Write a research paper in APA format on a subject of your choosing that is related to Business Intelligence. Integrate what you have learned from the course resources (.e.g. Textbook Readings, Discuss

Write a research paper in APA format on a subject of your choosing that is related to Business Intelligence. Integrate what you have learned from the course resources (.e.g. Textbook Readings, Discuss
Can you please revise the paper with the below-mentioned additions? Place some appropriate side headings for the paragraphs. (Refer to the uploaded reference). As it is now looking like a plain discussion. Include some images or tables supporting the explanation. (Refer to the uploaded reference) If possible, include any comparisons possible. Include the reference links for the references provided. (Doi number link) For Example: Larson, D., & Chang, V. (2016). A Review and Future Direction of Agile, Business Intelligence, Analytics, and Data Science. International Journal of Information Management, 36(5), 700-710. Respective link Provide the intext citation for each of the paragraphs, that is indicating which of the reference is being referred for the information provided.
Write a research paper in APA format on a subject of your choosing that is related to Business Intelligence. Integrate what you have learned from the course resources (.e.g. Textbook Readings, Discuss
Running head: DATA SCIENCE AND AI 0 Data science and AI roles continue the trend towards specialization Business Intelligence Reference Copy Data science and AI roles continue the trend towards specialization Introduction The concept of big data goes back to the 1960s. Back then, the world of data was just beginning to gather massive amounts of data. In 2005, the people started to realize how much data they generated through various online platforms. The rise of open-source frameworks such as Spark was instrumental in the growth of big data. Since people are still creating massive amounts of data, it’s not surprising that they are doing it all. Until recently, Big Data was the talk of the town, with companies hiring data scientists to handle the various tasks related to analyzing and extracting data. The demand for data scientists has increased significantly since employers understand the importance of extracting and validating the data (Barocas & Boyd, 2017). Cloud computing has allowed developers to get even more creative with their data, and graph databases are also becoming more important. Data Science Data science is a multi-disciplinary discipline that uses the collected data to extract actionable insights. It involves preparing and processing the data for analysis and reporting. The rise of data-driven industries has brought about a massive revolution in almost all sectors. Data Science combines various scientific and artificial intelligence techniques to extract value from vast amounts of data. Data preparation involves cleansing, aggregation, and manipulation of data. This process can be done in various forms such as cleansing, data mining, and analysis. The goal is to find patterns in the data and produce predictions that support business decisions. Data scientists use a variety of tools and methods to analyze and extract actionable insights from the data (Barocas & Boyd, 2017). A data scientist must be able to: Learn how to analyze and prepare data using various tools and techniques, Use Artificial Intelligence and Machine Learning to extract valuable data from various sources and illustrate stories that clearly communicate the meaning of results. Artificial Intelligence AI is a constellation of technologies that work together to enable machines to have the intelligence of humans. Machine learning, natural language processing, and deep learning are some of the various AI technologies that are making their way into businesses. Today’s AI uses the same hardware and programming interfaces as traditional software. Its future generations will use brain-inspired designs to make data-driven decisions. There are four main categories of AI initiatives: Reactive AI, Limited Memory AI, Theory of Mind AI, and Self-Aware AI. Reactive AI is based on real-time data for decision making whereas Limited Memory AI is based on storage data. Theory of Mind AI considers various subjective elements when making decisions, such as user intent. Self-aware AI has a human-like consciousness that can set goals and use data to reach them (Russell & Norvig, 2002). Job Significance With the rise of data science, the demand for data scientists has become more prevalent. Data science and Artificial Intelligence positions grew significantly in 2020 due to the increasing number of people who rely on data. These fields hold many job titles such as data scientist, data engineers, AI/ML engineers, data analyst, and specialist. In 2021, various new data science and analytics trends are expected to emerge. This category saw a 32% rise in hiring in 2020 as firms looked to protect themselves from the disruptive effects of the pandemic. This demand will create over 11 million jobs by 2021. Data Engineer Vs Data Scientist A data engineer prepares the infrastructure for analysis. They work with various developers and organizations to ensure the availability and integrity of their data. Data engineers usually have a programming background and are fluent in various languages. They can also work with analytical methods and techniques. They are experienced in handling massive datasets, such as those that contain data from oceans. Their goal is to help data Scientists turn these massive datasets into actionable insights. Data science is now considered an advanced level of analysis that’s driven by machine learning and computer science. Before data engineering was created, data scientists handled the data themselves. Data scientists are trained to analyze and interpret the data that was collected and prepared for them. There are some overlapping skills between data scientists and data engineers, but these roles are not interchangeable. Data scientists usually learned how to program to perform more complex analysis on their data sets (Davenport & Patil, 2012). Data engineers are responsible for analyzing, designing, testing, and building systems that enable the management and analysis of large volumes of data. They also create the infrastructure and architecture for generating data. They work with data scientists to create free-flowing data pipelines that can handle big data. They also provide deep learning and machine learning techniques to enable real-time analysis. The roles and responsibilities of data engineers do not cover the entire operation of the company’s computing systems except for those portions of the system that are associated with the data pipeline. Data scientists are responsible for analyzing and reporting on the findings of their studies. Data scientists often interact with the infrastructure of data warehouses and other facilities related to data, but they seldom build or maintain it. Instead, they work on developing new applications and services that help businesses grow. Data scientists can also work with business leaders to gain a deeper understanding of their customers’ needs and present complex findings in a way that can be easily followed by a general audience (Davenport & Patil, 2012). Most of the time, a data engineer should be familiar with SQL and its various features. Along with expertise in SQL/DML/DDL primitives, they should be able to perform various data engineering tasks such as entity-relationship modeling, data normalization, and indexation. Just as importantly, data engineers can no longer rely on traditional drag-and-drop ETL (Extract Transform and Load) data engineering to perform their jobs. Instead, they are embracing a more programmatic approach. Developing an efficient and resilient ETL framework will enable data-driven businesses to achieve their goals more effectively. It will also help them avoid repeating the same mistakes that they made in the past. Extract: Upstream data is received and moved to final or incremental places. Transform: Convert raw data into datasets that can be analyzed. Load: Send processed data to a destination for use or to a temporary location for ETL treatment. ETL plays a crucial role in data warehousing which is a part of data engineering. Data modeling is a process that involves extracting business information from structured data sources. This approach is a step towards data engineering, where the objective is to provide a complete view of a company’s entire operations. By performing data cleansing, an ETL solution can improve the quality of data by removing non-important data elements. It is also commonly used to create smaller target data repository that can be updated quickly. Centralized Vs Embedded Model A centralized data science team is an approach that enables companies to have a single point of contact for all their data scientists. This team coordinates all the work that their data scientists do, and their projects are managed directly by their managers. An Embedded data science team is where the data scientists are hired by the functional team responsible for the product or service but have little or no knowledge of the other data scientists working for the company. Advantages of Centralized team Advantages of Embedded team 1. Data scientists are well-equipped to manage teams and develop new skills due to the large org. They also have plenty of on-the-job training opportunities. 2. With deep expertise in data, cloud, security, and DevOps, the team has the necessary tools and infrastructure to enable rapid development and deployment of AI-native products. This ensures that the organization can maintain its competitive advantage in the market. 1. Agility and Responsiveness: Because teams are dedicated to the work of their respective departments, they can respond to the varying needs of their clients. 2. Having a decentralized structure allows data scientists to focus on their core expertise without worrying about the operations of other business units. Conclusion Data science has become the norm in today’s world. It enables the users to collect, analyze, and interpret the raw data. It has helped the businesses to improve their operations by performing various data-driven tasks. Data scientists are experts in analyzing and displaying data, while data engineers are more concerned with ensuring that the data flows properly. Both professions have a huge demand and are highly specialized. The goal of a data scientist is to provide the best possible solutions that meet the increasing demands of today’s world. This profession provides the necessary tools and resources to enable individuals to excel in their chosen field. In conclusion regardless of the career path, it is important to have the necessary education to be successful. There are some overlaps between data science and data engineering, but these positions are both lucrative and require extensive training and experience. Reference Davenport, T. H., & Patil, D. J. (2012). Data scientist: the sexiest job of the 21st century. Harvard Business Review, 90(10), 70–76. Retrieved from https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century. Barocas, S., & Boyd, D. (2017). Engaging the ethics of data science in practice. Communications of the ACM, 60(11), 23-25. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64-73. Russell, S., & Norvig, P. (2002). Artificial intelligence: A Modern Approach. Silberg, J., & Maryilka, J. (2019). Tackling bias in artificial intelligence (and in humans). McKinsey Global Institute. Please do consider the below comments and make the changes accordingly. Comments from Professor: Comments Good points mentioned in the Introduction about big data, although the Introduction should also include some discussion about AI roles which is the central to the theme of the paper. Comments The central theme of the study is developed, although the paper must include at least 5 peer reviewed sources (e.g., Academic Journal Articles, Textbooks, or Government Documents). In this paper, the (Silberg & Maryilka, 2019) is not peer-reviewed. Also, at least one of the textbooks for this course must be used as a reference source for the paper. Comments The Conclusion should also include some key points about AI roles which is a central theme of the paper.