Assessment TaskIAB303 Data Analyticsfor Business InsightSemester I 2019Assessment 2 – Data Analytics NotebookName Assessment 2 – Data Analytics NotebookDue Sun 28 Apr 11:59pmWeight 30% (indicative weighting)Submit Jupyter Notebook via BlackboardRationale and DescriptionFoundational to addressing business concerns with data analytics is an understanding ofpotential data sources, the kinds of techniques that may be used to process and analysethose data, and an ability to present the final analytics in a way that is meaningful for thestakeholders.This assessment will involve the creation a Jupyter notebook, demonstrating yourunderstanding of the technical process required to address a business concern using dataanalytics.You will use your knowledge from the workshops together with the techniques practiced in thepractical lab sessions, and apply both to a selected business scenario. You will not onlyperform the necessary steps, but also provide an explanation of your decision process.Learning OutcomesA successful completion of this task will demonstrate:1. An understanding of how a variety of analysis techniques can be used to take raw dataand turn it into information that is meaningful to a business concern.2. How a particular business concern shapes the decision-making process in dataanalytics.3. An ability to select, prepare, and use appropriate data, analysis techniques, andvisualisations.4. An understanding of a variety of data sources and the way that the data is structured.Essential ElementsYou must submit 1 Jupyter notebook which will:1. Demonstrate an understanding of:a. Selecting and processing data appropriate for required analysisb. Selecting and performing analysis techniques appropriate to a business concernc. Addressing a business concern through visualisation of analysis2. Document your decision making with explanations of your choicesYou will use the code cells of the notebook to demonstrate your grasp of analysis techniques,and you will use the markdown cells to (a) craft a narrative linking the analysis to a businessconcern, and (b) document your decision making.Further detail on the steps required to produce the notebooks is outlined in the ‘detailedinstructions’ section below.Marking CriteriaThis assessment is criteria referenced, meaning that your grade for the assessment will begiven based on your ability to satisfy key criteria. Refer to the attached Criteria Sheet andensure that you understand the detailed criteria.It is important to realise that the assessment does not only require that you know orunderstand, but also that you demonstrate or provide evidence of your understanding. Thismeans that you are making your knowledge and understanding clear to the person markingyour assignment.You will not receive marks or percentages for this assessment. You will receive an overallgrade (e.g. pass - 4, high distinction - 7) based on the extent to which you meet the criteria. Ingeneral, the most important criteria (criteria 1-5) will be essential to the grade, and the leastimportant (criteria 6-7) will affect the grade when important criteria results conflict or areambiguous.Detailed InstructionsThe notebook should tell a story (narrative) based on a selected scenario, that starts with thedata selection, moves through the analysis, and concludes with connecting the visualisation tothe primary business concern of the scenario. The story should make sense to thestakeholders.For each step, you must document your decision making and explain why you did what youdid. This description of thinking should align with the overall narrative.1. Scenario: This will briefly describe the business, the business concern and its significanceto the business, and the key stakeholders who have an interest in the concern. Scenarioswill be provided via blackboard for you to select from. You may choose your own scenarioonly if it is approved (in advance) by a member of the teaching team – it must meetminimum standards. A description of how you interpret your scenario should be providedat the beginning of your notebook.2. Data: You will choose a data source appropriate to your scenario, and write the necessarycode to obtain the data and make it available for analysis in your notebook.3. Processing: The data may need to be processed prior to analysis. At a minimum it shouldbe cleaned, but it may need to be processed in other ways appropriate to your chosenanalysis technique.4. Analysis: You will need to select an analysis that is appropriate to your scenario, and whichalso includes:a. At least two of: reading and cleaning a text file, parsing unstructured data,analysing with social media data.b. At least one of: use of open data API or web-scraping.5. Visualisation: You will need to create a visualisation that is appropriate to your scenario andthe results of your analysis. You must include at least two different types of visualisation(e.g. tabular, graph or chart, annotated text).6. Connect with concern: You need to connect your visualisation back to the businessconcern in a way that is meaningful to the stakeholders of the business. This may involveproviding additional descriptive text that explains how the visualisation might address theconcern.ResourcesThe following resources may assist with the completion of this task: Refer to the workshop and lab notebooks for techniques and discussions of businessconcerns Use Slack to exchange code and discuss detail of the taskQuestionsQuestions related to the assessment should be directed initially to your tutor during the lab session oron the appropriate slack channel. Your tutor may address these for the benefit of the whole class.The teaching team will not be available to answer questions outside business hours, nor immediatelybefore the assessment is due.Criteria Sheet – Assessment 1 Workbook - IAB303 Data Analytics for Business InsightCriteria 7 6 5 4 3 2[1] Evidence of ameaningful connectionbetween data analyticsand a businessconcern.Makes a meaningfulconnection between dataanalytics and a businessconcern with aconsistently clearnarrative that is interestingand engaging.Makes a meaningfulconnection betweendata analytics and abusiness concernthrough a consistentlyclear narrative.Mostly establishes ameaningful connectionbetween data analytics anda business concern butlacks some consistency inthe clarity of the narrative.Sufficiently connects thedata analytics to abusiness concern toestablish a meaningfulrelationship through theuse of a suitable narrative.Some elements of thenarrative make it difficult tosee a meaningfulconnection between thedata analytics and abusiness concern.There is little or noevidence of ameaningful connectionbetween the dataanalytics and abusiness concern.[2] Demonstration ofappropriate techniquesfor addressing abusiness concern withanalytics.All techniques are clearlyappropriate and areconsistently implementedin an exemplary way.All techniques areclearly appropriate andare implemented well.All techniques areappropriate but someimplementations could beimproved.Techniques are sufficientlyappropriate and areimplemented adequately.Techniques are eitherinappropriate and/or areused incorrectly.There is little or nodemonstration ofappropriate techniqueselection or use.[3] Evidence ofunderstanding analyticsvisualisation and itssignificance to thebusiness concern.Provides exemplaryevidence of a deepunderstanding of analyticsvisualisation and itssignificance.Provides evidence of arobust understandingof analyticsvisualisation and itssignificance.Mostly provides evidence ofan understanding ofanalytics visualisation andits significance.Provides evidence of abasic understanding ofanalytics visualisation andits significance.There is a lack of evidenceof understanding analyticsvisualisation and/or itssignificance.This is little or noevidence ofunderstanding ofanalytics visualisation.[4] Evidence of anunderstanding of dataselection and analysistechniques and theirimportance to the dataanalytics.Provides exemplaryevidence of a deepunderstanding of dataselection and analysistechniques and theirimportance.Provides evidence of arobust understandingof data selection andanalysis technique andtheir significance.Mostly provides evidence ofan understanding of dataselection and analysistechniques and theirsignificance.Provides evidence of abasic understanding ofdata selection and analysistechniques and theirsignificance.There is a lack of evidenceof understanding of dataselection and/or analysistechniques and/or theirsignificance.There is little or noevidence ofunderstanding of dataselection and analysistechniques.[5] Demonstration ofappropriate dataselection, processingand analysis techniquesin order to yield adesired result.Data selection is excellentfor the task and alltechniques are clearlyappropriate andimplemented in anexemplary way.Data selection is wellsuited to the task andall techniques areappropriate andimplemented well.Data selection, processingand analysis is mostlyappropriate and suitable tothe task. Most areimplemented well.Data selection, processingand analysis isdemonstrated sufficientlyto achieve a desired result.Some processes ortechniques are missing,incomplete and/or areinsufficient to achieve arequired result.There is little or nodemonstration of dataselection and/oranalysis.[6] Demonstration ofeffective Englishexpression and use ofmarkdown.Excellent Englishexpression and use ofmarkdown.Very good Englishexpression and use ofmarkdown.Generally good Englishexpression and use ofmarkdown.English expression and useof markdown issatisfactory for the tasks.English expression and/oruse of markdown isinsufficient for the tasks.There is little or noevidence of ademonstration ofEnglish expression.[7] Demonstration ofgood qualityprogramming practicesin the notebook code.Excellent code quality dueto adherence to qualityprogramming practices.Good code quality dueto mostly adhering toquality programmingpractices.Generally good code qualityby mostly adhering toquality programmingpractices.Code implementations aresufficient for the requiredtasks.Code implementations areinappropriate and/orinsufficient for the tasks.There is little or noevidence of goodprogrammingpractices.本团队核心人员组成主要包括BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：[email protected] 微信：codehelp
