Data Science and Data Analytics: Opportunities and Challenges

Data science is a multi-disciplinary field that uses scientific methods to extract knowledge and insights from structured and unstructured data. It is the future of AI and a necessity for making things easier and more productive. Big Data analytics/data analytics are used to analyze this large amount of data to predict valuable information and for decision-making. However, there are several concerns such as complexity, scalability, privacy leaks, and trust issues. This book covers all possible areas, applications, and challenges in data science and data analytics with a comparative analysis/taxonomy. It is useful for academics, data scientists, and stockbrokers from industry/business to design optimal strategies to enhance their firms productivity.

Format: Hardback
Length: 464 pages
Publication date: 23 September 2021
Publisher: Taylor & Francis Ltd

Data science is a dynamic and interdisciplinary field that employs a wide range of scientific methods, processes, algorithms, and systems to extract valuable knowledge and insights from both structured and unstructured data. It holds immense potential as the future of artificial intelligence (AI) and is essential for enhancing productivity and simplifying complex tasks in various industries.

At its core, data science involves the exploration and discovery of data, uncovering hidden patterns, and extracting meaningful insights. This process involves a combination of statistical analysis, machine learning, and artificial intelligence techniques to analyze large datasets and extract valuable information.

One of the key challenges in data science is the handling of large amounts of data. With the rapid growth of digital data, data scientists must develop efficient and scalable methods to analyze and process this data. This requires the use of advanced technologies such as Hadoop, R, and Python, which enable data scientists to efficiently store, manipulate, and analyze large datasets.

Another challenge in data science is the complexity of the data. Data can be structured, semi-structured, or unstructured, each with its own unique characteristics and challenges. Structured data, for example, is organized and labeled, making it easier to analyze using traditional statistical methods. Unstructured data, on the other hand, is not organized or labeled, and requires advanced techniques such as natural language processing and machine learning to extract meaningful insights.

In addition to these challenges, data science also faces concerns related to privacy, security, and trust. As data is collected and analyzed, there is a risk of privacy breaches, data misuse, and unauthorized access. Data scientists must therefore ensure that sensitive data is protected and that ethical practices are followed throughout the data science process.

Despite these challenges, data science offers numerous opportunities for innovation and growth. It can be used to improve healthcare outcomes, optimize business processes, and make informed decisions in various industries. For example, data science can be used to develop personalized medical treatments, predict disease outbreaks, and optimize supply chain management.

In conclusion, data science is a rapidly evolving field that has the potential to transform the way
way we understand and analyze data. It requires a combination of technical skills, analytical thinking, and ethical considerations to extract valuable knowledge and insights from complex data. While there are challenges to be addressed, the opportunities for innovation and growth in data science are immense, and it is likely to play a critical role in shaping the future of AI and various industries.

Weight: 993g
Dimension: 254 x 178 (mm)
ISBN-13: 9780367628826