financial decisions for schools. Let’s review six key strategies for implementing data analytics in education. This process isn’t simple. Still, the benefits are too large to ignore it:

1. Understanding Data Sources

А. Understand your data sources

The first step is understanding where the data in your institution comes from. There are many sources from which one can get some relevant information. In our opinion, these are the key elements you should consider: 

  • Attendance records
  • Standardized test scores
  • Teacher evaluations
  • Student feedback
  • Learning Management System reports
  • Curriculum maps
  • Student transcripts
  • Demographic data
  • School climate surveys
  • Parent surveys
  • Graduation rates
  • Student retention rates
  • Smartphone app data
  • Psychological and medical reports

Generally, we can split all this information into four major groups. Firstly, there’s individual student data, which includes attendance and scores. This data informs us about student intelligence and psychology. For instance, we know that SAT scores tend to have a moderate correlation with IQ. These scores serve as a relatively good analytical tool, helping us make judgments on student progress.

Secondly, a big aspect is feedback of all types. Students, teachers, and parents offer interesting qualitative data. You can learn if some subjects are boring or if certain teachers are too strict with the students. Thirdly, there’s collective information on demographics, graduation, and retention. It’s perfect for diagnosing the success of particular schools and even whole countries. Lastly, we’ve also included technological data. Apps of all kinds provide a lot of behavioral knowledge. It’s possible to understand a student’s psychological profile and interests through an LMS or even a reading app. 

B. Ensure data quality and relevance

It’s not enough to collect information about the students. This data should also be as accurate as possible. What are some ways to test data accuracy? You should consider the following:

  • Do unexpected checks of student performance. A random test permits administrators to see if teachers overestimate student success. We live in an age of grade inflation. Education as a product model pushes colleges to award higher scores because they need to attract more students. A’s (and not B’s) are now the most widespread score in the US. The Grade Inflation site presents a detailed statistical analysis of this phenomenon. 
  • Unify all data collection standards. If all teachers collect data differently, analyzing it will be impossible. You should have one standard for collecting this information.
  • Establish a clear pathway for sending data. The information must be up-to-date to be relevant. This means you must have a well-organized system for fast data collection. 
  • Compare qualitative and quantitative feedback. Qualitative feedback can reveal many problems in an otherwise perfect facility. For example, great grades may hide a culture of overwork. We can’t call a school efficient if it pushes students towards anxiety disorders and depression. A truly efficient school has great grades and great feedback from all stakeholders.
  • Use modern technology to collect behavioral data. Many interesting insights about various students hide in their small behaviors. Learning Management Systems and learning apps can reveal them.

2. Using the Right Tools and Platforms

Knowing what data you have and then analyzing it right away isn’t enough. You should also focus on efficiency. In our opinion, the best way to achieve it is to select the appropriate tools:

A. The importance of choosing user-friendly and comprehensive analytics tools

You should concentrate on two values when choosing analytics tools. Firstly, they must be user-friendly. Why is this important? Proper data analytics involves a myriad of stakeholders. Here are some of them: teachers, administrators, government officials, nonprofit workers, parents, and students. An unfriendly app can exclude those people. For example, statistics often hide inefficient learning practices from parents. Another problem is that some people may simply lack the skills to operate overly complex software. It’s better to sacrifice complexity than disrupt user-friendliness.

Secondly, it’s crucial to find a comprehensive platform. It must offer as many advanced tools as possible to ease trend tracking. Isn’t that a contradiction, though? After all, user-friendliness is about simplicity. In our opinion, there’s no major contradiction. Our concept of high-quality analytics tools follows Bushnell’s Law. This principle arose in the video game sector. Its creator is Nolan Bushnell, the founder of Atari, the largest gaming company of the 1970s and early 1980s. What does it say? “Games should be easy to learn but hard to master.” In our opinion, data analytics tools should follow a similar pattern. They must be easy to learn for everyone and advanced enough for a power user. In such a system, common users will be able to review information without major effort. In turn, experts will have in-depth tools for analyzing reality.

B. Edstruments: KIPP Massachusetts Experience

Edstruments is a finance and resource management tool for the American education system. It assists with analyzing data about key financial transactions. One of the cases on its website showcases how potent data analytics instruments are. KIPP Massachusetts is an organization uniting multiple public charter schools in the US. Before cooperating with Edstruments, it had major problems with financial tracking. Its entire resource system relied on 20 Excel spreadsheets. In this light, adding data to the whole framework was extremely time-consuming. Only select individuals could process the incoming information. More importantly, getting any information was difficult, too. Teachers and administrators had to file separate requests for data access. They often required several days for processing.

Edstruments eliminated the majority of the concerns KIPP Massachusetts had. 

It saves 45 hours a month by reducing the number of spreadsheets to three. That’s an 85% reduction in the scale of paperwork. More importantly, this framework has advanced tools for managing permissions. Now, providing access to key data is easy. Many teachers already have the necessary permissions. In this way, the app simplifies data analytics for all stakeholders. It helps collect a lot of information and provides vital data to everyone. 

3. Promoting a Data-Driven Culture

Having the right tools and using them isn’t enough. You should also learn to use them efficiently. To achieve this, you have to target all stakeholders:

A. Encouraging educators and staff to make data-informed decisions

Good data analytics tools give all stakeholders access to information. Considering this factor, it makes sense to focus on fostering a data-driven culture in your organization. How exactly can you do this? Thorwald Herbert, the head of Semarchy, a data analytics company, proposes four vital steps: 

1) Educating your team about data analytics. It’s impossible to learn data analytics without understanding what it is and training to use the relevant tools.

2) Democratizing decision-making. One of the key reasons to use data analytics is to expand access to information. Enable team opinions supported by analytics and data to make important decisions.

3) Creating a leadership team that will push the transformation. Data analytics concepts are typically complex. Some resistance can occur in this situation due to a lack of knowledge. A vanguard team with a good understanding of data analytics is crucial. It’ll approach the problems strategically and change the opinions of the skeptics.

4) Emphasizing the benefits of those tools. In our opinion, the process of integrating data analytics is painful. Early transformation requires a lot of work. However, your team can boost productivity and confidently respond to challenges. You need to show long-term positive results to convince the stakeholders.

We fully agree with this approach. The only thing to add here is that you need to also emphasize data collection. High-quality information doesn’t appear out of nowhere. It requires a thorough approach to collection. You can’t achieve it without proper theory and practice.

B. Regular training and workshops

The worst approach to teaching is to dump all information into one small session. Some people won’t understand your lessons at all. Others will have major misconceptions about data analytics. What does this mean in a practical sense? Training should become regular. “Repetition is the mother of learning,” an ancient saying claims. Modern science confirms these assumptions. It emphasizes strategies such as interleaving and spaced repetition to ease learning. You must spread out data analytics training over a long period of time. Start with basic concepts and then gradually process harder topics. One needs regular training and workshops to answer stakeholder questions and help them acquire key skills.

4. Prioritizing data security and privacy

The key problem with all data-driven technologies is security. They expose information to the Internet to ease analysis and knowledge dissemination. As a result, stealing the relevant information is easier than in the case of paper records. You need to focus on two aspects while considering security. Here they’re:

A. Ensuring student and staff data is protected

The first thing you need for data analytics in higher education and schools is a good security process. In this respect, you should consider the following steps:

  • Use robust user authentication methods;
  • Apply encryption to safeguard data in transit and at rest;
  • Implement strict access controls to limit user permissions;
  • Keep software and systems up-to-date to patch vulnerabilities;
  • Continuously monitor for suspicious or unauthorized activities;
  • Conduct routine security audits to identify and address weaknesses;
  • Educate users about security best practices;
  • Establish a comprehensive incident response plan;
  • Secure physical access to servers and data storage;
  • Use intrusion detection and prevention systems;
  • Maintain backups of critical data;
  • Regularly review and update security policies and procedures.

B. Complying with regulations like FERPA

We recommend addressing legal documents if you don’t find this list sufficient. Many countries have advanced regulations for data security that detail high-value practices. For school data analysis, you should consider both specialized and general laws. An example of an education-centric information law is FERPA. FERPA stands for the Family Educational Rights and Privacy Act. This regulation allows parents to review the key school records of their children. As for the general laws, GDPR (General Data Protection Regulation) is a central regulation. Working in Europe is impossible without adhering to this policy. This law regulates key obligatory practices, such as encryption.

5. Collaborating and Sharing Insights

The inability to collect information is a path to failure. And how do companies fail in this regard? The most widespread problem is authoritarian control. A good example of this issue is a company called Blockbuster. In the early 2000s, it was the largest movie rental service in the US. Its inability to realize why its late fees were bad killed the business. Many customers hated them, but the management didn’t understand the scope of the issue. At the same time, nobody in the lower parts of the hierarchy was able to contradict the managers. When Netflix appeared, it destroyed Blockbuster through its subscription model without late fees. You can read more about this case in this great Forbes article. Its author, Greg Satel, a media manager and business consultant, concludes that excessive hierarchy killed this company, too.

You need collaboration and insight sharing to avoid these concerns. How do you achieve them in school settings? Firstly, you need to convince everyone that data sharing is a good practice. Only more or less full staff support can help. Secondly, to share insights, you have to actually allow their sharing. Considering these factors, democracy is essential. You need to allow rank-and-file teachers to question the decisions of the management. Why is this not damaging to subordination? Because this criticism combines with data-driven arguments. Democracy without data can lead to political infighting because it selects people based on their interests. Democracy with data selects people based on their competence. It’s hard to hide bias when everyone has open access to data.

6. Promoting continuous assessment and iterative improvement

The final step you should consider is continuous assessment and iteration. No data analytics framework is perfect. You’ll always have flaws in your process. In this light, it’s crucial to do three things:

  • Confirm your data insights through practice;
  • Collect the stakeholder feedback;
  • Use statistical methods such as regression analysis to find flaws in your methodology.

Based on this data, you should continuously remake your system. Start with big problems, such as data collection, and then iron out small ones. Practice and revise all the time to make your system as perfect as possible.

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V. Challenges in Implementing Data Analytics in Education

Challenges in implementing data analytics in education

Here are some difficulties that can disrupt your ability to implement data analytics:

A. Resistance to change from staff and students

Data analytics is a complex method that requires a lot of effort. Many teachers will have to completely remake their assessment processes. Individual learning programs for all students will also require major observation efforts. In this situation, rigid individuals will likely resist any change. Why does this happen? Two reasons exist for this. Firstly, some people enter education solely to earn money. Any changes in established processes complicate everyday work for them. Secondly, age plays a role, too. Learning becomes increasingly difficult with age, according to modern psychologists. As a result, older adults can feel threatened by new systems. Fearing that they won’t be able to adapt to them, they’ll sabotage any change. You need to present data analytics in a user-friendly way to avoid those problems.

David Labaree from the Stanford University Graduate School of Education also notes that students resist such changes. He notes that schools are massive sites of resistance and struggle today. Data collection can be invasive for students. For instance, an analysis of student interests may require portfolios. Few people like additional work, so resistance from them is likely. The only way to avoid this issue is to offer something interesting in exchange for students. Enhance your classes with gamification or culturally relevant content. 

B. Data overload: making sense of vast amounts of data

The key concern for all data analytics is organization. Collecting data is only the first step. You also have to organize information and make sense of it. The difficulty here is that the amount of data can be overwhelming. Many data types exist: you can have enrollment data and engagement information from classes. Getting lost in it is simple. As Ludwig von Mises, a libertarian economist, claims, fully understanding human activity is impossible. We can only make generalizations based on our data.

What are the key steps to overcome this problem? Here they’re:

1) Understand what data is the most decisive. Don’t analyze everything.

2) Use technological tools such as AI to analyze Big Data. Automate as much as you can.

3) Delegate data analysis to multiple people at different points in a hierarchy. Spread data analysis among as many people as possible.

C. Technical challenges

Implementing data analytics is generally difficult. It’s a time-consuming process that demands major expertise. Here are the core problems you can face:

  • Data cleansing: dealing with noisy, inconsistent, or incomplete data;
  • Real-time processing: managing data streams for immediate insights;
  • Database Optimization: ensuring fast queries and data retrieval;
  • Data Warehouse Design: structuring data storage for efficient analytics;
  • API Integration: connecting with various education software and platforms;
  • Data Privacy Compliance: adhering to regulations like FERPA and GDPR.

We believe the best way to avoid these issues is to seek professional help. Keenethics has more than eight years of experience developing projects in edtech and fintech. You can contact us to get advanced assistance services.

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D. Ethical concerns: potential misuse of student data.

The final challenge includes ethical considerations. In the wrong hands, Big Data can be weaponized. 

Here are some best practices to prevent this:

  1. Anonymize data;
  2. Have multiple analytical centers;
  3. Collect only essential information;
  4. Have strong access controls;
  5. Have an independent anti-discrimination and anti-bullying committee;
  6. Use automatic tools when possible.

VI. The Future of Data Analytics in Education

Data analytics is already a major trend in modern education. No organization can stay on top without being data-driven. Here are two trends that greatly influence modern education:

A. Predictive analytics and AI’s role

The first trend is the rise of predictive analytics and AI. Predictive analytics uses past performance to judge future student success. AI is the use of self-learning algorithms for analyzing data. They often work well in combination. Self-learning algorithms can find trends in data. Humans or some algorithms can then make high-quality judgments about student progress. 

The key aspect here is Big Data analysis because it helps find small trends in student behavior that change prediction trajectories. For instance, problems with reading skills can have a minor impact on math first. Later, they will become an unbeatable obstacle for a learner. Math teachers can easily overlook this element. Ultimately, predictive analytics is now essential for all learning tools. Learning management systems like Edly actively work on adding these innovations.

B. The rise of ed-tech

Another big trend is the rise of edtech. It involves apps that simplify or transform learning. For example, video conferencing apps allow distance learning. Learning management systems automate testing and promote multimodal studying. These systems advance computer-based learning and, as a result, help with data collection. You can keep an eye on what students do when they use a learning management system (LMS) or a language learning app. One of the largest players in this market, Google, is entirely data-driven. It provides analytical tools as part of its Education Workspace package.

VII. Conclusion

Why is data analysis important in education? It’s the best tool for improving education. Data can reveal your key weaknesses and strengths. Knowing them, you will be able to reform vital processes. Data analysis shows that distinct learning styles don’t exist and that rote memorization needs major modifications. In this light, it’s possible to remake our general education strategies. 

We think that all schools and universities should adopt a data-driven approach. Many high-quality practices hide behind Big Data. All you need to do is uncover them through data analysis. Keenethics has been developing edtech products for multiple years. You can ask us to create a custom data analytics solution for you. This approach is the best because this app will target your school’s unique needs.

Data analytics can revolutionize your approach to education.

We can assess and develop your data analytics project. Don’t hesitate to contact us!

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