ICT205 Data Analytics Assessment 1 – Case Study Report Overview A data analytics project starts with collecting the data and ends with communicating the results from the data. In between, there are...

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ICT205 Data Analytics Assessment 1 – Case Study Report Overview A data analytics project starts with collecting the data and ends with communicating the results from the data. In between, there are multiple steps that are required to be followed- data preprocessing is one of the most important steps among them. The data preprocessing step itself has multiple steps depending on the nature, type, value etc. of the data. On the other hand, data visualisation uses visual representations to explore, make sense of, and communicate data that often includes charts, graphs, illustrations etc. Today, there is a move towards visualisation that can be observed among many big companies. Timelines and Expectations Students are expected to work individually to prepare a report that details the use and applications of data preprocessing and data visualisation techniques on a selected data set. The aim of this assessment is to enable students to create a report that evaluates the use of data preprocessing and data visualisation techniques applied to a given case. Students are required to select a data set and answer the following questions: - What is the purpose of the data set, and what kind of insights can be extracted from the chosen data set? - Have you applied any data cleaning approaches (e.g., missing value handling, noisy data handling) for the chosen data set? Explain in your own words what data cleaning approaches you have perform or why it was not required. - Have you applied any data transformation techniques (normalisation, attribute creation, discretisation etc.) for the chosen data set? What data transformation techniques you have performed or why it was not required to perform any transformation? Explain in your own words. - Have you applied any data reduction techniques (reduce dimension, reduce volume, balance data) ?If yes, then describe the data transformation technique(s) you have followed; otherwise, explain why no transformation techniques were not required. - Design an interactive dashboard using 3-4 charts/graphs/illustrations to represent the data. Case Study Report (20%) Individual Report Due (20th August 2021 Week 6 Friday 11:59pm) Expected word count 1,500 words Students are expected to submit their assessments via Turnitin on Moodle. Minimum time expectation: 15 hrs Learning Outcomes Assessed The following course learning outcomes are assessed by completing this assessment task: LO1. review and differentiate between the methods of data analysis and presentation; LO2. analyse internal and external sources of data relevant to business environments including TEQSA: PRV14311 CRICOS: 03836J Australia Advance Education Group Pty Ltd. trading as Sydney International School of Technology and Commerce ABN 74 613 055 440 |ACN 613 055 440 Level 14/233 Castlereagh Street, Sydney NSW 2000 P a g e | 2 technology and service utilisation data to identify relationships and trends; LO3. develop and apply skills in spreadsheets to sort, manage, summarise and display data to support managerial decision-making; Assessment Details For this assignment, students are required to write 1,500 words report on a specific case study and explain the use and applications of data preprocessing and data visualisation techniques on a selected data set. Students can choose any suitable data set that is publicly available on the internet. In week 6, students will be required to submit their report on moodle. Students are expected to work individually and undergo their own research without collaboration with any other student. Students are expected to prepare a comprehensive report on the application of their knowledge of data preprocessing and visualisation on a given case study. 1. All reports must include at least 5 academic references which must be done using APA7 reference style. 2. The case study must assess the value propositions of the chosen data set and discuss what types of business questions can be answered using the data set. It must highlight the suitability of data cleaning approachesfor the selected data set. It must highlight the data transformation techniques that are applicable to the data set. Students must also highlight how an interactive dashboard can be designed for the chosen data set to communicate the data effectively. 3. This unit requires you to use APA system of referencing. See Sydney International’s quick reference guide. It should be used in conjunction with the online tool Academic Writer: https://extras.apa.org/apastyle/basics-7e/#/. 4. A passing grade will be awarded to assignments adequately addressing all assessment criteria. Higher grades require better quality and more effort. For example, a minimum is set on the wider reading required. A student reading vastly more than this minimum will be better prepared to discuss the issues in depth and consequently their report is likely to be of a higher quality. So before submitting, please read through the assessment criteria very carefully. Submission All assessments must be submitted through Turnitin on Moodle. Marking Criteria / Rubric Refer to the attached marking guide. Feedback Feedback will be supplied through Moodle. Authoritative results will be published on Moodle. Academic Misconduct To submit your assessment task, you must indicate that you have read and understood, and comply with, the Sydney International School of Technology and Commerce Academic Integrity and Student Plagiarism policies and procedures. TEQSA: PRV14311 CRICOS: 03836J Australia Advance Education Group Pty Ltd. trading as Sydney International School of Technology and Commerce ABN 74 613 055 440 |ACN 613 055 440 Level 14/233 Castlereagh Street, Sydney NSW 2000 P a g e | 3 You must also agree that your work has not been outsourced and is entirely your own except where work quoted is duly acknowledged. Additionally, you must agree that your work has not been submitted for assessment in any other course or program. Individual report sample structure - Coversheet (mandatory) - Title page - Table of content 1. Introduction 2. Overview of the data 3. Data Preprocessing a. Data Cleaning b. Data Transformation c. Data Reduction 4. Dashboard Design 5. Conclusions 6. References 7. Appendix Note: Students are allowed in include other sections as they deem necessary based on their case study. Sample data set for case study: Absenteeism at work Data Set Bank Marketing Data Set Iranian Churn Dataset Data Set Productivity Prediction of Garment Employees Data Set Real estate valuation data set Data Set Apartment for rent classified Data Set Chronic_Kidney_Disease Data Set TEQSA: PRV14311 CRICOS: 03836J Australia Advance Education Group Pty Ltd. trading as Sydney International School of Technology and Commerce ABN 74 613 055 440 |ACN 613 055 440 Level 14/233 Castlereagh Street, Sydney NSW 2000 P a g e | 4 Case Study Report Marking Guide – Marks 100 Weighting: 20% Student IDs: Assessment Criteria: Score Very Good Good Satisfactory Unsatisfactory Presentation Information is well Information is Information is somewhat Information is somewhat /Layout organised, well written, organised, well written, organised, proper organised, but proper and proper grammar with proper grammar grammar and grammar and and punctuation are and punctuation. punctuation mostly punctuation not always used throughout. Correct layout used. used. Correct layout used. Some elements of /05 marks Correct layout used. used. layout incorrect. Structure Structure guidelines Structure guidelines Structure guidelines Some elements of Enhanced followed exactly mostly followed. structure omitted /05 marks Introduction Introduces the topic of Introduces the topic of Satisfactorily introduces Introduces the topic of the report in an the report in an the topic of the report. the report, but omits a extremely engaging engaging manner which Gives a general general background of manner which arouses arouses the reader's background. the topic and/or the the reader's interest. interest. Indicates the overall overall "plan" of the Gives a detailed general Gives some general "plan" of the paper. paper. background and background and indicates the overall indicates the overall /10 marks "plan" of the paper. "plan" of the paper. Details All topics are discussed in Consistently detailed A topic has been Inadequate discussion Depth coherently. discussion. Displays adequately discussed. of issues Little/no Significant evidence of sound understanding Displays some demonstrated Critical analysis and with some analysis of understanding and understanding or Reflection. Topics. analysis of issues. analysis of most issues and/or some irrelevant /65 marks information. Summary & Conclusion An interesting, well A good summary of the Satisfactory summary of Poor/no summary of the written summary of the main points. the main points. main points. main points. A good final comment A final comment on the A poor final comment on An excellent final on the subject, based subject, but introduced the subject and/or new comment on the on the information new material. material introduced. subject, based on the provided. /05 marks information provided. Referencing Correct referencing Mostly correct Mostly correct Not all material correctly (APA7 Style). All quoted referencing (APA7 Style). All referencing (APA7 Style) acknowledged. material in quotes and quoted material in Some problems with Some problems with the acknowledged. All Quotes & acknowledged. quoted material and reference list. paraphrased material All paraphrased material paraphrased material acknowledged. acknowledged. Some problems with the Correctly set out Mostly correct setting reference list. /10 marks reference list. out reference list. SubTotal-/100 marks Total out of 20
Answered 8 days AfterAug 09, 2021ICT205

Answer To: ICT205 Data Analytics Assessment 1 – Case Study Report Overview A data analytics project starts with...

Shubham answered on Aug 13 2021
145 Votes
Assessment 1 – Case Study Report
Table of Contents
1. Introduction    3
2. Overview of the data    3
3. Data Preprocessing    4
a. Data Cleaning    4
b. Data Transformation    5
c. Data Reduction    6
4. Dashboard Design    7
5. Conclusion    9
6. References    10
7. Appendix: Data set    12
1. Introduction
    Data visualization is the process that is used for translation of information that is used for visualization of context and making data easier to understand. It provides an easier way to identify patterns and outline the large data set. It is the element of broader data discipline that aims for locating, id
entification, manipulation and delivery of the data in an effective way. This provides with advanced analytics that is important for visualization of output for monitoring the result and ensures that it provides easy interpretation of numerical output. It provides an effective way for communicating with the information using visual information.
2. Overview of the data
    Data set includes the collection of the data and the data is presented in the tabular form. Every column represents the particular variable. Every row corresponds to the given member of the dataset. It provides the value for each variable and it includes the weight and height of the object. It comprises the data for more members corresponding to the number of rows. The state of the data includes collection of specific purposes and it provides the way for data to be collected (Qin et al. 2020). It includes collection of the information that contains case level data and it provides statistical manipulation of case levels for survey instances. The dataset provides information on 5077 cars along with the information of MPG, displacement, number of cylinders, horsepower, weight, model, Origin and acceleration. In the provided data set, the information about different cars can be extracted.
3. Data Preprocessing
a. Data Cleaning
    The data cleaning process includes removal of irrelevant values, this is the foremost important thing that can help in removing useless pieces of data from the data set. The removal of a particular piece of the data can help in ensuring that the garbage data is analyzed. It is the process for removal of incorrect, corrupted and duplicate data within the dataset. It includes the combination of multiple data sources and it provides the opportunity for the data to be mislabeled. It provides the way for prescribing the exact set in the data cleaning process because the process plays a crucial role in the establishment of templates for removal of data. Reduplication is the largest area that should be considered and it can help in analysis of the problem (Po et al. 2020). It provides the option for dropping the observation for the missing value and it is based on observation. It provides the opportunity for losing the integrity of the data that are operating along with the assumption.
    Getting rid of duplicate value can help in removing the useless value. Duplicate data can increase the amount of data and waste the time. It is important to get rid of the data with the use of simple search. It can be combined with data from multiple sources. It is important to remove duplicate data and find a couple of duplicates and it must move on without worrying. The pattern for the application includes a common pattern for seeing the big block of duplicates due to the data that are being copied and pasted at the end of the existing data. It is required for speeding up the performance and this can help in minimizing the time for the process and it can help in reduction of the time that is required for defragmentation. The use of software can help in searching for duplicate data by content and name. This can help in identification of identical data that have the same context.
    It is important to take care of missing value. It is important for handling and keeping the data clean and free from errors. It includes particular columns in the dataset that have too many missing values (Po et al. 2020). This can help in analysis of the data and it provides a variable of interest. It includes the technique that can help in increasing power for the analysis and assuming the missing data. It provides the iterative value and process for prediction of dependent value. It is the step that provides a linear relationship between the variables that are used for regression.
b. Data Transformation
    Aggregation is the method that is used for raw data that are expressed and gathered in the summary form from the statistical analysis. The data is aggregated and is written as the report and this can help in analysis of aggregated data for gaining the insight about the particular resources. It is the process that is used for gathering the data and presentation of the data in summarized format. The data can be gathered from multiple data sources with the intent of combining them into a summary for data analysis. It provides the insight from the data analysis and it depends on the quality and amount of data used. This requires gathering of data for providing quality and accurate data (Nusrat, Harbig & Gehlenborg, 2019). The aggregate data includes demographics and behavior metrics. The data can be used for marketing and it can help in providing better offers. It provides the process of the data that can be brought and it can be used for performance of the statistical analysis. The information can be drawn from statistical analysis and data aggregation that can be used for telling all kinds of information about the data.
    The aggregation of the data can be used for manual processes and it provides the necessary process. It provides with the process for successful marketing campaign analysis and it provides with the process that can help in improving the marketing result. It is used for comparison of data for multiple channels that is essential for making marketing decisions. It focuses on the benefits of providing the platform for providing support. It is used...
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