Importance and Inclusion of Data Science and Analytics in the Indian Education Space and the NEP 2021.

The new education system aspires to implement transformational improvements in the classroom and higher education systems. Replacing India's 34-year-old education system is another significant step toward enhancing India's position as a global power. The main goal of adopting and executing the NEP is to improve education quality for all Indians while also increasing India's position as a global superpower.

The NEP was prepared by a group led by Kasturirangan, the former chief of the Indian Space Research Organization (ISRO), and delivered to Ramesh Pokhriyal, the Union Minister for Human Resources Development when he joined the office in 2019.

The New Education Policy Highlights:

●??????A single regulator will oversee all higher education institutions, with the exception of medical and law schools.

●??????MPhil courses will no longer be offered.

●??????Exams will be more application and knowledge-based in the future.

●??????The same rules will apply to both public and private higher education institutions.

●??????Up to class 5, instruction will be in local/home languages in order to promote and emphasize the regional language/mother tongue.

●??????All entrance tests for colleges and universities will be held at the same time.

●??????The focus of the school curriculum should be on essential topics.

●??????From the sixth grade onwards, vocational education will be provided.

●??????The 10+2 study culture will be phased out, and a new structure of 5+3+3+4 will be implemented, with age groups of 3-8, 8-11, 11-14, and 14-18 years being considered.

The four key issues identified in the Indian Education System are:

I.) High student dropout rates at various levels of schooling;

ii) The current educational system's inability to identify learning disabilities in children;

iii) One-size-fits-all pedagogy and ineffective traditional methods of evaluation; and

iv) A lack of focus on teacher performance and teaching methods. Classification, grouping, and regression are some of the machine learning strategies that were employed to solve the difficulties.

Research suggests that data science has a lot of potential for discovering innovative solutions to a variety of difficulties in the school and higher education sectors.

High student dropout rates at various levels of schooling:

A child who drops out of school is a significant waste of our country's resources. Finding a way to reduce the dropout rate will benefit society and the country as a whole. With the help of data science, an attempt has been made to estimate the number of students who will drop out in a certain academic year, as well as the number of students who will drop out in a given school/region, which will aid the government in dealing with the causes of dropout.

For the same reason, data on the numerous causes that contribute to a student's/dropping student's out must be gathered. Multiple elements, such as the family's financial situation, educational specifics, family health background, gender of the kid, student performance, and so on, are required to forecast dropout rates.

To forecast if a student would drop out this academic year, a classification system is applied. Decision trees, Nave Bayes, and KNN are amongst the other models that can be used. A regression method also forecasts the number of dropouts in a school or region for the future year. Linear regression and polynomial regression are two models that can be used.

The current educational system's inability to identify learning disabilities in children:

Learning disabilities are one of the most misunderstood, untreated, and unpredictably occurring issues in children. This is due to four factors: a lack of awareness of such issues among elders, a child's inability to properly express his situation, a lack of appropriate mechanism to observe or measure such disabilities directly in children, and the issue of over-identification in the current method faculties observation and various checklists.

Individuals with learning difficulties need early detection and assistance if they are to succeed in the long run. Families and their children under the age of three who have or are at risk of having a disability are evaluated and counseled as part of the early identification process.

Dyscalculia, Dysgraphia, Dyslexia, Non-Verbal Learning Disorders, Oral / Written Language Disorder, and Specific Reading Comprehension Deficit, ADHD, and Dyspraxia are some of the learning disabilities. Students can be divided into distinct disability classes using a multiclass classification technique. Dyslexia, dysgraphia, dyscalculia, and others are examples of classes.

Other difficulties include the considerable processing power necessary to analyze the data obtained and the analysis of data incorporating images and videos using opaque techniques and models. Lack of openness is a problem since school officials must respond to parents if their children are diagnosed with learning difficulties.

One-size-fits-all pedagogy and ineffective traditional methods of evaluation:

The lack of methods to identify curious and innovative minds, the least weightage for extra-curricular activities and soft skills, and the inability to use teaching methods based on a student's aptitude and interest or student's strength and weakness are some of the flaws in the current education system's pedagogy and evaluation. All of this has placed an unwarranted pressure on students' shoulders, causing them to focus solely on getting good grades by any means possible, ignoring the primary goal of education.

The goal of education should be to improve a student's skills, aptitude, knowledge, and perception all within a single framework. The New Education Policy 2020 calls for a shift away from grading based on grades and toward "continuous and comprehensive evaluation." Critical thinking and more holistic, inquiry-based, discovery-based, discussion-based, and analysis-based learning are also receiving a lot of attention. The policy also emphasizes character development and the development of comprehensive and well-rounded persons. With this in mind, the significance of data science in improving student evaluation and, as a result, building student-tailored pedagogy is explored in this study.

Rather than evaluating a subject as a whole in a written exam, each subject is evaluated on a number of characteristics that are checked on a regular basis. Based on a scientifically designed evaluation rubric, the evaluation is done on a scale of 1 to 5. Written assessments, practical sessions, projects, group discussions, and class participation are all used to collect data. The same approach is used to evaluate the kids' behavior as indicated previously. Teachers rate the parameters on a scale of 1 to 10 using an evaluation rubric. Data is collected from a variety of sources, including group activities, sports, debates, and so on. Both academic and behavioral evaluations use a clustering technique.

?A lack of focus on teacher performance and teaching methods. Classification, grouping, and regression are some of the machine learning strategies that were employed to solve the difficulties:

Teachers are severely overworked now, as they have never been before. They are ineffective because their traditional functions of education, socialization, assessment, and classroom administration are insufficient. Furthermore, today's teachers encounter issues that traditional school teachers have never experienced. They are in the midst of a shift in the educational landscape, which is littered with numerous and complex scenarios.

The main issue is that teachers are evaluated exclusively on their problem-solving abilities. Furthermore, during teacher evaluations, experience is given more weight without regard for whether or not there has been any self-improvement, and no assessment of instructors' character attributes is made.

Teacher evaluations are frequently used to assess teacher competence as well as promote professional development and progress. A teacher assessment system will also provide teachers with important input on classroom needs, allowing them to acquire new teaching strategies and make appropriate modifications in their classrooms. As a result, the value of teacher assessment in effecting positive change is now widely recognized and accepted. The evaluation's major goal is to enhance the educational situation throughout time.

The performance of a teacher is divided into several classes using a multiclass classification algorithm. Excellent, good, middling, poor, and very poor are some examples of grades.

Introduction to Data Analytics

With the rise of Big Data, two new buzzwords in the industry have emerged: Data Science and Data Analytics. The entire world now contributes to massive data growth in colossal volumes, thus the term "Big Data." The World Economic Forum states that by the end of 2020, the daily global data generation will reach 44 zettabytes. By 2025, this figure will have risen to 463 exabytes!

Big Data encompasses everything we do online, including messages, emails, tweets, user queries (on search engines), social network activity, and data created by IoT and connected devices. Traditional data processing and analysis tools can't handle the massive amounts of data generated every day by the digital world. This is where Data Science and Data Analytics come in. We commonly use Data Science and Data Analytics interchangeably since Big Data, Data Science, and Data Analytics are still developing technologies. The fact that both Data Scientists and Data Analysts work with Big Data contributes to the misconception.

Data Analytics and Data Science are two sides of the same coin.

Data Science and Data Analytics both deal with Big Data, but in different ways. Data Science is a broad term that incorporates both data analytics and data science. Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence are all included in Data Science.

Data mining, data inference, predictive modeling, and machine learning algorithm development are all used to discover patterns from large datasets and turn them into meaningful business strategies. Data analytics, on the other hand, is mostly concerned with Statistics, Mathematics, and Statistical Analysis.

Data Analytics is aimed to reveal the particular of extracted insights, whereas Data Science focuses on uncovering significant correlations between vast datasets. To put it another way, Data Analytics is a subset of Data Science that focuses on more detailed solutions to the issues that Data Science raises.

Data Science aims to find fresh and interesting issues that might help businesses innovate. Data analysis, on the other hand, tries to uncover answers to these questions and decide how they might be implemented within a company to encourage data-driven innovation.

Data Science vs. Data Analytics: What are the Differences Between the Jobs of a Data Scientist and a Data Analyst?

Data Analysts and Data Scientists use data in different ways. Data Scientists clean, process, and evaluate data using a combination of mathematical, statistical, and machine learning approaches in order to extract insights. They use prototypes, machine learning techniques, predictive models, and specialized analysis to create advanced data modeling methods.

Data analysts collect enormous amounts of data, arrange it, and analyze it to find important patterns, while data analysts study data sets to detect trends and draw conclusions. After the analysis is completed, they aim to convey their findings using data visualization techniques such as charts and graphs. As a result, Data Analysts translate complex insights into business-savvy language that can be understood by both technical and non-technical personnel of a company.

Both positions collect, clean, and analyze data to varying degrees in order to gain actionable insights for data-driven decision-making. As a result, Data Scientists' and Data Analysts' tasks frequently overlap.

The Evolution of Data Science (AI, ML, VR, AR, Python Programming)

Choosing which programming language to learn can be overwhelming and tough with 256 languages available today. Some languages are better suited to game development, while others are better suited to software engineering and data science.

A computer's most intelligible programming language is called a low-level programming language. Assembly language and machine language are two examples of this. Assembly language is used to manipulate direct hardware, access specialised processor instructions, and solve performance problems. A machine language is made up of binaries that the computer can read and execute directly. To convert assembly languages into machine code, you'll need assembler software. High-level languages are slower and use more memory than low-level languages.

Unlike low-level programming languages, high-level programming languages provide a significant abstraction from computer details. This allows the programmer to write code that isn't affected by the type of computer being used. These languages are far closer to human language than a low-level programming language, and the interpreter or compiler converts them to machine language behind the scenes. Most of us are more familiar with these. Python, Java, Ruby, and many others are just a few examples. These languages are usually portable, so the programmer doesn't have to worry as much about the program's technique, allowing them to concentrate on the task at hand. Today, there are a lot of programmers.

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Conclusion

In this paper, we use data science to try to solve four of the most pressing issues we've observed in the education industry. For example, if numerous causes contributing to school dropout can be identified and data collected using an acceptable mechanism, children with a high likelihood of dropping out in a given academic year can be forecasted. Similarly, if the right machine learning technique is employed, a student's learning difficulty can be appropriately detected. Even if employing data science to evaluate instructors and pupils has some limits, greater research in the subject and the use of more advanced and scientific data collection methods could provide more insight into overcoming those challenges. We have sought to demonstrate the potential of data science in addressing some of the difficulties in the education sector in this work, and it should be viewed as a forerunner to further in-depth studies in the field of education that will be conducted in the near future.