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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 24/2/2024 Date Received 1st submission Re-submission Date Date Received 2nd submission Student Name Nguyễn Vũ Cường Student ID BH Class SE06302 Assessor name Lê Văn Thuận 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 Cuong Grading grid P1 P2 P3 P4 M1 M2 D
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A. Introduction
Big data is a combination of structured, semi-structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications. Systems that process and store big data have become a common component of data management architectures in organizations, combined with tools that support big data analytics uses. Big data is often characterized by the three V's: the large volume of data in many environments; the wide variety of data types frequently stored in big data systems; and the velocity at which much of the data is generated, collected and processed. These characteristics were first identified in 2001 by Doug Laney, then an analyst at consulting firm Meta Group Inc.; Gartner further popularized them after it acquired Meta Group in 2005. More recently, several other V's have been added to different descriptions of big data, including veracity, value and variability. Although big data doesn't equate to any specific volume of data, big data deployments often involve terabytes, petabytes and even exabytes of data created and collected over time.
B. Content
I. Project Management Plan:
A project management plan is a comprehensive document that outlines how a project will be executed, monitored, controlled and closed. For project managers and their teams, it's the ultimate toolkit for achieving their objectives while managing day-to-day pressures such as time, cost, scope, resourcing and risk. This guide outlines what a project management plan is used for, why it's important, and offers a step-by-step guide on how to make one that actually works.
II. Project Charter:
A project charter is a preliminary statement that includes the goals, scope, and responsibilities of your project with the aim of gaining approval from key stakeholders. We recommend that you briefly explain the main points of the project before starting the actual work. This will ensure the project is approved or adjusted if necessary before moving on to developing other, more complete project planning documents. The project charter is one of many project planning documents to have in your toolbox. Let's see together what makes it different from other documents of the same type.
III. Project Objectives:
The goal of a project corresponds to what you hope to achieve or achieve at the end of the project. These can be products or resources, but can also be more intangible goals, such as increasing productivity or motivation. These goals must be attainable, timely and specific, and you must be able to evaluate them at the end of the project. Project goals are of great importance in project management: without them, there is no way to succinctly communicate what you are trying to accomplish before and during the project or to evaluate its success. judgment when it ends.
IV. Project scope:
Project scope is the part of project planning that involves determining and documenting a list of specific project goals, deliverables, tasks, costs and deadlines. The documentation of a project's scope is called a scope statement or terms of reference. It explains the boundaries of the project, establishes responsibilities for each team member and sets up procedures for how completed work will be verified and approved.
P1 Demonstrate qualitative and quantitative research methods to
generate relevant primary data for an identified theme.
Result : I used the survey method to collect data. I chose Google Form to create a survey. Question:
a. Quantitative data analysis: Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions. Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers. Using inferential statistics, you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter.
- Advantages and Disadvantages: Advantages of quantitative research: Quantitative research is often used to standardize data collection and generalize findings. Strengths of this approach include: Replication Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts. Direct comparisons of results The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically. Large samples Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis. Hypothesis testing Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion. Disadvantages of quantitative research: Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include: Superficiality Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research. Narrow focus Predetermined variables and measurement procedures can mean that you ignore other relevant observations. Structural bias Despite standardized procedures, structural biases can still affect quantitative research. Missing data, imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions. Lack of context Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.
- Qualitative research: Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or
values. Quantitative analysis helps in evaluating performance, assessing financial instruments, and making predictions. It encompasses three main techniques of measuring data: regression analysis, linear programming, and data mining. Advantages and Disadvantages: Advantages of qualitative research
- Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for: Flexibility: The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand. Natural settings: Data collection occurs in real-world contexts or in naturalistic ways. Meaningful insights: Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products. Generation of new ideas: Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise. Disadvantages of qualitative research:
- Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from: Unreliability: The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data. Subjectivity: Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated. The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly. Limited generalizability: Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population. Labor-intensive: Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.
- Methodology for Combining Qualitative and Quantitative Research to Investigate a Specific Theme: Combining qualitative and quantitative research methodologies can provide a comprehensive and nuanced understanding of a specific theme. This approach, known as mixed methods research, allows researchers to leverage the strengths of both qualitative and quantitative data to gain deeper insights. Here's a general methodology for combining qualitative and quantitative research: Define the research question: Clearly articulate the specific theme or research question you want to investigate. This will guide the overall research process. Determine the overall design: Decide on the overall design of your study. Will it be sequential, concurrent, or convergent? In sequential designs, one method is conducted before the other, while in concurrent designs, both methods are conducted simultaneously. Convergent designs involve separate qualitative and quantitative data collection and analysis, followed by the integration of findings.
Select your sample: Determine the sampling strategy for both qualitative and quantitative components of your study. Consider how the samples will complement each other and provide a comprehensive understanding of the research question. Data collection - Qualitative phase: Conduct qualitative data collection first. This could involve methods such as interviews, focus groups, observations, or document analysis. Ensure that your qualitative data collection methods align with the research question and provide rich, in-depth insights. Data analysis - Qualitative phase: Analyze the qualitative data using established qualitative research methods such as thematic analysis, content analysis, or grounded theory. Identify key themes, patterns, and relationships within the data. Data collection - Quantitative phase: After completing the qualitative phase, collect quantitative data. This could involve surveys, experiments, or other quantitative research methods. Design your quantitative instruments to capture specific variables related to the research question. Data analysis - Quantitative phase: Analyze the quantitative data using appropriate statistical techniques. This may include descriptive statistics, inferential statistics, or multivariate analyses. Generate numerical results that provide empirical evidence and statistical significance. Integration of findings: Once you have analyzed both qualitative and quantitative data, integrate the findings. Look for areas of convergence or divergence between the two data sets. Determine how the qualitative insights can help explain or contextualize the quantitative results, and vice versa. Interpretation and discussion: Interpret the integrated findings within the context of your research question. Discuss the implications and broader significance of the results. Address any limitations or challenges encountered during the research process. Conclusion and dissemination: Summarize the key findings and draw conclusions based on the integrated analysis. Consider how the mixed methods approach has contributed to a deeper understanding of the research question. Finally, disseminate your findings through academic publications, conferences, or other appropriate channels.
P2: Examine secondary sources to collect relevant secondary
data and information for an identified theme.
- Primary research: Primary research is data which is obtained first-hand. This means that the researcher conducts the research themselves or commissions the data to be collected on their behalf. Primary research means going directly to the source, rather than relying on pre-existing data samples.
teaching and learning processes. Overview of operational efficiency and its importance in educational institutions: Operational efficiency in educational institutions refers to achieving good results and using resources optimally. Applying Big Data technology can provide many benefits to educational institutions, including:
- Track and evaluate progress: Big Data technology allows tracking student progress and evaluating the effectiveness of teaching and curriculum measures. This helps teachers and administrators make adjustments and improvements to maximize learning effectiveness.
- Personalized learning: Big data can be used to create personalized learning models, providing students with instructional materials, exercises, and feedback tailored to their needs and abilities. each student. This helps improve student engagement and learning motivation.
- Predict and analyze trends: By analyzing big data, educational institutions can identify trends and learning patterns. This helps predict aspects such as graduation rates, program success, and educational trends. This information supports strategic decision making and quality management in education. Purpose of the study and research objectives: The purpose of this study isThe purpose of research on Big Data technology in an academic context may include:
- Improve understanding of the application of Big Data technology in the field of education: An important goal of the research is to increase understanding of how Big Data technology can be applied to improve the teaching process and study. Research may focus on analyzing the methods, tools and techniques of Big Data technology and applying them to specific educational situations.
- Shaping big data analytics methods and tools in education: Research could focus on developing and evaluating big data analytics methods and tools for the education sector. The goal is to identify effective methods for mining information from big data in education and apply them to make smart decisions and optimize the teaching and learning process.
- Research on the impact of Big Data technology on educational management: Research can focus on evaluating the impact of Big Data technology on educational management, including optimizing the process management process, predict trends and discover important information from educational data. The goal is to provide methods and frameworks for using Big Data technology to improve educational management and strategic decision making.
- Assessing the impact of Big Data technology on students and teachers: Research can focus on assessing the impact of Big Data technology on students and teachers. The goal is to identify the benefits and challenges of applying Big Data technology in the learning and teaching process, as well as learn about how to take advantage of this technology to improve the quality of education. This is a research article on the field of applying Big Data in academic libraries: literature review and future research directions. In the article, people studied three different stages. In there:
- The first phase consisted of searching for studies related to Big Data from four selected databases.
- The second stage involves classifying these works in different fields of knowledge according to the research questions.
- Finally, the third phase involves a detailed document review report. For this review, four online academic research databases, namely Scopus, EBSCO, Springer Link and ScienceDirect, were examined for relevant documents. Benefits of Big Data technology for operational efficiency: Data-Driven Decisions: How Big Data Technology Helps Educational Institutions Make Data- Driven Decisions and Improve Operational Efficiency Resource Optimization: How Big Data analytics helps optimize resource allocation, leading to cost savings and improved operational efficiency across academic fields Personalized learning and student support: How Big Data technology can facilitate personalized educational experiences and student support systems, leading to improved learning outcomes and retention rates. maintain students Efficient Administrative Processes: How Big Data Applications Can Automate Administrative Tasks, Simplify Workflows, and Improve Performance in Educational Institutions. Conclusion: By conducting a thorough review of existing literature, studies and reports, this secondary research provides a detailed insight into the impact of Big Data technology on operational efficiency in the academic field. It highlights the benefits, challenges and considerations associated with the application of Big Data technology and provides recommendations for educational institutions looking to leverage this technology to improve operational processes. his movements. Advantages and disadvantages of primary research Primary research has many advantages, although it is not the most appropriate type of research for every situation. It is important to consider the individual requirements before deciding on the most appropriate research method for the situation. Advantages of primary research Primary research methods offer a targeted approach to market research. This allows specific issues to be addressed, keeping the research completely relevant to the objectives and scope of the project. This means that the research is specific to the individual market, rather than the mass market. This type of research also allows the marketer to have complete control over the methodology used, along with the representative sample size and the sample selection process. This helps to further improve the relevancy of the research to the person or organisation. Secondary research is often outdated and may no longer be accurate for the market the researcher is trying to target. Primary research guarantees that the information collected is up-to-date and relevant, enabling accurate trends to be revealed. Primary research also allows the person or organisation to control ownership of the data. They may choose to release the information to enhance their position as an authority in the field, or they may choose to keep the data private to avoid giving competitors an advantage. Disadvantages of primary research
about the effectiveness of primary research. Hence, organizations or businesses can form a hypothesis and evaluate cost of conducting primary research.
- Secondary research is quicker to conduct because of the availability of data. It can be completed within a few weeks depending on the objective of businesses or scale of data needed. As we can see, this research is the process of analyzing data already collected by someone else, and it can offer a number of benefits to researchers. Disadvantages of Secondary Research On the other hand, we have some disadvantages that come with doing secondary research. Some of the most notorious are the following:
- Although data is readily available, credibility evaluation must be performed to understand the authenticity of the information available.
- Not all secondary data resources offer the latest reports and statistics. Even when the data is accurate, it may not be updated enough to accommodate recent timelines.
- Secondary research derives its conclusion from collective primary research data. The success of your research will depend, to a greater extent, on the quality of research already conducted by primary research. Key Differences between Primary Research and Secondary Research Understanding the distinction between primary research and secondary research is essential in determining which research method is best for your project. These are the two main types of research methods, each with advantages and disadvantages. In this section, we will explore the critical differences between the two and when it is appropriate to use them.
Primary Research Secondary Research
Research is conducted first hand
to obtain data. Researcher “owns”
the data collected.
Research is based on data
collected from previous
researches.
Primary research is based on raw Secondary research is based on
tried and tested data which is
data. previously analyzed and filtered.
The data collected fits the needs
of a researcher, it is customized.
Data is collected based on the
absolute needs of organizations or
businesses.
Data may or may not be according
to the requirement of a
researcher.
Researcher is deeply involved in
research to collect data in primary
research.
As opposed to primary research,
secondary research is fast and
easy. It aims at gaining a broader
understanding of subject matter.
Primary research is an expensive
process and consumes a lot of
time to collect and analyze data.
Secondary research is a quick
process as data is already
available. Researcher should know
where to explore to get most
appropriate data.
P3 Discuss the features and operational areas of a businesses in an identified sector.
- Project Logbook: Project Logbook: The Impact of the Application of Big Data Technologies in Operational Efficiency in Academic Areas Project Title: The Impact of the Application of Big Data Technologies in Operational Efficiency in a Range of Academic Areas Project Start Date: [2/10/2023] Project End Date: [5/11/2023] Project Goal: The goal of this project is to investigate and analyze the impact of applying Big Data technologies
