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ASSIGNMENT 1
Qualification BTEC Level 5 HND Diploma in Computing Unit number and title Unit 06: Planning a computing project Submission date Date Received 1st submission Re-submission Date Date Received 2nd submission Student Name Vu Duy Hoa Student ID BH Class Assessor name Vu Anh Tu Student declaration I certify that the assignment submission is entirely my own work and I fully understand the consequences of plagiarism. I understand that making a false declaration is a form of malpractice. Student’s signature Hoa Grading grid P1 P2 P3 P4 M1 M2 D
❒ Summative Feedback: ❒ Resubmission Feedback:
Grade: Assessor Signature: Date: IV Signature:
I. Introduction
Big Data is a term used to describe the processing and analysis of large, complex, and diverse datasets using specialized technologies and methods. Big data typically includes information from multiple sources and can exist in various formats, including text, images, videos, audio, data from social networks, IoT sensors, and many other data sources. Processing big data helps extract valuable insights, conclusions, and predictions from the data to support decision-making and create value for organizations and individuals. The project 'This project aims to leverage big data to enhance understanding of the real estate market and support investment decisions in this sector' highlights the application of technology and big data analytics to gain deeper insights into the real estate market. Here is a general introduction to this project: Project Objective: This project focuses on using big data to improve knowledge about the real estate market and support investment decisions in this sector. The primary goal is to leverage big data to generate valuable information, predict market trends and changes in the real estate market, and provide this information to investors and individuals interested in the real estate sector. Implementation: The project begins with data collection from various sources such as real estate transaction systems, websites, government agencies, and survey projects. This data is then analyzed using big data analytics tools and techniques. Through analysis, the project identifies market trends, predicts the future value of real estate properties, assesses potential locations, and forecasts market demand. Benefits: This project optimizes investment decisions in the real estate sector. Investors gain specific information and accurate market predictions, enabling them to make informed investment decisions. Additionally, the project has the potential to support sustainable development in the real estate sector and improve asset management."
1. Project purpose: Objective : This project aims to leverage big data to enhance understanding of the real estate market and support investment decisions in this sector. Implementation: Data Collection: Gather data related to real estate prices, real estate transactions, property sizes, locations, infrastructure, and factors influencing property values. Data sources may include real estate transaction systems, websites, government agencies, and survey projects. Data Analysis : Utilize big data analytics tools to construct predictive models for property values based on key factors. Analyze the data to identify market trends, price fluctuations, and forecast the future of the real estate market. Future Value Prediction : The project can predict the future value of real estate properties in specific areas based on information from predictive models. This can assist investors and individuals in making better- informed decisions regarding real estate investments. Location Analysis: The project can identify potential locations for real estate development based on regional conditions and various factors such as public utilities, schools, hospitals, and market demand. Market Demand Prediction : Utilize big data to forecast market demand for real estate in the future, aiding real estate developers in planning property development projects based on these forecasts.
II. Information gathering and data collection relevant primary research for an identified theme (P1).
1. Primary research:
Giới thiệu chung về các phương pháp tự thu thập dữ liệu gồm (phỏng vấn, quan sát, khảo sát …).
2. Qualitative Research Method:
a. Definition:
Khái niệm.
b. Research:
Phương pháp đặt câu hỏi định tính, và mục tiêu em đặt câu hỏi đó nhằm thu thập thông tin gì?
c. Results:
Name: Age: Job: Company: Question: Answer: Name: Age: Job: Company: Question: Answer: Name: Age: Job: Company: Question: Answer: Name: Age: Job:
Company: Question: Answer:
d. Summary:............................................................................................................................................
Chỉ đưa ra nhận xét gọn về dữ liệu các em thu thập được, không nhận xét
3. Quantitative Research Method:
a. Definition:
Định nghĩa.
b. Research:
Nêu rõ phương pháp các em sử dụng để thu thập dữ liệu định lượng.
c. Results:
Chụp ảnh kết quả thu thập
4. Primary Research Findings:
Trình bày kết quả phương pháp thu thập dữ liệu primary research dựa trên phương pháp định tính và định lượng
III. Information gathering and data collection relevant secondary research for an identified theme (P2).
1. Secondary research
. Secondary research is the process of collecting and utilizing existing information and data, rather than gathering new data from its original source. This type of research relies on examining and using materials, documents, reports, studies, statistical data, and available information to acquire knowledge or necessary information to answer research questions or address specific issues. Examples of secondary research: Example 1: A company wants to understand the new mobile phone market. Instead of conducting a survey on their own, they search for and utilize market reports available from market research companies like Nielsen or Gartner. These reports contain information on consumer trends, market competition, forecasts of future development, and information about existing products and brands. Example 2: A student is writing an essay on the impact of climate change on the ecosystem in a specific region. Instead of conducting on-site research, they search for and use published research papers on the topic. These papers provide information on the impact of climate change and changes in the ecosystem based on previous research.Data source
Data Collection Method: Data from scientific research is usually published in research papers and documents. You can extract data from these sources to use in your research.
3. Secondary Research Findings
a. Quantitative method results:
Kết quả nghiên cứu định tính
b. Qualitative method results:
Kết quả nghiên cứu định lượng
2. Operational efficiency of businesses in the Economic area Operational efficiency of businesses in the context of "Big Data of Economics" refers to the ability of companies to optimize their operations, processes, and resource utilization using large-scale data analytics and insights from the economic domain. Here's how operational efficiency applies to businesses in the field of Big Data of Economics:
- Resource Allocation: Businesses use big data analytics to make informed decisions about resource allocation, including manpower, capital, and time. By analyzing economic data, they can identify areas where resources can be allocated more efficiently, reducing waste and maximizing productivity.
- Supply Chain Optimization: Big data analytics can help businesses streamline their supply chain management. This involves improving inventory management, logistics, and procurement processes to ensure products are delivered to customers efficiently and at the lowest cost.
- Customer Engagement: Understanding economic data allows businesses to tailor their marketing and sales strategies more effectively. They can target the right customer segments, predict consumer behavior, and personalize offerings, leading to improved customer engagement and higher sales.
- Risk Management: Businesses can use economic data to assess and mitigate risks more effectively. By analyzing market trends, financial indicators, and economic conditions, they can make informed decisions regarding investments, financial planning, and risk exposure.
- Operational Processes: Companies can enhance their internal operations by using big data analytics to optimize production processes, streamline workflows, and reduce operational costs. Economic data helps in forecasting demand and making decisions about production capacity and resource allocation.
- Innovation and Research: Big data of economics provides insights into emerging economic trends, market dynamics, and consumer behavior. This information can guide businesses in their innovation and research efforts, enabling them to stay competitive in rapidly changing markets.
- Competitive Advantage: Operational efficiency driven by big data insights can lead to a competitive advantage in the marketplace. Companies that leverage economic data effectively can respond to market changes more rapidly and provide better products or services to their customers. .
3. Big data application impact to operational efficiency of bussiness 2. Operational efficiency of businesses in the Error! Bookmark not defined.
The application of big data to business operations can have several positive impacts on overall operational efficiency. Here are some significant effects of using big data in business:
- Optimizing Resource Management: Big data allows businesses to monitor and manage resources like human capital, capital, and time more efficiently. This helps optimize resource allocation, reduce waste, and increase productivity.
- Improving Production Processes: Big data can help businesses enhance production processes by monitoring and optimizing every aspect of the process, from inventory management to production lines. This results in more efficient production and cost savings.
- Supply Chain Optimization: Utilizing big data, businesses can improve supply chain management by optimizing transportation, supplier selection, and predicting customer demand. This ensures that products are delivered on time and at the lowest cost.
- Enhancing Customer Interaction: Big data enables businesses to gain a better understanding of customer behavior. They can create personalized marketing campaigns and services based on this data, leading to improved customer interaction and satisfaction.
- Prediction and Risk Management: Big data provides information to predict market trends and business risks. Businesses can use this data to make more informed decisions about investments, financial planning, and risk management.
- Improving Product and Service Quality: By tracking and analyzing data, businesses can optimize the quality of their products and services. This leads to higher customer satisfaction and a competitive edge.
- Innovation and Research: Big data provides insights into emerging market trends, market dynamics, and consumer behavior. This information can guide businesses in their innovation and research efforts, helping them stay competitive in rapidly changing markets.
- Competitive Advantage: Utilizing big data efficiently can lead to a competitive advantage in the marketplace. Companies that leverage big data can react more swiftly to market changes and provide better products or services to their customers.
