You're a data analyst at Adventure Works who needs to analyze year-over-year sales growth. What should you do to define the scope of your data for this task?0 / 1 pointDefine the years, geographical regions, and product categories to include in the analysis.Start the process of cleaning and preparing the data for data analysis.Identify the data type to be collected and extract the data from the relevant sources.Include as many data sources and datasets in your analysis as possible to ensure comprehensive insights.
Question
You're a data analyst at Adventure Works who needs to analyze year-over-year sales growth. What should you do to define the scope of your data for this task?0 / 1 pointDefine the years, geographical regions, and product categories to include in the analysis.Start the process of cleaning and preparing the data for data analysis.Identify the data type to be collected and extract the data from the relevant sources.Include as many data sources and datasets in your analysis as possible to ensure comprehensive insights.
Solution
To define the scope of your data for this task, you should:
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Define the years: Determine the time period you are interested in. For year-over-year sales growth, you will need at least two consecutive years of sales data.
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Define the geographical regions: Depending on the scope of your business and the purpose of your analysis, you might want to include all regions where your company operates, or you might want to focus on specific regions.
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Define the product categories: Again, depending on the purpose of your analysis, you might want to include all product categories, or you might want to focus on specific categories.
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Identify the data type to be collected and extract the data from the relevant sources: You will need sales data, which is typically numerical. You might also need categorical data, such as product category or region. You will need to extract this data from your company's databases or other sources.
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Start the process of cleaning and preparing the data for data analysis: This might involve dealing with missing values, outliers, or errors in the data. It might also involve transforming the data, such as by aggregating it by year, region, and product category.
Including as many data sources and datasets in your analysis as possible can provide more comprehensive insights, but it can also make the analysis more complex and time-consuming. You should aim to include all relevant data, but not necessarily all available data.
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