The numbers, character types, or signifiers through which a computer performs operations, which can be stored and transferred as electrical signals and registered on magnetic, optical, or mechanical media. Big data refers to large, difficult-to-manage volumes of data – both organized and unstructured – that populate businesses daily. However, it is not just the type or quantity of data that is important; it is what organizations do with the data pertaining. Big data can be analyzed to gain insights that help enhance decisions and provide self-belief in business development moves. The value is determined by how you use it. You could really look for answers that:
1) simplify resource utilization,
2) efficiencies and improve,
3) optimize product design,
4) drive new revenue - enhancing possibilities, and
5) enable sensible decision making by analyzing visualize data.
Big data provides important insights in and out of customers those businesses can use to improve their advertisements, adverts, but instead promotional offers in effort to expand customer conversions and sales. Both chronological and real-time data can be analyzed to assess changing consumer or organizational types of requests, allowing businesses to become even more available to respond needs and desires. In big data systems, various data types will need to be kept and controlled together. Furthermore, big data applications frequently include different datasets which might not be assimilated at the outset. Big data analytics may try to determine product sales by trying to link data from previous sales, comes back, reviews online, and customer care calls. IO
As-a-service infrastructure: Data-as-a-service, technology, and platform-as-a-service all reinforce the notion that instead of mining information, data license fees, or channels for running Big Data technology, it could be made available "as a customer experience" than as a commodity. As even the practitioner possesses all the involved in setting up and wanting to host the architecture, this decreases the initial capital investment required for consumers to start to put their own data, or social media, to work for them. Data mining: The process of extracting insights from the data is known as data mining. Even though Big Data is still so large, it is mainly managed by computational modeling that are computer controlled, such like decision trees, maintains proper, and, more lately, computer vision. This can also be doing think of as using machines' brute numerical power to detect trends in information that would be invisible to the eye due to the dataset's sophistication.
Big Data and Car Fuel Data Analysis
Data science: is the specialized discipline concerned with transforming data into useful information such as important discoveries or modeling techniques. It effectively taken from sectors such as statistical data, math and science, computer science, connectivity, and specialized knowledge such as industry knowledge.
Hadoop: is a Big Data information technology framework that has been officially announced into the public sphere as open source and therefore can be voluntarily used nowadays. It is made up of a series of configurations, each of which is tailored to a different critical step in the Big Data process.
MapReduce: is a computer technology protocol for processing of large data - sets that was developed in response to the troubles of reviewing and understanding extremellarge datasets using traditional computing methods and techniques. It contains, as the name implies, of two techniques: mapping and analyzation.
Predictive modelling: At its most basic, this is predicting just what would take place next relying on data from previous events. Prognostications have become more exact in the Big Age of big data but there is more data available than ever before. Predictive analysis is a crucial component of most Big Data proposals.
NoSQL: refers to a structured database that is intended to hold something beyond data that is simply organized into tables, single row, and editorials, as in a relational database system. Because Digitalization is often messy, disorganized, and therefore does not easily fit in to other traditional information methodologies, this metadata format has proved time and time to be very prevalent in Big Data applications.
Python: is a programming language that has grown in popularity in the Big Data space owing to its capacity to function effectively with sizable, disorganized data sources. It is thought to be easier to learn for a complete novice in data science than most other language groups such as R.
“A Critical Evaluation of the Big Data Approach to Car Fuel's Data Analytics”
The aim of this research and report is to critically analyse Big Data approaches to Car Data Analysis as implemented in Assignment 1 using Tableau. You should discuss the advantages and disadvantages of the approach of Big Data Analytics from a technical and business perspective (please refer to the marking guidance in the Marking Criteria).
You are also required to discuss the Big Data Analytics method/tools you have applied in producing the output required in Assignment 1. You should provide a rationale and justification for your choices, supported by the relevant academic literature.
The suggested word count for this report is max. 1000 words.
- Cover sheet completed
- In-text citations correctly written
- Background of BDA tools provided
- Critical analysis
- Recommendations given
- Spelling and grammatical errors checked
You have provided a background of Big Data Analytics tools to facilitate car fuel data analytics in the given scenario. Your introduction reads well and provides an overview of this evaluation report. The suggested word count for the introduction is 150 words
You have provided a critical analysis and justification of the use of Big Data Analytics tools to complete the tasks in this scenario. The suggested word count for this section of report is 600 words
You have provided recommendations and a conclusion for the successful implementation of a Big data analytics solution for the scenario in Assignment 1.
The suggested word count for this section is 250 words
You have used an appropriate report layout and formatting style (see the guidelines below), as well as academic citations and a reference list. Your report should be free from grammatical and spelling errors.
How will your work be assessed?
Your work will be assessed by a subject expert who will use the marking grid provided in this assessment brief. When you access your marked coursework, it is important that you reflect on the feedback so that you can use it to improve future assignments.
How will feedback be provided?
Students will have access to formative feedback on each task set in workshops, thereby helping them to refine their approach to the summative tasks that have been set. However, please note that this feedback is limited to recommendations on improving your work. Lecturers will not confirm any grades or marks. The feedback can be one-to-one or in-group sessions.