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ASSIGNMENT 1 FRONT SHEET
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 TRAN QUOC ANH Student ID BH Class SE06206 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 QUANH Grading grid P1 P2 P3 P4 M1 M2 D
❒ Summative Feedback: ❒ Resubmission Feedback:
Grade: Assessor Signature: Date: IV Signature:
- I. INTRODUCTION
- II. Content - 1. Project charter - 2. Project objectives - 3. Project Scope..............................................................................................................................................
- identified theme. A. Demonstrate qualitative and quantitative research methods to generate relevant primary data for an
- Quantitative research
- Qualitative research
- Efficiency in Various Academic, Scientific, and Economic.................................................................................. 3. Qualitative and Quantitative Analysis to the Impact of applying Big Data Technologies on Operational
- Methodology for Combining Qualitative and Quantitative Research to Investigate a Specific Theme
- B. Examine secondary sources to collect relevant secondary data and information for an identified theme.
- Primary research
- Secondary Research
- Academic, Scientific, and Economic 3. Primary Research on the Impact of Applying Big Data Technologies on Operational Efficiency in Various
- Operations: Insights from Secondary Research. 4. Exploring the Challenges and Opportunities of Big Data Techniques in Academic and Research
- economic 5. How the secondary data and information affect for an bigdata technology in academic, scientific and
- Comprehensive report
- Evaluation of Project Management Process and Research Methodologies
- C. Discuss the features and operational areas of businesses in an identified sector
- Project Logbook
- Project Team
- Efficiency in Various Academic, Scientific, and Economic.................................................................................. 3. Features of a datacenter business in the protection of the Big Data Big Data Technologies on Operational
- in Academic, Scientific, and Economic 4. Operational areas of a datacenters business to protect Big Data Technologies on Operational Efficiency
- D. Discuss the role of stakeholders and their impact on the success of a business
- What are Stakeholders.............................................................................................................................
- Role of stakeholders in the Big Data Technologies on Academic, Scientific, and Economic
- Economic 3. How stakeholders impact the business in the Big Data Technologies on Academic, Scientific, and
- III. Conclusion
- IV. References
- Figure 1:: WBS diagram Table of figure
- Figure 2: User survey questions in the style of quantitative research
- Figure 3:User survey questions in the style of quantitative research........................................................................
- Figure 4:User survey questions in the style of quantitative research........................................................................
- Figure 5:User survey questions in the style of quantitative research........................................................................
- Figure 6:User survey questions in the style of quantitative research........................................................................
- Figure 7:User survey rate
- Figure 8:User survey rate
- Figure 9:User survey rate
- Figure 10:User survey rate
- Figure 11:Qualitative research style user survey questions
- Figure 12:Qualitative research style user survey questions
- Figure 13:Primary research
- Figure 14:Secondary research
- Figure 15:Comprehensive report
- Figure 16:Project team
- Figure 17:Stakeholders
I. INTRODUCTION
Challenges and Opportunities of Big Data Techniques in Academic and Research Operations Whether we are aware of it or not, every day we produce vast volumes of data. Tech corporations collect data from everything we do online, including clicking on links, sending emails, watching videos on YouTube, and like posts on Instagram. Companies should use the vast quantity of data they have acquired to better understand their consumers and how they act. This explains why data science has become so much more popular in recent years. Big Data approaches have the potential to completely change how data is gathered, processed, and used in academic and research settings. Large volumes of data from many different sources, including scientific trials, surveys, social media, sensor networks, and administrative records, are now available to researchers. Unprecedented opportunities to produce insights, spot trends, and make data-driven decisions are presented by this abundance of data. An in-depth knowledge of the subjective elements of cloud computing's environmental effect may be obtained through qualitative research approaches. Researchers can examine the opinions, attitudes, and experiences of people and organizations engaged in cloud computing by gathering data through focus groups, interviews, and case studies. We may learn a great deal about environmental effect from the viewpoints of stakeholders, such as service providers, users, and experts in the subject, by diving into qualitative research. On the other hand, quantitative research methods involve the systematic collection and analysis of numerical data. This approach allows us to measure and quantify environmental impact, providing a more objective perspective. Quantitative research may involve collecting data through surveys, data mining, and statistical modeling. Researchers may become overwhelmed by the sheer amount of data produced by Big Data approaches, leading to an information overload. Sophisticated analytical methods and procedures are necessary to extract useful insights from massive data sets, and specialist expertise is needed to interpret and contextualize the results. Despite these obstacles, engineering offers chances for researchers to find hidden patterns and connections, support or refute established hypotheses, and develop a greater knowledge of complicated processes. Big data offers the academic and scientific community great benefits. Large-scale data analysis has the potential to spark novel discoveries, support multidisciplinary cooperation, and advance evidence-based decision making.
1. Project charter
The project charter defines the authority and responsibilities of the project manager and project team. It clarifies their role in decision making, resource allocation, and overall project management. The project charter serves as the foundational document that defines the purpose, objectives, and scope of the project. It demonstrates the importance of the project, establishes the roles and responsibilities of the project team and identifies relevant stakeholders. This project aims to investigate and evaluate the potential and problems related to applying big data technology in academic and research endeavors. The project's objectives are to maximize academic procedures, increase research outputs, and make better decisions by utilizing big data. The initiative aims to offer significant insights and practical recommendations for researchers and academic institutions by efficiently leveraging big data.
2. Project objectives
Project objectives identify the specific goals and outcomes that the project aims to achieve. They provide clear direction and purpose for the project and serve as the basis for planning, implementation and evaluation. Project goals must be specific, measurable, achievable, relevant and time-bound (SMART). They help stakeholders understand desired outcomes and guide the project team in its efforts to accomplish them.
- Identify the current state of academic and research activities as well as existing challenges and limitations.
- Explore and analyze potential applications of big data techniques in academic and research contexts.
- Evaluate the infrastructure requirements, data sources, and data management strategies needed to deploy big data techniques.
- Evaluate the ethical and privacy considerations related to the collection, storage, and use of big data in academic and research settings.
- Develop a roadmap and instructions for successfully deploying big data techniques in academic and research activities.
- Provide recommendations for academic institutions and researchers to exploit the benefits of big data effectively.
- Bringing products out for use so everyone can access technology.
3. Project Scope
Project scope defines the boundaries and level of work that will be performed within a project. For the project to explore and analyze the challenges and opportunities associated with implementing big data techniques in academic and research activities, the scope includes the following key aspects:
- Literary research and criticism: Conduct a comprehensive review of existing research, studies, and literature related to the implementation of big data techniques in academic and research activities.
- Collect and analyze data: Collect relevant data sources, including academic and research datasets, to analyze the challenges and opportunities of implementing big data techniques.
- Involvement of relevant parties: Interact with academic experts, researchers and stakeholders to gather insights, opinions and experiences related to the implementation of big data techniques in academic and research fields rescue. This can be achieved through interviews, surveys, focus groups or other appropriate methods.
- Infrastructure assessment: Evaluate the technological infrastructure requirements for implementing big data techniques in academic and research activities. This includes assessing the availability and suitability of the hardware, software, networks and storage systems required to collect, store, process and analyze big data.
- Data management and analysis: Investigate data management strategies, including data quality, data integration, data governance, and data security, to ensure robust and reliable processing of big data in academic and research activities. Analyze appropriate data analysis techniques and tools to derive insights and generate meaningful results.
- Analysis of challenges and opportunities: Analyze the identified challenges and opportunities related to the implementation of big data techniques in academic and research activities.
- Recommendations and instructions: Delivers actionable insights and best practices to solve challenges, optimize research outcomes, and enhance decision-making across academic and research sectors.
A. Demonstrate qualitative and quantitative research methods to
generate relevant primary data for an identified theme.
1. Quantitative research
Definition: Quantitative research is a research method in social sciences and anthropology that focuses on collecting and analyzing quantitative data to measure, identify relationships, and draw valid conclusions.. statistical properties and generalizability of the research phenomenon. [4]
- It is necessary to do a quantitative analysis of the project you are working on.
- Data Analytics: Analyze existing data sets, such as data from academic institutions or research organizations, to examine the challenges and opportunities associated with big data techniques. This may involve analyzing data about data infrastructure, data 9 management practices, research results, and resource usage. Statistical analysis can be applied to identify correlations, trends, and statistical significance.
Figure 2 : User survey questions in the style of quantitative research Figure 3 :User survey questions in the style of quantitative research
Figure 4 :User survey questions in the style of quantitative research Figure 5 :User survey questions in the style of quantitative research Figure 6 :User survey questions in the style of quantitative research This is data collected after surveying users.
Figure 10 :User survey rate
2. Qualitative research
- Definition: In the social sciences and anthropology, qualitative research is a strategy that concentrates on gathering and evaluating high-quality data in order to obtain a comprehensive grasp of the circumstances, viewpoints, experiences, views, and intangible aspects of the phenomena being studied. [5]
- Advantages of Qualitative research
- Deep understanding of experiences and opinions: Researchers can obtain a comprehensive grasp of the experiences, viewpoints, values, and unquantifiable characteristics of individuals or groups of persons through qualitative research. It offers comprehensive and perceptive details on the emotions, ideas, and mental conditions of research subjects.
- Explore and develop new concepts: Qualitative research allows the researcher to explore and develop new concepts, new ideas, and theoretical models.
- In-depth and multi-dimensional analysis: Researchers can examine a study topic in-depth and from several angles thanks to qualitative research. From the data gathered, researchers can extract patterns, trends, and deeper meanings using techniques like content analysis, social interaction analysis, and definitional analysis.
- Flexibility and combination: Qualitative research is very adaptable and may be used in conjunction with other methodologies. It can be applied in a mixed methods approach together with experimental or quantitative research to give a thorough understanding of the topic being studied. Richness and depth of information: Qualitative research offers in-depth knowledge and rich details about the issue being studied. By collecting comprehensive and varied data from research subjects, the investigator can enhance their comprehension of cultural influences, customs, beliefs, and other immeasurable facets associated with the investigation. connected to the phenomenon being investigated.
- Method of collecting user information: Survey users by giving them flyers and asking them to fill in their answers. This is information collected using qualitative research methods:
Figure 11 :Qualitative research style user survey questions
4. Methodology for Combining Qualitative and Quantitative Research to Investigate a
Specific Theme
Mixed methods, which integrate both qualitative and quantitative research techniques, are frequently employed in research to offer a thorough and in-depth examination of a certain subject. By combining the two approaches, one may better utilize their respective strengths and complement one another, yielding more comprehensive and multifaceted data. Here is an example of how to combine qualitative and quantitative research in specific research:
- Step 1: Gather qualitative data: To gather in-depth information about people's thoughts, worries, and experiences, use qualitative research techniques including content analysis, discussion groups, and interviews. one individual or a collection of researchers. To find out how Big Data Technology has altered research, teaching, and learning approaches, you may interview researchers, lecturers, and students.
- Step 2: Collect quantitative data: Use quantitative research methods such as surveys, statistical analysis, or regression analysis to collect and analyze numerical data related to the research topic. rescue.
- Step 3: Analyze and synthesize: After gathering information using both approaches, you may evaluate and combine the findings from the two sections of the investigation. A summary of the relationship between Big Data Technologies and performance in various academic, scientific, and scientific domains may be produced by contrasting and connecting data from qualitative and quantitative research. economics and studies. A more realistic image of the effects of big data technology may be produced by combining data from quantitative analysis with insights gained from interviewees' unique viewpoints and experiences.
B. Examine secondary sources to collect relevant secondary data and
information for an identified theme.
1. Primary research
Definition: Primary research is a research method in science and social studies that aims to collect new and specific data for a particular research problem. It is the process of gathering information directly from the original source through methods such as surveys, interviews, observations, or experiments. Primary research for the challenges and opportunities of big data techniques in academic and research operations could involve various data collection methods. [1]
Advantages of Primary research:
- Accurate and specific data: Primary research allows researchers to collect data directly from the original source, ensuring the accuracy and specificity of the information collected.
- Flexibility and adjustment: When doing primary research, the researcher can be adaptable and modify the data gathering procedure to fit the goals and specifications of the study. They can attain flexibility and customisation during the study process by making adjustments to the questions, techniques, and data gathering processes.
- Generates new data: Primary research allows for the generation of new data and provides unique information about the research topic.
- Interactive and insightful : By using methods such as interviews and observations, primary research allows the researcher to interact directly with individuals or groups of people in the research setting.
- Flexibility in changing research direction: During the data gathering phase, primary research enables researchers to freely adjust their study direction. When new information becomes available, researchers might modify their study strategies and objectives to capitalize on the enhanced understanding.
- Diversity and originality: Primary research can be conducted in many different environments and with diverse research subjects. Disadvantages of Primary research: Figure 13 :Primary research
Advantages of Secondary research:
- Overview of the research field: Secondary research helps provide an overview of the specific research field. It allows the researcher to better understand the issues, trends, and research progress that has taken place in that field.
- Synthesize and analyze data: Secondary research helps synthesize and analyze available data from many different sources. This helps create a more comprehensive and detailed view of important aspects of the study.
- Evaluating research quality: Secondary research evaluates the quality of existing studies, including research methods, sample models, results and conclusions.
- Identify trends and relationships: Secondary research helps identify development trends, patterns and relationships between research variables. It helps shape new directions for research and provides a basis for professional development.
- Support policy and practice decisions: The results of secondary research can support policy and practice decisions in the research field. The information and analytical concepts can be used to recommend improvements and changes in practice and policy.
- Save time and resources: Instead of conducting a new study from scratch, secondary research leverages data and results that are already available. This saves time and resources, while quickly delivering important knowledge and information. Figure 14 :Secondary research
- Encourage further research: Secondary research awakens interest and encourages further research in the field of study. It provides a basis for identifying new directions and issues that need to be explored in more depth. Disadvantages of Secondary research:
- Limited newness: Secondary research focuses on synthesizing and analyzing existing data from different sources. Therefore, it does not generate new data or be groundbreaking in discovery. Secondary research may be limited in providing new and innovative information in the field of research.
- Data quality risks: Secondary research depends on the quality and reliability of existing data sources. If data sources are inaccurate, incomplete, or inappropriate, the results of secondary research may be affected. It is important when analyzing published studies, as there can be discrepancies and inconsistencies between different studies.
- Risk of unfairness and bias: The process of selecting and analyzing data in secondary research can be affected by the bias and bias of the researcher. Subjective choices, a focus on limited data sources, or a neglect of other perspectives can lead to inequity and bias in research results.
- Limitations in updating information: Secondary research is based on information available at the time of research.
- Difficulty in synthesizing and analyzing data: Secondary research requires a complex process of synthesizing and analyzing data from many different sources.
- Cannot replace original research: Although secondary research has an important role in synthesizing and analyzing available information, it cannot completely replace original research.
3. Primary Research on the Impact of Applying Big Data Technologies on Operational
Efficiency in Various Academic, Scientific, and Economic
- Deep understanding of students and teachers: Primary research allows educators and researchers to deeply understand the experiences, perspectives, and realities of students and teachers.
- Build new knowledge: Primary research can generate new knowledge about various aspects of education. It helps expand understanding of the relationship between students, teachers, parents and the education system in general.
- Orienting education policy: Results from primary research can provide important information to guide education policy.
- Generate data and evidence: Primary research provides data and evidence to identify and substantiate phenomena in the field of education.
