Instructions:
The assignment consists of an excel file with 2 tables Sales and returns. Leverage those tables to
answer the following questions (also mentioned in 1st tab – Questions) of the Excel
1. What % of sales result in a return?
2. What % of returns are full returns?
3. What is the average return % amount (return % of original sale)?
4. What % of returns occur within 7 days of the original sale?
5. What is the average number of days for a return to occur?
6. Using this data set, how would you approach and answer the question, who is our most
valuable customer?
The data is self-explanatory and in case of any doubts, please feel free to make rational assumptions.
We expect the SQL code as well as the Excel for the same in a presentable and well formatted
manner.
Category: Data science
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“Analyzing Sales and Returns Data: Identifying Trends and Valuable Customers”
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“Exploring the World of Data Science: From Topic Selection to Publication”
I would need assistance to write a paper on Data science including topic selection.
That should evenituallu be published. -
Title: “Exploring Sales Data for a Retail Company: A Story of Growth and Opportunities” PDF file of draft dashboard: Attached One page write-up: Introduction: The retail industry is highly competitive and constantly evolving, making it crucial for companies
Your task is to do initial analysis on your data in Tableau. Create some visuals, considering how you might tell a story with this data. Create at least four visualizations. Only one may be a table. Create a draft dashboard with these visuals. This work needs to be done in Taableau software. You must be able to use this software and provide me the file that is in Tableau. Business Intelligence and Analytics Software | Tableau
Submit three things:
A pdf file of your draft dashboard
A one page write-up (use Word) explaining how you your dashboard tells a story
The link to your Tableau Public dashboard -
Title: Bridging the Gap: Exploring Key Areas for Advancement in mHealth Research and Development
For each research gap, please answer the following questions below. For each research gap, use two scholarly resources. Total of 10 resources.
Identified Research Gaps:
Integration of User Feedback in Development:
While the papers discuss evaluation tools and user perceptions, there is a potential gap in how user feedback is integrated into the continuous development and improvement of mHealth applications. There’s a need for research on adaptive frameworks that incorporate real-time user data to refine app functionalities.
Specific Health Outcome Measurements:
The reviewed papers focus on usability and general evaluation frameworks without a deep dive into how these mHealth tools affect specific health outcomes. Research could be expanded to link usability and user perceptions directly to health outcome improvements, especially in chronic disease management beyond cardiovascular health.
Cross-cultural and Economic Impact:
The papers provide insights into using mHealth in specific regions (e.g., Indonesia) or general settings. There’s a research opportunity to examine the cross-cultural applicability of these tools and their economic impact on different health systems globally.
Longitudinal and Comparative Studies:
Most studies focus on immediate or short-term evaluations. Longitudinal studies that track the effectiveness and user engagement over longer periods can provide insights into the sustainability and long-term impacts of mHealth applications.
Security and Privacy in mHealth Applications:
Given the sensitivity of health data, research focusing on security and privacy aspects of mHealth applications is crucial. This includes studies on user trust, data protection measures, and regulatory compliance across different regions.
Data Acquisition
How and where can we obtain the data necessary to address these gaps?
What are potential data sources that could provide the required information?
Methodology
What methods do you think could be used to address the research gaps?
Which methodologies are most suitable given the nature of the data and research questions?
Theoretical Frameworks
What theoretical frameworks can be applied to each identified research gap?
How can these frameworks guide the research process and analysis?
Data Collection
Considering the difficulty in finding data sources, what realistic data collection methods can be employed?
What specific populations can serve as target populations for each research gap? -
“Maximizing ROI on Sponsored Search Ads: A Case Analysis and Recommendations”
Case Questions for Measuring ROI on Sponsored Search Ads
Instructions:
This exercise is to be completed individually. You may discuss the assignment in a group, but
each person should complete the exercise separately.
Please provide a clear, concise, and well organized essay that addresses at least the following
questions. You are free to address other issues in the case as well. The intent of the assignment
is to have you think critically about the marketing problems faced in the case. It is important
that you consider both sides of the argument when you answer these questions. Analyze the
quantitative material, if any, in the case to support your answers. Spend most of your time in
defining and defending your recommendation for what should be done.
Good answers may require assumptions of facts that may not be presented in the case. You
are welcome to make these assumptions, but please state these assumptions and briefly justify
why that are reasonable. Also, you may use whatever resources you can locate to provide
further information about this industry or the web in general. Please reference your sources.
Your response must be typed, double spaced, with one-inch margins, and a 10 to 12 point
font size. This writeup must not exceed 3 pages in length. You may attach exhibits, tables,
and/or graphs to support your arguments. These supporting materials must be referenced in
the text and do not count toward the 3 page limit.
Required:
e-Marketing focuses on making marketing more “accountable”. In other words, we want to use
quantitative analysis to direct our marketing expenditures and to get the best possible return from
these investments. A common metric for doing this is Return on Investment (ROI). ROI is the ratio
of return (=Return – Investment) to Investment. In our problem the investment is the cost of the
advertising, and the return is the profits earned from the advertisement. Please read the case
“Measuring ROI on Sponsored Search Ads” and answer the following questions.
1. What would happen if sponsored ads are removed from the website? How many would
click through to the website anyways?
2. Repeat Bob’s ROI calculation of 320% for sponsored search advertising for branded
keywords on Google. What assumptions is he making? Are these assumptions possibly
incorrect? Please explain your reasons.
3. What analysis can you do from the data to get a better ROI calculation for sponsored search
advertising for branded keywords on Google? Please explain any assumptions you make
based on the discussion in the case, conduct the proposed analysis and report the ROI you
have calculated. (Hint: Consider looking for a natural experiment that would tell you what
would happen if you did not invest in the advertisements.) -
“Designing a Conceptual Data Model for a Non-Profit Organization: A Case Study with Oakmont & Partners LP” Title: Initial Data Model for Refugee Intake and Assistance Program
https://lucid.app/documents#/documents?folder_id=recent
Motivation
Start by reading the entire assignment and making a note in your calendar for the due date and the late submission window; set reminders. Block uninterrupted time slots of at least 45 minutes at a time to work on the assignment. Work on the assignment daily after working through the lessons.
In the context of database design, the conceptual data model stands as a foundational pillar, playing a critical role in shaping the structure and future performance of the database. It serves as a blueprint, guiding developers and stakeholders through the complex landscape of data requirements and relationships. This model, often abstract and technology-agnostic, lays down the groundwork for more detailed and specific design stages, ensuring that the database aligns with the organizational goals and user needs.
The importance of conceptual data modeling cannot be overstated; it acts as a communication tool that brings together different stakeholders, including business analysts, developers, and end-users, facilitating a common understanding of the data requirements. This collaborative approach ensures that the database is not only technically sound but also aligns with business objectives and user expectations. Furthermore, a well-designed conceptual model can significantly reduce complexity in later stages of database design, leading to more efficient implementation and easier maintenance. By addressing data inconsistencies and redundancies at an early stage, it also enhances data quality and integrity, which are crucial for reliable decision-making and operational efficiency. In essence, conceptual data modeling is not just a preliminary step; it is the strategic planning phase that determines the success and adaptability of the database system in a rapidly evolving data-driven world.
The conceptual data model is often expressed visually in a UML Class Diagram — it is database-agnostic and can serve as the foundation for relational or non-relational database designs. On the other hand, the logical model is geared towards the implementation of the conceptual data model in a relational database. While UML Class Diagrams can be refined towards a relational implemenation, it is common to express relational designs in an entity-relational diagram (ERD). There are several common ERD notations in use today – the choice often depends on available tools. In this assignment, we will use the common Information Engineering (IE) notation, which is also often known as the “Crow’s Feet” notation because of the way the multiplicity indicators looks like the feet of crows (a kind of bird).
Format
While you may collaborate with others in the class you must submit your own model.
Case Background
Oakmont & Partners LP has been busy building data models for a number of new clients and Monica is excited to start a new modeling effort for a local non-profit that provides assistance to homeless and others in need. She is keen on applying her UML and ERD modeling skills that she acquired while taking a corporate training class taught by Ars Doceo. She knows that the first step in building a conceptual data model is to conduct requirements analysis. So, she sets up an interview with Kaileen Ormond, the Director of Social Pedagogy who has been with the organization for over 18 years. To make sure she remembers what is being said, she decides to record the interview. The following is a transcription of part of that recording:
“… Let me give you a scenario. So, last month we took in 132 new homeless individuals and families. After we register them and determine their eligibility to shelters, we assign them to a bed in a common dormitory room in a shelter — if they are by themselves — or to a small family room if they are a family of five or fewer. If they are larger, then we generally cannot help them and we refer them to social services. Once we assign them to temporary housing (we need to record the date of the intake, as they can only stay a maximum of six months), we have to see if they can qualify for any government assistance programs such as WIC. Those programs are only accessible to US Citizens or permanent residents; they are not open to asylum seekers or anyone who is undocumented or is on a non-immigrant visa.”
Monica thinks she has enough to develop an initial data model but is reassigned to a new project where her skills in agile business analysis are needed. So, you are being asked to jump in and build an initial data model. Your task is to develop a conceptual and then a logical data model for the entities and relationships within the context of the above requirements, along with a full definition of all entities. Be sure to list all your assumptions used in the construction of the data models.
Problem 1 (60 Pts): Conceptual Data Model in UML
Express the data model in a UML Class Diagram using LucidChartLinks to an external site.. Label the relationships where useful. Use directionality indicators on the labels (▲▼▶◀). Add key attributes as appropriate with the stereotype «key».
To narrow and focus the scope of the model, consider only the specific requirements below. Those are the ones that the conceptual data model expressed in UML must support — you may omit any other considerations as this is clearly is very large project. The likely implementation will be a small application, perhaps a web app and this data model will help inform the database design and the user interface.
track the names and key demographic information (birthday, country of birth, citizenship or visa status) of all individuals registered
track immediate familial relationships, e.g., children (son, daughter), parents, grandparents
track the shelter to which they are assigned and the type of housing (bed or family room), including address
know when they were registered and when their permit to reside in-country expires
track eligibility for government assistance: while you do not need to address this, how would you manage eligibility for different programs, e.g., a person might be eligible for one program but not for another
legal representative(s) hired by or assigned to them
If there are unresolved questions from the notes, post your question on Teams and incorporate the new findings into your model. You may discuss the problem and share insights with your peers in the class but you must build and submit your own model. Keep your model to about 6-8 classes/entities, although you may have more as long as it is warranted, but we do not want you to “overmodel”. Make reasonable assumptions when the requirements are fully specified, document your assumptions in your model, and then build your UML model accordingly.
Problem 2 (40 Pts): Logical Data Model as ERD
After you have built your conceptual data model as a UML Class Diagram, create (in a separate page/tab), an Entity-Relationship Digram (ERD) in the IE (Crow’s Feet) Notation of the same entities, i.e., “translate” the UML to and ERD. This may not always be done in practice, but we want you to practice using both notations.
Time box your work to the allotted time of about 3 hours. If you spend substantially more than 3 hours then you are overthinking the problem. This is a large problem and we do not expect that you build a full domain data model — we want you to get started and pay only attention to the use cases. Of course, if you do want to explore the problem further, you may, but you will not get additiona points or “extra credit”.
Submission Details
Submit a public URL to your Conceptual Model as a UML Class Diagram and your Logical Model as an IE (Crow’s Feet) ERD, along with (in separate “tabs”) any notes and assumptions. You must use LucidChartLinks to an external site. to create the diagrams and track your notes in LucidChart.
https://lucid.app/documents#/documents?folder_id=recent -
“Exploring the Impact of Current and Emerging Trends on Organizations: An Ecological Perspective on Data Collection, Analysis, and Problem-Solving”
1. (Critically) evaluate the impact of current and emerging trends on
organizations.
2. Express mastery the ecological approach and field work data collection
processes.
3. Articulate the need to collect data and manage data from an ecological
perspective in order to solve problems in society.
4. Demonstrate an ability to effectively analyse, visualise problems and issues
employing a range of appropriate concepts, theories and approaches relevant to
human needs.
5. Establish and articulate the data quality process, where the problems come
from and how they can be resolved.
6. Apply tools and techniques of strategic and operations analysis on how
technology addresses human needs.
7. Develop succinct business reports. -
“Database Design and Implementation Report”
Please check all the files.
Guidance and Presentation
For Part A students are expected to write up their answers as a report. The report should look
professional and provide all the necessary information for Part A.
• Maximum 15 pages including diagrams and images.
• Additional diagrams and images should go into an appendix.
For more information you should see the rubric provided at the end of this document.
Submission Requirements
• Students are required to submit a word/pdf document that contains answers to part A.
• Students should also submit their Microsoft Access implementation for Part B.
• These should all be submitted to the submission point on Course Resources before the
deadline. -
“Creating a Professional Resume: A Step-by-Step Guide”
Please see attached instruction/template. If you have any questions, please do not hesitate to ask. Thank you!
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“Exploring the Relationship between Data Science and Advanced Quantitative Methods in Psychology” Guideline: 1. Introduction – Briefly introduce the topic of data science and its relevance to psychology – Explain the importance of using advanced quantitative methods in psychological research
it is Advanced Quantiteve Methods in Psychology, i couldn’t find this subject and data science is closest one to it.
i need to ask to go through guiedeline attached, check if you really understand what to do, i’ve done this before myself, i normally always do myself, but this time i failed, and it’s my last chance and i need it to be done perfectly. if needed i can also share comment on previous work, so you can see, what shouldn’t be done. all needed attachments below.