Which of the following should be accomplished NEXT after understanding a business requirement for a data analysis report?
Correct Answer:
B
The next step after understanding a business requirement for a data analysis report is to determine the data necessary for the analysis. This step involves identifying the data sources, variables, metrics, and dimensions that are relevant and sufficient to answer the business question or problem. This step also involves assessing the availability, quality, and accessibility of the data, and planning how to collect, clean, and prepare the data for analysis. The other options are not the next steps after understanding a business requirement, but rather subsequent steps in the data analysis process. Rephrasing the business requirement is a step that can help clarify and refine the business question or problem before determining the data necessary for the analysis. Building a mock dashboard/presentation layout is a step that can help design and visualize the report before performing the data analysis. Performing exploratory data analysis is a step that can help explore and summarize the data before drawing conclusions and recommendations from the data. Reference: Data Analysis Process - DataCamp
Which of the following technologies would be best suited for creating a multiple linear regression model?
Correct Answer:
B
R is a statistical programming language that is specifically designed for data analysis and statistical modeling, making it highly suitable for creating a multiple linear regression model. It has extensive libraries such as lm() for linear modeling, which simplifies the process of model creation, diagnostics, and interpretation. R also provides robust tools for data manipulation and visualization, which are essential for preparing data for regression analysis and understanding the results123.
While Microsoft Power BI, SQL, and Tableau have capabilities for regression analysis, they are more limited compared to R. Power BI and Tableau are primarily business intelligence tools that offer some built-in analytics capabilities, but they are not as comprehensive as
R. SQL is a database query language that can perform some statistical calculations, but it is not inherently designed for statistical modeling4567.
References:
✑ Multiple Linear Regression in R: Tutorial With Examples - DataCamp1.
✑ Implementing linear regression in Power BI - SQLBI5.
✑ Choosing a Predictive Model - Tableau6.
✑ How Predictive Modeling Functions Work in Tableau7.
An analyst is reporting on the average income for a county and is reviewing the following data:
Which of the following is the reason the analyst would need to cleanse the data in this data set?
Correct Answer:
B
A data analyst received a large amount of third-party data that needs to be joined with in- house data files. After the data is joined, the analyst notices three columns all contain dates. Which of the following should the analyst do to maintain data consistency?
Correct Answer:
C
When dealing with multiple date columns from different data sources, it??s crucial to ensure consistency and accuracy in the dataset. The best practice is to merge the date columns and standardize the date format across the entire dataset. This approach helps maintain data integrity, simplifies analysis, and avoids confusion that could arise from having multiple date formats. Unifying the date format is particularly important when the data will be used for time series analysis or when dates are key to joining with other datasets.
References:
✑ Best practices in data merging emphasize the importance of a single point of reference and the need to avoid data loss or damage to individual data structures1.
✑ Power BI guides suggest that merging columns should be done carefully to maintain data integrity and avoid errors and inconsistencies2.
✑ Oracle Blogs highlight the need for a consistent number of columns among data sources when combining data with unions3.
✑ Excel tutorials recommend organizing data before merging and using formulas for complex merges4.
✑ An Excel guide on merging date and time columns advises employing functions to ensure seamless handling of non-date values5.
An analyst is designing a dashboard that will provide a story of the sales and sales customer ratio. The following data is available:
Which of the following charts should the analyst consider including in the dashboard?
Correct Answer:
A
For a dashboard that aims to tell a story about sales and the sales customer ratio, a column chart is an effective choice. Column charts are particularly useful for showing data changes over a period of time or for illustrating comparisons among items. In this case, a column chart can clearly display the sales figures for each site, allowing for easy comparison across different sites. Additionally, it can be used to represent the sales customer ratio by showing the proportion of sales per customer, which can provide insights into customer behavior and sales effectiveness.
✑ Line charts are best suited for displaying data trends over time, rather than for comparing individual categories.
✑ Pie charts could show the proportion of sales for each site, but they are not as effective as column charts for comparing multiple categories.
✑ Scatter charts are used to show the relationship between two variables, which is not the focus in this scenario.
References:
✑ Effective Use of Column Charts1
✑ Choosing the Right Chart for Your Data2
✑ Sales Dashboards: Examples & Templates3