Article

Developments in the field of data science have been evident for many years, these years have brought various technological advances that have revealed our present and provided a glimpse of what is to be built in the future. In today's ever-changing world of technology, people must understand the many areas that data science is evolving into. 

Technological progress is based at an exponential rate, with each year bringing even more changes than the previous one. 2023 is likely to be no different, with some major data science paradigms, that every big data specialist should take note of. 

If the 21st century has shown us anything, it's that big data will continue to be... well, bigger. Even the name "Big Data" refers to the ever-evolving computing technology that makes it possible to collect and interpret increasingly valuable data.  

But if big data has been popular for so long, what changes could happen in the next year? Experts have identified some of the following challenges for big data professionals in 2023: 

• Toolkit advancement: Development in AI and machine learning will certainly continue to grow in the coming year, which in turn will have a huge impact on the tools available to data scientists for research and analysis. This is already having a significant impact on most areas of data science, all of which rely on machine learning tools to function.  

• Hollow volumes of data: As a direct consequence of the advances made by A.I. and ML, companies receive a much larger amount of all types of data. Some of these extend to existing datasets, while other advances have led to entirely new forms of data. In any case, adjustments are needed to enable companies to use this data and find safe and convenient ways to store it. As you will see, this will be crucial for companies in various industries. 

• Threats of security: For better or for worse, this is an age-old trend in the data science world, where new forms of cyberattacks are constantly emerging. For those working in cybersecurity, this means strategic planning and rigorous research to identify data breaches and prevent new ones. For anyone working with Big Data, this means that vigilance is always required as some types of cyberattacks, such as phishing and other forms of data fraud, involve deceptive tactics that can be targeted. 

This age, standing on the new horizons of technology, sees several developing trends in the field of data sciences. These are trends that every individual related to technology might have contributed to or researched. Some of these trends have been discussed below: 

Artificial Intelligence 

Recent years have made it clear that artificial intelligence has made astounding leaps that are likely to alter not only the way we trade but the way we live. Outside of data science, this has already entered public awareness: A.I. Programs that enlarge or generate images and text have already become a trend, even among those who are not familiar with information technology. 

Augmented Analytics 

In Big Data, artificial intelligence is used primarily to help collect the ever-growing amounts of data that new devices are capturing and storing. This is consistent with the greater reliance of the world's population on technology to manage their daily lives. To keep up with the massive amounts of information that are constantly coming in, machine learning and artificial intelligence are improving their processing capabilities to speed up this process by significantly preparing and analyzing massive amounts of new data. This is called augmented analytics and it can help businesses tremendously. Augmented Analytics also fits into the Business Intelligence category. The field is expected to grow at breakneck speed in the coming years. 

Deep Learning 

When people think of AI, what they imagine is more akin to deep learning, the branch of artificial intelligence that focuses on teaching computers to behave like humans. These respond to neural network architectures and large data sets, which have so far proven effective. Indeed, by 2023, both data scientists and casual observers alike are likely to take note of computers' increasing ability to mirror human interactions. For businesses, deep learning can be used to anticipate human behaviors, which in turn can impact areas such as marketing and overall business strategies. As this technology becomes more sophisticated, it will continue to transform our approach to customer service and impact how businesses understand their customers' needs. 

Transition to cloud-based operations 

As the amount of data captured by advanced tools increases, there is a growing need for advanced storage solutions. Cloud computing is rapidly being adopted as a solution to this problem, offering vastly improved storage capabilities that can keep up with the changing state of data collection. Most are beginning to see it as the environment where data-driven businesses will be stored in the future, although other storage options are technically available. Cloud computing impacts the big tech field in multiple ways, touching on data science, customer interactions, artificial intelligence, transactional systems, DevOps, and more. If you are interested in or work in any of these areas, keeping up with developments in cloud-based technology is crucial to efficiently manage your database and maintaining it over the long term. 

Data Analytics 

At a very broad level, data analysis is about making decisions or drawing conclusions based on systematically organized data. A more formal definition would include the phases of the data analysis process, which include planning, collecting, cleaning, organizing, and interpreting/communicating. A deeper dive into the process will reveal how each state is an integral part of the data analysis that creates value. 

In the planning phase, the details of the data analysis project would be formulated. This includes any specific questions that need to be answered and the methods that will be used. This phase is essential as it maps the details of the subsequent steps. For example, during the planning phase, analysts decide which data sources to use for the project. Not only the data sources are defined, but also the analysis periods. In other words, from what period is the data taken for use in the analysis? Most importantly, careful planning ensures that the data analysis project delivers value that justifies the costs involved. 

In the collection phase, the data to be used for the analysis is collected. This may include data stored internally within an organization, obtained through primary survey research, or collected from external sources. The data can be quantitative or qualitative. Quantitative data are expressed in numbers like measurements or counts, while qualitative data expresses characteristics or properties. The difference between the two is that quantitative data defines while qualitative data describes (BusinessDictionary.en). 

The cleanup phase verifies the quality and usefulness of the data collected. This stage is important as it impacts the analysis of the data and ultimately the decisions and conclusions drawn from it. Data cleaning checks for relevance, corruption, duplication, and correct format. If possible, the data should be corrected or corrected. However, if this is not possible, it should be excluded from the analysis in order not to jeopardize the validity of the data analysis. After the data cleansing is complete, it is important to run reports to confirm that the required changes have been made correctly and that the data does not contain any conflicting information. 

The previous phases are vital and allow the organization phase to develop more smoothly. In this phase, the core of the data analysis takes place. Cleansed data is organized and manipulated to find answers to questions articulated in the planning phase. There are different types of data analysis tools that can be used to manipulate the data. The tools can be as simple as spreadsheet software like Microsoft Excel or more advanced statistical software packages like SPSS or SAS. R, a programming language and software environment, has become a popular choice for data analysts. 

The last phase is the interpretation/communication phase. The first step in this phase is to develop a 'story' based on the answers found in the organization phase. The “story should help develop actionable steps. The decisions and conclusions made in this phase must be supported by the knowledge discovered in the previous phase. Below are some of the new data analytics trends to watch out for: 

Real-time analysis 

One of the considerable data advancements of 2023 is real-time analytics. Data assortment tools have expanded in speed and scope, which means we have access to an even greater bulk of real-time information that can illuminate our understanding of all categories of processes. Organizations are just beginning to learn how to use these datasets to make important business decisions. If you are a data analyst or work in a related field, you will find it very useful to follow the real-time analytics news and updates coming out throughout this year. 

Mobile analytics 

While it doesn't look like it's going to grow any further, the presence of mobile devices is increasing in communities around the world. This means a lot more mobile data needs to be collected. For certain industries, mobile analytics is at the heart of their strategy, providing the most insightful and useful information to guide marketing and promotional tactics. This includes recording user engagement, and customer satisfaction, monitoring app traffic, and identifying security threats. 

Applications of Data science in Education 

The applications of data science in education are just as diverse. Educators spend their time with students and need more time to analyze data. However, data analysis is becoming all the more important for them. 

Data scientists use their coding, statistical, and content skills to build data models that help educators do their jobs better. They also directly teach educators how to understand data at a deeper level so they can actively apply it in educational settings to improve student outcomes. Similarly, data science is used in education to ensure data security for students and staff and to organize data for system-wide use. This is partly to provide proper data collection by filtering out irrelevant or dangerous incoming data. 

Education is an extraordinarily diverse industry. Within any institution, there are hundreds of different departments with vastly different and overlapping data stores, some necessary and some not. This is different in higher education. 

Education bureaucracies exist in grades K-12 with comparable data needs. They go through this data to find out what is one of the specialties of data science and is fundamental to data science applications in education. Education here, however, is one of many disciplines with many functions. In any case, data science itself is different. Some data scientists specialize in creating database architectures, while others interpret and analyze data to create understandable reports for educators. 

"If you can’t explain it simply, you don’t understand it well enough - Albert Einstein"

Developments in the field of data science have been evident for many years, these years have brought various technological advances that have revealed our present and provided a glimpse of what is to be built in the future. In today's ever-changing world of technology, people must understand the many areas that data science is evolving into. 

Technological progress is based at an exponential rate, with each year bringing even more changes than the previous one. 2023 is likely to be no different, with some major data science paradigms, that every big data specialist should take note of. 

If the 21st century has shown us anything, it's that big data will continue to be... well, bigger. Even the name "Big Data" refers to the ever-evolving computing technology that makes it possible to collect and interpret increasingly valuable data.  

But if big data has been popular for so long, what changes could happen in the next year? Experts have identified some of the following challenges for big data professionals in 2023: 

• Toolkit advancement: Development in AI and machine learning will certainly continue to grow in the coming year, which in turn will have a huge impact on the tools available to data scientists for research and analysis. This is already having a significant impact on most areas of data science, all of which rely on machine learning tools to function.  

• Hollow volumes of data: As a direct consequence of the advances made by A.I. and ML, companies receive a much larger amount of all types of data. Some of these extend to existing datasets, while other advances have led to entirely new forms of data. In any case, adjustments are needed to enable companies to use this data and find safe and convenient ways to store it. As you will see, this will be crucial for companies in various industries. 

• Threats of security: For better or for worse, this is an age-old trend in the data science world, where new forms of cyberattacks are constantly emerging. For those working in cybersecurity, this means strategic planning and rigorous research to identify data breaches and prevent new ones. For anyone working with Big Data, this means that vigilance is always required as some types of cyberattacks, such as phishing and other forms of data fraud, involve deceptive tactics that can be targeted. 

This age, standing on the new horizons of technology, sees several developing trends in the field of data sciences. These are trends that every individual related to technology might have contributed to or researched. Some of these trends have been discussed below: 

Artificial Intelligence 

Recent years have made it clear that artificial intelligence has made astounding leaps that are likely to alter not only the way we trade but the way we live. Outside of data science, this has already entered public awareness: A.I. Programs that enlarge or generate images and text have already become a trend, even among those who are not familiar with information technology. 

Augmented Analytics 

In Big Data, artificial intelligence is used primarily to help collect the ever-growing amounts of data that new devices are capturing and storing. This is consistent with the greater reliance of the world's population on technology to manage their daily lives. To keep up with the massive amounts of information that are constantly coming in, machine learning and artificial intelligence are improving their processing capabilities to speed up this process by significantly preparing and analyzing massive amounts of new data. This is called augmented analytics and it can help businesses tremendously. Augmented Analytics also fits into the Business Intelligence category. The field is expected to grow at breakneck speed in the coming years. 

Deep Learning 

When people think of AI, what they imagine is more akin to deep learning, the branch of artificial intelligence that focuses on teaching computers to behave like humans. These respond to neural network architectures and large data sets, which have so far proven effective. Indeed, by 2023, both data scientists and casual observers alike are likely to take note of computers' increasing ability to mirror human interactions. For businesses, deep learning can be used to anticipate human behaviors, which in turn can impact areas such as marketing and overall business strategies. As this technology becomes more sophisticated, it will continue to transform our approach to customer service and impact how businesses understand their customers' needs. 

Transition to cloud-based operations 

As the amount of data captured by advanced tools increases, there is a growing need for advanced storage solutions. Cloud computing is rapidly being adopted as a solution to this problem, offering vastly improved storage capabilities that can keep up with the changing state of data collection. Most are beginning to see it as the environment where data-driven businesses will be stored in the future, although other storage options are technically available. Cloud computing impacts the big tech field in multiple ways, touching on data science, customer interactions, artificial intelligence, transactional systems, DevOps, and more. If you are interested in or work in any of these areas, keeping up with developments in cloud-based technology is crucial to efficiently manage your database and maintaining it over the long term. 

Data Analytics 

At a very broad level, data analysis is about making decisions or drawing conclusions based on systematically organized data. A more formal definition would include the phases of the data analysis process, which include planning, collecting, cleaning, organizing, and interpreting/communicating. A deeper dive into the process will reveal how each state is an integral part of the data analysis that creates value. 

In the planning phase, the details of the data analysis project would be formulated. This includes any specific questions that need to be answered and the methods that will be used. This phase is essential as it maps the details of the subsequent steps. For example, during the planning phase, analysts decide which data sources to use for the project. Not only the data sources are defined, but also the analysis periods. In other words, from what period is the data taken for use in the analysis? Most importantly, careful planning ensures that the data analysis project delivers value that justifies the costs involved. 

In the collection phase, the data to be used for the analysis is collected. This may include data stored internally within an organization, obtained through primary survey research, or collected from external sources. The data can be quantitative or qualitative. Quantitative data are expressed in numbers like measurements or counts, while qualitative data expresses characteristics or properties. The difference between the two is that quantitative data defines while qualitative data describes (BusinessDictionary.en). 

The cleanup phase verifies the quality and usefulness of the data collected. This stage is important as it impacts the analysis of the data and ultimately the decisions and conclusions drawn from it. Data cleaning checks for relevance, corruption, duplication, and correct format. If possible, the data should be corrected or corrected. However, if this is not possible, it should be excluded from the analysis in order not to jeopardize the validity of the data analysis. After the data cleansing is complete, it is important to run reports to confirm that the required changes have been made correctly and that the data does not contain any conflicting information. 

The previous phases are vital and allow the organization phase to develop more smoothly. In this phase, the core of the data analysis takes place. Cleansed data is organized and manipulated to find answers to questions articulated in the planning phase. There are different types of data analysis tools that can be used to manipulate the data. The tools can be as simple as spreadsheet software like Microsoft Excel or more advanced statistical software packages like SPSS or SAS. R, a programming language and software environment, has become a popular choice for data analysts. 

The last phase is the interpretation/communication phase. The first step in this phase is to develop a 'story' based on the answers found in the organization phase. The “story should help develop actionable steps. The decisions and conclusions made in this phase must be supported by the knowledge discovered in the previous phase. Below are some of the new data analytics trends to watch out for: 

Real-time analysis 

One of the considerable data advancements of 2023 is real-time analytics. Data assortment tools have expanded in speed and scope, which means we have access to an even greater bulk of real-time information that can illuminate our understanding of all categories of processes. Organizations are just beginning to learn how to use these datasets to make important business decisions. If you are a data analyst or work in a related field, you will find it very useful to follow the real-time analytics news and updates coming out throughout this year. 

Mobile analytics 

While it doesn't look like it's going to grow any further, the presence of mobile devices is increasing in communities around the world. This means a lot more mobile data needs to be collected. For certain industries, mobile analytics is at the heart of their strategy, providing the most insightful and useful information to guide marketing and promotional tactics. This includes recording user engagement, and customer satisfaction, monitoring app traffic, and identifying security threats. 

Applications of Data science in Education 

The applications of data science in education are just as diverse. Educators spend their time with students and need more time to analyze data. However, data analysis is becoming all the more important for them. 

Data scientists use their coding, statistical, and content skills to build data models that help educators do their jobs better. They also directly teach educators how to understand data at a deeper level so they can actively apply it in educational settings to improve student outcomes. Similarly, data science is used in education to ensure data security for students and staff and to organize data for system-wide use. This is partly to provide proper data collection by filtering out irrelevant or dangerous incoming data. 

Education is an extraordinarily diverse industry. Within any institution, there are hundreds of different departments with vastly different and overlapping data stores, some necessary and some not. This is different in higher education. 

Education bureaucracies exist in grades K-12 with comparable data needs. They go through this data to find out what is one of the specialties of data science and is fundamental to data science applications in education. Education here, however, is one of many disciplines with many functions. In any case, data science itself is different. Some data scientists specialize in creating database architectures, while others interpret and analyze data to create understandable reports for educators. 

"If you can’t explain it simply, you don’t understand it well enough - Albert Einstein"

The data science practice is best described as a combination of analytical engineering and exploration. The business presents a problem that we would like to solve. Rarely is the business problem directly one of our fundamental data mining tasks. We break the problem down into subtasks that we think we can solve, usually starting with existing tools. For some of these problems, we may not know how well we can do them, so we need to examine the data and run an analysis to see that. If that does not succeed, we may need to try something completely different. In the process, we may discover knowledge that will help us to solve the problem we had set out to solve, or we may discover something unexpected that leads us to other important successes. 

Neither analytical engineering nor exploration should be omitted when considering the application of data science methods to solve a business problem. Omitting the engineering aspect usually makes it much less likely that the results of mining data will solve the business problem. Omitting the understanding of the process as one of exploration and discovery often keeps an organization from putting the right management, incentives, and investments in place for the project to succeed.

The data science practice is best described as a combination of analytical engineering and exploration. The business presents a problem that we would like to solve. Rarely is the business problem directly one of our fundamental data mining tasks. We break the problem down into subtasks that we think we can solve, usually starting with existing tools. For some of these problems, we may not know how well we can do them, so we need to examine the data and run an analysis to see that. If that does not succeed, we may need to try something completely different. In the process, we may discover knowledge that will help us to solve the problem we had set out to solve, or we may discover something unexpected that leads us to other important successes. 

Neither analytical engineering nor exploration should be omitted when considering the application of data science methods to solve a business problem. Omitting the engineering aspect usually makes it much less likely that the results of mining data will solve the business problem. Omitting the understanding of the process as one of exploration and discovery often keeps an organization from putting the right management, incentives, and investments in place for the project to succeed.

About the Author

Dr. Yojana Arora, is an Associate Professor with 9+ years of experience in the field of Higher Education & Research. A technical trainer and learning specialist with strong educational background and skilled in analytical, problem solving, teaching, research, presentation and leadership skills. Ability to learn new technologies, flexible, creative and good team player. Research Interest includes Data Analytics & Network Security.