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I have attached the document with 4 discussions and I need to reply to each one of them in 150 words with APA format. hussain_replies.docx Unformatted Attachment Preview Big data 2 days ago Vikram Lanka week1 C OLLA PSE Data science is a multi displinary field which is used for various scientific methods,algorithms for extracting the information from both structured and unstructured data. Data science include some concepts of big data and data mining concept. It uses more powerful algorithms and to solve the problems which are based on maths,statics and etc .Data science includes the job of data engineers and research,analysis. Data science is a study of the data where it can be structured or unstructured data.At first the data is extracted and next transformation of data is done and at last data analysis and predicting of the data is done to gain knowledge of the data and most of the employee assist decision making process Big data deals with large amount of data .the properties that differs the data and big data are velocity,veracity,volume of the data .The various complex tools are used in big data when compared to data science .Tools used in big data are flink,spark . basically it is used in telecommunication and emails. Basically big data is about storing of the data where the data science is about analysis of the data.big data basically deals with one of the part of the data science. Data analytics basically about examining the data sets in order to find the conclusion of the information which it contains.Tools used in data analysis are apache spark and r programming language and when compared to data science and big data analytics and big data the annual salary for data science will be more .Applications areas of data analytics are health care centres and gaming . Data science is one of the practical applications of machine learning in mainly focus about the problems solving in the real world.machine learning is one of the standard requirement for data scientist and it has ability to evaluate the processes which is helpful for improvement of data science skills. Machine learning and statistical learning both depends upon the data sets. Machine learning deals with the patterns of the data sets and statistical learning deals with the mathematical operations of data.machine learning is used for application in day to day life for example when your tagging a person in instagram or facebook and while using google search engine it produces frequently search history of yours.Statistical learning basically contains topics of regression,classification and various resampling methods and concepts . Artificial intelligence basically it is an ability of computer to think and learn on it own. Machine learning is subset of artificial intelligence.both of the includes the deep learning concepts .In artificial intelligence it includes making of intelligent machines and programs but in machine learning it has ability to learn but not explicitly programmed .Artificial intelligence mainly used in travelling apps such ubers. References: https://data-flair.training/blogs/big-data-vs-data-science/ https://towardsdatascience.com/cousins-of-artificial-intelligence-dda4edc27b55 3 days ago Vinay Kumar Thota Discussion 1 C OLLA PSE Data Science, Big data analytics, Machine learning Although the topics Data Science, Big data analytics, Machine learning and Artificial Intelligence (AI) are almost the same realm and interrelated, they have their specific uses and significance. All these domains may be overlapped from time to time, but in principle, each term has its own distinctive usage. We continue with the word data science, since it occupies the highest place in the hierarchy of technology relevant to results. Artificial intelligence Artificial intelligence (Russell, & Norvig, 2016) pertains to machinery simulating the human mind and its functions. Some specific designs like an artificial neural network may demonstrate human-like reasoning capabilities. The main human tasks handled by an AI computer include logical thinking, understanding and self-correction. Artificial intelligence has always had a wide range of implementations but it is one of the most challenging technologies to develop. Machines are not clever in themselves and so we need plenty of computational power and ‘big data’ to recreate the thinking of people. Machine learning Machine learning is a system’s ability to adapt to the context and gaining experience so that its performance or efficiency is enhanced without any specific programming. The aim of machine learning is to enable algorithms to train from the data presented, gain insights and infer previously uncharacterized data through the data collected. Machine learning’s three fundamental models are supervised, unattended and strengthened. Relevant information is derived from the data science. This hires many technologies in various fields, such as algebra, machine learning, software development, mathematical analysis, system processing and simulation, pattern recognition and understanding, modelling complexities, memory space, and cloud computing. Data science (Provost, & Fawcett, 2013) utilizes a wide range of technology that focuses on data such as SQL, Python, R, Hadoop, etc. However, statistical analysis, pattern recognition, distributed architectural design and more are also used extensively in order to extract purpose from massive data sets. This is what corresponds to big data analytics. Data scientists are experts whose have expert knowledge in data science areas which allows them to change roles in the development cycle of data science developments fast. Artificial intelligence is a very broad term for automaton to text interpretation applications. There are debates over whether or not we should strive towards high-level AI. This is still a technology which is being established. Machine learning is an AI type focused on a restricted range of activities. In reality, with some uses in real-world problems, it is the only true artificial comprehension. Data science is not a machine learning subset precisely, but uses tools from machine learning to analyse information and make forward-looking predictions. Machine learning is combined with other disciplines, such as Big Data and Cloud. Data science is a functional machine learning technology. Data science encompasses AI, including machine learning, in particular. Artificial intelligence means “smart machines,” so that they can decide on their own in accordance with environments with little or no human intervention. And “the means of making these machines smart” is machine learning. Today we are all seeing a nationwide and digitally exponential rise in information that leads to the notion of big data. Because of the challenges involved in integrating as well as using various techniques, algorithms, and advanced programming strategies for insightful analysis of large amounts of data, data science would be quite a task. There are therefore large or big data analytics and data science that have evolved from data science. References Bruijn, H. Janssen, M. (2017). Building cybersecurity awareness: The need for evidence-based framing strategies Bruijn, H. Janssen, M. (2017). Building cybersecurity awareness: The need for evidence-based framing strategies Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,. Info tech import 9 hours ago Anupama Raman Nair week1_Discussion C OLLA PSE InfoTech Import in Strat Plan Dr Aline Turik Anupama Raman Nair University of the Cumberlands Business strategy defines the direction in which the business is moving. It specifies the Vision, Mission and Goal of the organization. Organizational strategy is the organization’s design, as well as the choices it makes to define, set up, coordinate, and control its work processes. Information System strategy is the plan the organization uses in providing information systems and services. A coordinated business strategy with some set of actions to fulfill objectives, purposes and goals. Business Strategy always starts with a mission. Those mission will be stated in mission statements. And both Organization and Information Systems need to be moving in the same direction. It plays major role in financial and resource distribution Typically, we seem to think that changing or upgrading an information system (will only positively impact a business. Quite the opposite, in fact by making changes in organizational strategy or IT strategy first, it can make them misaligned with Business strategy References: • • http://barney.gonzaga.edu/~chen/mbus626/chapters_pres/summer/chapter_I_1_summer.pdf https://sis.binus.ac.id/2018/02/13/is-strategy-triangle/ 2 days ago Naveen Goparaju Discussion C OLLA PSE Business Strategy Information Technology Importance in Strategic Plan Dr. Aline Yurik University of the Cumberlands 1. The Information Systems Strategy Triangle is a straightforward structure for understanding the effect of IS on associations. From the Information System Strategy Triangle, we can see that the IS methodology triangle comprises of Business Strategy, Organizational Strategy and Information Strategy. A Business Strategy is a well-explained vision of where the business tries to go and how it hopes to arrive. It is the structure by which a business conveys its objectives. Authoritative procedure is worried about imagining a future for your privately-owned company, making an incentive according to your clients, and assembling and continuing a solid situation in the commercial center. Data Strategy implies the equipment, programming, application, database, methodology, individuals, and system. (Kourdi, J. 2015). As we probably am aware, the effectively firms business system drives both their hierarchical and IS procedures. It implies that they should look to adjust business, hierarchical, and IS techniques. IS Strategy is influenced by different procedures a firm employments. Hierarchical and Information Strategy are then needy upon the Business Strategy. Changes in any methodology require changes in the others to keep up balance Business IS and authoritative systems must be always balanced. IS technique consistently includes results on business and hierarchical procedure. Since the business procedure drives both Organizational and Information methodology, it keep a key job in the IS Strategy Triangle. In the event that the business methodology doesnt drive different systems, the triangle will become unbalance. That is, the equalization among the three methodologies would be broken, and it might prompt the disappointment of an association. Whats more, the associations are difficult to be fruitful in organizations without the parity in IS Strategy Triangle. 2. In the event that a conventional assembling organization that needed to exploit the Internet and the Web, the plan of action of the organization has transformed from the customary to E-business produce. Electronic markets permit participating contending organizations to control how a market in a specific business region works. This can have key ramifications because of way this controls the ecological danger and openings. Web business can possibly supplant conventional deals diverts in both the business-to shopper and business-to-business segments. Whats more online business can create new business openings, for example the closeout of electronic substance has given another channel to the conveyance of music, and it might be contended, given an alternate item. Generally speaking, the plan of action has changed, so the activity of the association additionally would be changed. (Kourdi, J. 2015). As a business procedure is created it ought to be utilized to advise the systems regarding the offices that make up the association, along these lines the Organizational and IS methodology of an organization would be affected by the business technique. At the point when an organization needs to change its plan of action from customary to E-trade, the Organizational and IS technique will likewise change following the Business Strategy with the goal that the IS Strategy Triangle can keep balance. Reception of internet business systems infers that new advances must be aced and better approaches for working will be experienced. An association must break down the prerequisites and guarantee that it can meet them with the goal that the association can have a high achievement in its business. References Kourdi, J. (2015). Business strategy: a guide to effective decision-making. London: Economist. Elsevier Butterworth-Heinemann. (2005). Business strategy. Oxford. ... Purchase answer to see full attachment