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(https://Chrisanthe Steininger(She/Her)()13 May 2021In thinking about the challenges of data analytics projects, the following questions would helpto work through the difficulties:1) Poorly defined problem:What are the goals? - This one seems like an obvious first step. You cannot jump into a projectwithout knowing what you are trying to achieve.What resources will I need to solve the problem? - It's critical to know if you have the rightresources to begin the work, and if not, you need to identify what you need and how you willget it.2) Incomplete or incorrect data:Where did this data come from? - This might give more context to the data and would help tounderstand the gaps.Can we gather additional data? - The answer might be "no," but it's important to think aboutwhether or not you have access to supplemental data that would help to complete any gaps.How can I transform this information? - This is part of the analysis, and would help to thinkthrough how you will clean the data to make it more useful to complete the project.3) Insufficient communication:How can I best explain myself? - This is important to think about so you consider all theoptions for communicating. Perhaps there are tools you can use, or there are preferredcommunication methods across your team or audience.
5/29/22, 7:49 PMTopic: Module 5 Discussion Board AssignmentWho is the intended audience? - This is a really important piece of communication. Theaudience dictates your communication style, whether in the work process itself, or in theproduct of the work.
42/54Reply(httpYingrui Shao()14 May 2021ReplyHello Chrisanthe,Thanks for sharing your idea with us. I agree that it's very important to learn about theintended audience. The audience determines how we will communicate the results of theanalytics. In addition, I think that we show work on how to best explain the results ofanalytics. As for the quality of data, we should be aware of the data sources, such asprimary or secondary data sources. Sometimes, we need to confirm whether we haveselected the correct data for model building.(httpChrisanthe Steininger(She/Her)()15 May 2021Edited byChrisanthe Steininger()on 15 May 2021at 15:06ReplyHi Yingrui,Absolutely - I think that the data sources are very important to understand. This givesmore context to the data itself, and also can confirm the legitimacy or accuracy of thedata. Reputable sources allow for more confidence in accurate data, which allows youto have confidence in your analysis. Thanks!

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data analyst, data analytics process

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