data science homework help

data science homework help

The Importance of Data Science in the Modern World

1. Introduction to Data Science

Data Science is poised with handling huge data which ranges from genome, biodiversity, medical, finance, astronomical, remote sensing, and multimedia data. Data science gives decisive contributions in the fields of Medicine, Biology, Health Informatics, Biotechnology, Science, Humanities, Telecommunications, Marketing, and E-Commerce. Data mining models derived from topological relativity describe a multitude of economic and financial phenomena such as the bond, stock market, stochastic dynamics etc. Data Science and the developments in the cognitive neurosciences have initiated biomedical research, which helped in generation of data to find structural, architectural properties of brain networks.

When the term “Developed world” get defined as the countries in the Organization for Economic Co-operation and Development (OECD), and their investing nations i.e., EU, multitudes get surprised since the share of the world population in this region is only seventeen percent. Even though the world tries to reach the quality of OECD such as Health, Education, Civil Rights & Peace, at large we are far away from the goal. To approach the value based growth of OECD, we have to advance in technology which has potential for solving Social issues. Data science is one enabling technology in this sense.

Further, the buzzword “Data Science” becomes trendy since the discipline deals with data-driven science technologies while other buzzwords such as Analytics, Business Intelligence fail to establish. In other words, when an activity gets established with a clear definition, since Data Science is a multi-disciplinary approach, it constitutes the heart of interdisciplinary sciences itself.

The term “Data Science” is one of the booming, but yet to be established professions, which is rarely defined. Since the data is well disbursed in the present world, the science of utilizing, understanding and working with it is important in equal measure. To define data science, an ultra discipline, joining various fields such as Statistics, Computer Science, Information Theory, Mathematics, Pattern Recognition, Machine Learning, Data Mining, Databases, and Visualization, has to be derived. That stated, a data scientist is supposed to have in-depth understanding in various fields related to Data Science, not necessarily a mastery over all of them. When traits of a data scientist are analyzed in a broad perspective, the concept of a data scientist tends to merge with the concept of a data mining practitioner with features such as Analytics, and experimentation.

2. Key Concepts and Techniques in Data Science

The main goal of data science is to extract useful information and ultimately business interests from large complex data sets. There are several key concepts and techniques that play important roles in both data science and current data analysis. Firstly, to manage big data, we need to rely on multiple machines or one powerful machine. When we deal with large data, we need to do one data analysis on each machine and then combine the results, usually by analyzing how each machine produces the results. The machines can be shared or distributed depending on the scale of the data. We can utilize large shared data by sending the data to different machines to run in parallel, or program different machines to work independently and communicate the results. Sharing data has a lower communication overhead and is generally faster. The more processors are working in parallel, the faster the speed. In principle, it is possible that the execution time is reduced to a small value by sharing the data among a sufficiently large number of processors.

Data science is the study of extracting value from data and sharing it in techniques for storing and managing data, data mining, and advanced algorithms for data analysis. With the advent of new technology, the amount of data being produced by computer and communication technology, sensors, science, business, and the Internet is growing at an exponential rate. Moreover, both industry and government are placing increasing importance on big data and its implications for commerce and national security. Data science, in contrast with current data analysis methods, considers large and complex data sets and accounts for human factors. This paper discusses how data science helps the modern world and its role, describes the main concepts and methods used in data science, and analyzes some future directions.

3. Applications of Data Science in Various Industries

Data science is used by various industries like entertainment, oil and gas, retail, sports, etc., in different scopes by providing personalized recommendations, performance tracking, customer segmentation, etc.

Others

Applications of data science in manufacturing are predicting machine and equipment maintenance, real-time defect recognition, enhanced supply chain management, waste reduction, etc.

Manufacturing and Productions

Data science helps in the following scenarios in education: personalized learning, performance analysis, understanding different learning modalities, assessing effectiveness of individual students, etc.

Education

Applications of data science in government include smart cities, enhanced personalization for citizens, criminal data analysis, cybersecurity, disaster response, etc.

Government and Public Policies

Applications of data science in healthcare are prediction of the length of patient stay in the hospital, forecasting the admission rates of patients, predicting if a patient is likely to be a no-show or not, predicting chronic diseases among people, etc.

Healthcare

Applications of data science in finance are fraud detection, algorithmic trading, customer segmentation, sentiment analysis, etc.

Finance

Here are the ways in which data science is widely used in various industries.

4. Challenges and Ethical Considerations in Data Science

Data science, as a novel and rapidly evolving field, has attracted increasing attention in the academic and popular press. This attention is driven by a number of factors, including the rapidly evolving tools and technologies available for use in data science, increasing digitization of society, and unprecedented volumes of data created by mobile phones, social media, IoT devices, and more. Data science holds the promise to lead societal change via improved decision making in business, increased efficiency in governance, and deep advancements in all fields of scientific endeavor. At the same time, it also presents challenges: significant societal and practical concerns relating to the replicability of findings, privacy, commercial protection and discrimination, amongst others. Despite these concerns, data science as a field is of growing interest to students and beyond, with demand for data science skills increasing dramatically at universities and among private-sector and government employers.

Data science is a novel field rapidly evolving due to the rapid growth of technology and the increasing digitization of society. This growth brings various scientific, engineering, and job opportunities, but also presents significant challenges. Herein, we discuss four key challenges in data science, namely reproducibility of experiments, ephemerality of knowledge, generalizability of conclusions, and privacy. These challenges are diverse but highly interrelated: each influences the others. We reflect upon the ethical considerations underpinning these challenges and discuss the crucial role that data scientists should play in addressing them. Finally, we provide a critique as to how modern data science curricula are addressing these challenges and suggest how, in the future, they might adapt to better prepare students to meet them in practice.

5. Future Trends and Opportunities in Data Science

When I think about automated systems that collect, store, and analyze large amounts of data, the first technology that comes to mind is Data Science. Data Science is the field that analyzes and manipulates data, often massive amounts of data, and utilizes it to reduce uncertainty, prevent errors, and create business value. It is a fertile field that allows for decision automation based on data that is extremely useful in the development or improvement of products and services. Data Science uses a combination of statistics, data analysis, machine learning, and domain expertise to deliver data insights and recommendations that may help the business or the society to take better decisions, understand their results better, detect trends, and plan effective strategies. It is a continually developing field that has attracted the largest number of experts in the world.

For the trend towards the use of transformers other than BERT, it seems to me that this does not have the strength (at least not for now) to replace BERT. Some of the reasons that lead me to believe this are:

BERT has been constantly proving to be a very reliable model focused exclusively on understanding the context of each sentence and predicting the next sentence in the right context and in the right sequence. This is what really differentiates BERT. The more information that is passed during training, the better it can contextualize a word or an expression. Lastly, it is important to emphasize that all results presented here were obtained with the uncased version of BERT-base.