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A mock exam paper needs realignment based on the guided file that is attached One question needs to be answered on 2022 paper Please follow the guide that is included in the attached file and include
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Data Analysis
Students Name
Institution Affiliation
Instructor’s Name
Course Name
Date
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Data Analysis
Question 1:
As a research assistant for the FinTech firm, my main task is to develop a data
analysis strategy that focuses on comprehending and assessing the possible benefits and
hazards related to their objective of offering automated financial advice to disadvantaged
communities. The first phase is establishing explicit goals in cooperation with the founders.
After that, the data collection process revolved around particular financial information,
demographic information, and market trend information of the target population of lowincome families (Kumar, 2023). Data cleaning and preparation, which include dealing with
missing values and outliers and adhering to data privacy standards, are critical elements in
ensuring dataset integrity. Statistical and visual tools are then applied to exploratory data
analysis (EDA) to find patterns and trends within the data.
Feature engineering is essential for creating relevant variables, such as income levels
or measures of financial literacy. The following development of the machine learning model
encompasses using regression, classification, and clustering models to forecast opportunities
and evaluate risks. Validation and assessment processes are used to ensure the correctness of
the models while also maintaining interpretability to explain the results effectively.
Scalability and integration deal with the possibility of many users, while a feedback loop
ensures ongoing improvement. We proactively address anticipated problems such as data
privacy, bias, user trust, and regulatory compliance to provide a complete and reliable data
analysis process.
Question 2
In the FinTech project designed to provide automated financial advice to
underprivileged communities, the quality and fairness of the study might be affected by
various biases in data collection. The presence of selection bias is of utmost importance since
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an unrepresentative sample may need to fully reflect the different financial requirements of
economically disadvantaged individuals. Therefore, it is essential to make a focused and
coordinated attempt to create a complete and representative dataset while addressing this
issue.
One way to accomplish this is by using stratified sampling techniques, which
systematically select people from various income categories. Confirmation bias increases the
likelihood of risk when the founders make preexisting assumptions about the financial habits
of the target demographic. To mitigate this issue, it is essential to cultivate an impartial and
receptive mindset while doing data analysis (Kelly, 2020). Blind data analysis approaches
may reduce human biases when interpreting data.
The need for precise measurements in financial analysis makes measurement bias
especially important. Implementing meticulous validation and calibration procedures for
measuring gear is crucial to reduce measurement mistakes and provide extensive training for
data collectors (Kumar, 2023). Utilizing several techniques for measuring might augment the
dependability of the gathered data. By actively acknowledging and confronting these biases,
analyzing data becomes more resilient, guaranteeing that the insights produced serve as a
more precise and impartial basis for the FinTech company’s financial advisory services.
Question 3
Performing an online poll on Facebook to assess the viewpoints of people from
impoverished households about the FinTech company concept presents several possible
prejudices. Sampling bias occurs because Facebook users may not accurately reflect the total
population, particularly those without internet access. However, it is advisable to vary the
techniques used to gather data by including offline procedures to alleviate this issue. This will
enable a more comprehensive variety of viewpoints to be captured. Response bias may arise
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when people with more pronounced ideas or experiences are more inclined to engage, leading
to a distortion in the dataset.
Strategies such as randomizing the sequence of surveys help address this issue,
ensuring a more equitable representation. The issue of digital divide prejudice is problematic
since it may result in excluding those with limited internet access or digital literacy. (Runkler,
2020). It is essential to actively recruit a wide range of participants to mitigate self-selection
bias, which occurs when individuals with specific interests are more likely to participate.
Additionally, privacy concerns may impact participants’ willingness to divulge private
financial information. These worries may be alleviated by highlighting strong privacy
safeguards in survey introductions, resulting in a more reliable and inclusive dataset (Kelly,
2020). It is crucial to have a comprehensive and varied strategy for collecting data on
Facebook while addressing any potential biases to provide accurate and dependable insights
into the perspectives of people from economically disadvantaged backgrounds.
Question 4
Within the FinTech project, various data analytics research approaches may be
explored to evaluate the benefits and drawbacks of offering automated financial advice.
Regression analysis provides a quantitative method for forecasting numerical results but may
oversimplify intricate connections (Hughes et al., 2021). Categorical predictions may be
effectively made using classification models; however, their interpretability might need to be
revised. Cluster analysis facilitates dividing customers into distinct groups based on their
similarities, uncovering concealed patterns. Time series analysis is crucial for comprehending
temporal interdependencies in financial data, but stationarity assumptions might affect its
accuracy.
Sentiment analysis is a method of assessing public opinion via social media, focusing
on the qualitative aspect. Ensemble approaches involve mixing models to improve forecast
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accuracy, providing resilience and increasing intricacy. The research inquiries and data
attributes should select the methodologies, and adopting a complete strategy that integrates
many approaches may result in more extensive insights. Regular validation and assessment
help minimize possible drawbacks, guaranteeing that the results are relevant and applicable
(Runkler, 2020). In the end, choosing research methodologies for data analytics involves
carefully considering the pros and cons of each strategy to get significant and practical
insights that align with the aims of the FinTech organization.
Question 5
To successfully deliver the three-month research and data analytics results to the
founders, it is essential to use a holistic method that combines qualitative and quantitative
insights. The presentation will begin with a concise executive summary, providing a brief
overview of the goals and approaches. Founders could actively study the data via interactive
dashboards that display critical metrics and trends. Visual storytelling, including charts,
timelines, and infographics, would effectively lead individuals through the research process,
promoting a distinct comprehension of the project narrative.
Comparative analysis using visuals and spatial analyses, such as heatmaps, would
effectively showcase trends and regional differences. According to Kumar (2023), visualizing
scenario studies and predictive models would allow the founders to anticipate future
outcomes based on many factors. Transparently conveying the quality and limits of the data
via visual indicators guarantees a nuanced comprehension of the research’s credibility. The
primary objective of this visual-focused method is to provide the founders with a thorough,
easily understandable, and practical comprehension of the research results. This will enable
them to make well-informed choices when molding the business plan of the FinTech firm.
Question 6
Research Question:
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How does social media data integration with strategic decision-making influence enterprise
performance and competitiveness?
Background information
The introduction of the current topic goes to the extent of how different social media
in contemporary society affect business. An introduction of historical information on the
latter and its application in real life gives an effective framework for making critical findings
in answering the research question. Arguably, this is connected with a database based on
correcting, storing, and retrieving for various manipulations. It would be worth noting
different data analysis applications, including Excel, Power BI, SPSS, and R-coding, as they
apply in the analysis and representation of small and big data. An introduction of relevant
websites for data collection is also effective in this area, purposely considering unique local
and international companies that save data online. For example, the introduction of Yahoo
and related sites would be effective in this case.
Research Design:
1.0 Literature Review:
1.1 Objective
To build a strong foundation by locating information on presently available evidence to
synthesize theories and previously done research predicting the influence of investment in
social media on business performance and competitiveness.
1.2 Methods
A Systematic Review of different academic literature available from published
journals, industry reports, and case studies relevant to the topic, mainly focusing on the
impact of social media data in strategic decision-making, will be employed for the current
exercise. This extends to an exploratory review of the theories related to information
processing, organizational learning, and competitive advantage. Arguably, reviews
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connecting social media with different business dynamics will apply. Close navigation of
scholarly articles related to decision-making and its influence on enterprise performance and
competitiveness is effective in this case. Also, the connection of different cases by the
company in reference to contemporary business operations can apply in this case. However, a
nuanced literature review then guides the development of a research design to identify gaps in
the knowledge of a specific area the researcher seeks to fill. In general, decision-making
influences enterprise performance and competitiveness with respect to social media are well
researched, which is a remedy for using many filters in searching the required materials.
2.0 Theoretical Framework Development:
2.1 Objective
To establish a theoretical foundation for the research, integrating insights from the literature
to guide the investigation into the relationship between social media data integration,
strategic decision-making, and business outcomes.
2.2 Methods
Synthesis of the concepts from literature and form a theoretical framework that should
identify critical variables, relationships, and potential moderating or mediating factors.
A well-crafted theoretical framework ensures coherence and provides a roadmap for both the
formulation of hypotheses and the structure of the research approach.
3.0 Research Approach:
3.1 Objective
To employ a mixed-methods approach to comprehensively understand how social media data
influences strategic decision-making and subsequent business outcomes.
3.2 Methods:
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Surveys: Develop a survey instrument that will quantitatively assess the level of
utilization of social media data, types of decisions influenced, and improvements in perceived
business performance. Target a diverse sample of businesses by industry.
In-Depth Interviews: To interview business executives about their business decisions of
interest, challenges faced, and success stories related to integrating social media data.
Combining surveys and interviews provides a holistic perspective, allowing for quantitative
measurement and qualitative depth, capturing the phenomenon’s complexity.
4.0 Data Analysis:
4.1 Objective
To analyze the collected data, discern patterns, and derive meaningful conclusions regarding
the impact of social media data on strategic decision-making.
4.2 Methods:
Quantitative Analysis: Apply regression analysis to expose relationships between meanings
of social media data uses and performance measurements using statistical methods.
Qualitative Analysis: Apply thematic analysis to recognize patterns, topics, and rantings from
the interview to understand how to make decisions.
Applying quantitative and qualitative analyses will enable a triangulated interpretation that
legitimizes the results and gives a more holistic view of the exploration.
5.0 Ethical Considerations:
5.1 Objective:
To ensure the research’s ethical conduct and protect participants’ rights and privacy.
5.2 Methods:
Informed Consent: Explain the purpose of the study and get informed consent from the
research participants.
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Confidentiality Measures: Take strict measures to keep the identity and response of those
participating a secret.
Ethical considerations are crucial to developing trust with the respondents and actualizing the
desired research integrity.
6.0 Limitations and Delimitations:
6.1 Objective:
To acknowledge potential constraints and define the scope of the research.
6.2 Methods:
Outline explicitly the limitations, such as generalizing findings to particular industries or
company sizes, and delimitations, such as focusing on the specific geographic region.
Recognition of limitations and delimitations ensures that the findings are realistically
interpreted, thereby setting expectations right for the study.
Data Analysis
This includes a close navigation of data collected via the approaches mentioned above. This
might include a connection between social media data and their integration with strategic
decision-making as they influence enterprise performance and competitiveness with respect
to different media spaces and data available.
Findings
This will give the results as per the data analysis with respect to the question, “Social media
data integration with strategic decision-making influence enterprise performance and
competitiveness.” This is the part that gives the climax of the review and method used in the
investigation of the very issue.
7.0. Conclusion
This detailed research design aims to critically contribute to understanding the impact of
social media data on strategic decision-making over business performance and
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competitiveness. The above-highlighted methods with the dual approach of analysis applied
for quantitative and qualitative methods are strategically chosen to address the detailed
complexities of the researched question.
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References
Hughes, A. C., Orr, M. C., Ma, K., Costello, M. J., Waller, J., Provoost, P., … & Qiao, H.
(2021). Sampling biases shape our view of the natural world. Ecography, 44(9), 12591269.
Runkler, T. A. (2020). Data analytics. Wiesbaden: Springer Fachmedien Wiesbaden.
Kelly, K. (2020, May 27). What is Data Analysis: The Essential Guide. Simplilearn.com.
https://www.simplilearn.com/data-analysis-methods-process-typesarticle#:~:text=Answering%20the%20question%20%E2%80%9Cwhat%20is
Kumar, P. (2023, January 31). Data Analysis, Interpretation, and Presentation Techniques: A
Guide to Making Sense of Your Research Data – hmhub. HM HUB.
https://hmhub.in/data-analysis-interpretation-and-presentation-techniques/
Section B – Candidates should answer the question. Your answer to this question should be around
1000-1500 words.
Question 6
The phenomenon: In recent years, firms have communicated an increased awareness of their social
purpose in society via advertisements, announcements, annual reports and on their websites. This trend
has been closely preceded by more than a decade of corporate social responsibility (CSR)
communications on similar outlets. As new firms develop a presence in the metaverse – a digital 3-D virtual
market – firms need to identify whether they should engage in more social purpose driven narratives or
corporate social responsibility narratives or both.
Your Tasks: Clearly define one research question about this phenomenon from the business
perspective, and then outline a detailed research design to answer the question.
Your answers should focus on your research plan but without actually carrying out the research. Your
answers should cover all stages and the entire process of research design with adequate justifications and
explanations. In particular, you should clearly define and justify one research question from the business
perspective in relation to this phenomenon; and then explain the reasons behind your choice of appropriate
methods for literature review, data collection and data analysis in order to answer your research question.
The research methods you choose can be inductive or deductive, qualitative or quantitative, depending on
the nature of your research question. You should explain why a particular method has been chosen and
its strengths and weaknesses in relation to your research question.
(50 marks)
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