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COMM1190 data analyst
创建日期：2024-05-30 16:30:50
标签： COMM1190
data analyst

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COMM1190 Industry-based Assessment 1: Individual Report

Week 4: 3:00 pm Friday (AEDT)
20%
A written report
Maximum word count of 750, excluding references, figures, tables, and
appendices.
Via Turnitin on Moodle course site
Objective
The objective of this individual assessment is to evaluate your ability to conceptualize and
solve analytics problems, your proficiency in R programming, and your capacity to provide
business recommendations based on analytics results. In this assignment, you will conduct
an exploratory data analysis as a data analyst. You are expected to analyze data using
statistical and visualization techniques. This learning content has been covered in the
course until the end of Week 3.
Company and Product Background
You have recently been employed as a data analyst for Amazing Sports Australia Ltd
(ASAL), an online e-store. ASAL specializes in selling a wide range of branded and non-
branded sports products, which are broadly categorized as (i) Equipment, (ii) Apparel, and
(iii) Footwear. The company has recently launched a shopping mobile app and is concerned
about its effectiveness in increasing sales and promoting its products. The management
team aims to understand customer spending patterns and behavior with the ultimate goal
of optimizing the app usage and enhancing sales.
About the task:
As a data analyst on this project, your primary task is to utilize R to explore the provided
dataset and generate visualizations that assist ASAL's app managers in comprehending
user behavior and engagement with the app. By using R, you will analyze the dataset
and create exploratory visualizations to identify trends and patterns that can provide
insights for marketing and sales strategies.
The company has provided you with data encompassing user demographic information
(such as age, gender, etc.) and application usage information (such as number of
referrals to friends). A separate document, the Data Dictionary, will be shared with you,
containing a detailed description of each attribute in the dataset.
• Conduct descriptive analytics to identify the factors associated with customers'
spending on sports products. Descriptive analytics encompasses the use of
statistical analysis and visualization techniques. For instance, employing a box
plot and a bar chart are considered as two distinct techniques.
• Offer recommendations to the leadership team on enhancing customers'
spending and user engagement on applications, based on the results obtained
from descriptive analytics.
Guidance on Data Analysis
Note: The dataset and the data dictionary will be provided to you separately.
There is not a single correct answer to the assignment. The dataset includes many
attributes for you to explore, and some attributes are likely to be more useful than
others. Therefore, it is important that you systematically explore different variables in
the dataset to facilitate your analysis.
• Consider potential key factors associated with increasing sales and app
engagement by relating them to real-world scenarios. Justify your selection of
variables by referring to industry examples and supporting arguments.
• While creating multiple graphs for your assignment, ensure that you only
include figures that support your main findings. These graphs should highlight
key features of the associations you are reporting.
• Your recommendations to the leadership team at ASAL should be well
supported by your visualizations and/or statistical summaries.
• Explicitly state any key assumptions that impact your data analysis and any
caveats regarding your recommendations.
Requirements:
1. Problem Exploration (10%)
• Explore and understand the business problem of retail sales within the
Australian context.
• Clearly state the purpose of the analytics tasks.
2. Data Analysis (50%)
• Justify the selection of techniques and variables, with a recommendation of
using more than 3 variables for analysis.

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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