The PL-300 Microsoft Power BI Data Analyst examination emphasizes the need for data professionals to thoroughly evaluate their data, including data statistics and column properties. Good data evaluation is crucial for meaningful analysis as it aids in data cleaning and data structure organization, consequently improving the accuracy of the insights derived from it.
Evaluating Data
Evaluating data is a crucial step in data preparation and analysis. It involves examining the data to understand its quality, relevance, consistency, and accuracy. Through a thorough evaluation of your data, you can potentially predict patterns and trends, validate or refute assumptions, and ultimately make informed decisions.
Power BI provides various methods and tools for data evaluation. An example is Power Query, a powerful ETL tool that lets you connect, transform, and load your data from various sources into your Power BI reports.
In Power Query, you can view and evaluate your data by inspecting the “Preview pane”. In this pane, you’ll see a preview of your currently loaded data, allowing you to evaluate its structure and completeness at a glance.
let
Source = Excel.Workbook(File.Contents("C:\SampleData.xlsx"), null, true),
Sales_Table = Source{[Item="Sales",Kind="Table"]}[Data]
in
Sales_Table
With this script, you are loading a ‘Sales’ table from an Excel Workbook. Once loaded, inspect the data in the Power Query Preview pane to evaluate its structure and contents.
Data Statistics
Data statistics, in the context of data analysis, are the measures that provide a summary or an overview of your dataset. The measures usually consist of mean, median, mode, standard deviation, and variance, among others.
In Power BI, you can easily view these measures through the ‘Column profile’ pane inside Power Query. This pane gives you a quick glance of these statistics, like distinct count, minimum and maximum values, average, and the total null count.
To illustrate, let’s consider a dataset of sales records. In the column profile pane, the average (mean) may give you the average revenue per sale, while the minimum and maximum can indicate the range of revenue values.
Column Properties
Column properties hold equally important information about your dataset. They describe the attributes of the dataset’s variables, such as their names, types (numerical, textual, date/time, etc.), and other meta-information like descriptive statistics.
You can view the column properties in Power Query by selecting ‘Column Tools’ -> ‘Column Info’.
Here, you can see information such as the column’s data type, any special characteristics it has (like if it’s a key), and any description provided. This helps in understanding the types of data you’re dealing with and guides how to process and analyse it.
let
Source = Csv.Document(File.Contents("C:\Sales.csv"),[Delimiter=",", Columns=3, Encoding=1252, QuoteStyle=QuoteStyle.None]),
#"Changed Type" = Table.TransformColumnTypes(Source,{{"Column1", type text}, {"Column2", Int64.Type}, {"Column3", type datetime}})
in
#"Changed Type"
In this script, you’re loading a CSV file and directly changing the data types of the columns. This makes clear what data types you’re dealing with from the onset.
In conclusion, evaluating data, scrutinizing data statistics and column properties are invaluable skills for data analysts. Power BI provides several easy-to-use interfaces and capabilities that can help you efficiently carry out these tasks in your data preparation and analysis process. These methods provide a solid foundation to further dive into more complex data analysis and create meaningful, reliable insights from your data.
Practice Test
True or False: A histogram is an inappropriate type of visualization for evaluating column properties.
- False
Answer: False
Explanation: A histogram is a useful tool for visualizing and evaluating column properties, particularly the distribution of data.
In Power BI, a data analyst can use DAX calculations to evaluate data.
- A. True
- B. False
Answer: A. True
Explanation: DAX (Data Analysis Expressions) is a library of functions and operators used to create formulas in Power BI, Analysis Services, and Power Pivot in Excel.
You can use the Scatter Chart to evaluate the correlation between two numeric columns in Power BI.
- A. True
- B. False
Answer: A. True
Explanation: A scatter chart in Power BI is used to find the correlation between two numeric columns, making it possible to evaluate the relationship between two different data points.
Which of the following are data statistics you can evaluate in Power BI? (Multiple select)
- A. Mean
- B. Median
- C. Mode
- D. All of the above
Answer: D. All of the above
Explanation: Power BI allows you to evaluate all basic statistical measures including mean, median and mode.
In Power BI, null or blank values in your column data can impact your calculations.
- A. True
- B. False
Answer: A. True
Explanation: Null or blank values in your data can impact aggregate calculations like averages, sums, and counts, thus leading to inaccurate results.
True or false: Power BI does not allow column property evaluation for data fields with text data type.
- False
Answer: False
Explanation: Power BI allows for column property evaluation for all data types including text, and gives us statistical measures like count of distinct values.
In Power BI, which feature allows you to explore, transform, and cleanse data?
- A. Power View
- B. Power Query Editor
- C. Power Pivot
- D. Power Maps
Answer: B. Power Query Editor
Explanation: Power Query Editor is the feature in Power BI that provides an environment for data exploration, transformation, and cleansing.
You can use DAX calculations to create calculated columns and tables in Power BI.
- A. True
- B. False
Answer: A. True
Explanation: DAX allows you to create new information from data that already exists in your model through calculated columns and tables.
True or False: In Power BI, you cannot change the data type of a column.
- False
Answer: False
Explanation: You can change the data type of a column in Power BI using the Power Query Editor or through modeling in the main Power BI interface.
In Power BI, you can split columns based on delimiters or by number of characters.
- A. True
- B. False
Answer: A. True
Explanation: Power BI allows you to split columns in a table by delimiters like comma or by number of characters using Power Query Editor.
Which of the following is not a statistical property that a Power BI data analyst can evaluate?
- A. Max value
- B. Sample space
- C. Standard deviation
- D. Variance
Answer: B. Sample space
Explanation: Sample space, a concept from probability theory, isn’t a statistical property that a Power BI analyst typically evaluates.
True or False: Power BI doesn’t support the evaluation of basic column properties like minimum value, maximum value, and average value.
- False
Answer: False
Explanation: Power BI supports the evaluation of basic column properties including minimum value, maximum value, and average value.
Which of the following DAX functions is used to calculate the average of a column in Power BI?
- A. AVERAGE()
- B. AVG()
- C. MEAN()
- D. ALL OF THE ABOVE
Answer: A. AVERAGE()
Explanation: In Power BI, the AVERAGE() DAX function is used to calculate the arithmetic mean of a set of values in a column.
To evaluate data in Power BI using Python, you should enable the Python scripting option.
- A. True
- B. False
Answer: A. True
Explanation: To use Python scripts in Power BI for data evaluation and analysis, the Python scripting option should be enabled.
True or False: You can’t evaluate missing data in Power BI.
- False
Answer: False
Explanation: Power BI allows you to evaluate missing or null data, and can replace these with meaningful substitutes if required for better interpretation.
Interview Questions
What is the primary purpose of evaluating data statistics in Power BI?
The primary purpose of evaluating data statistics in Power BI is to understand the overall data distribution, uncover trends and patterns, identify outliers and anomalies, and perform predictive analysis.
What are column properties in Power BI and why are they significant?
Column properties in Power BI are the unique characteristics or attributes of a data column, which include data type, name, and categorization. They are significant as they affect the type of visualizations, calculations, and analyses that can be performed on the data.
Explain the function of the “SUMMARIZECOLUMNS” function in Power BI?
The “SUMMARIZECOLUMNS” function in Power BI is used to return a summary table for the requested columns, grouping by the specified columns. It helps in aggregating and summarizing data for analysis purposes.
What is a histogram in Power BI and what is its purpose?
A histogram in Power BI is a graphical representation of data distribution across various categories. It helps in understanding the frequency of data points in different ranges, thereby revealing the underlying frequency distribution.
How do you change the data type of a column in Power BI?
To change the data type of a column in Power BI, select the column, go to the “Column tools” tab, and change the “Data type” drop-down menu to the desired data type.
What does the “COUNT” function do in Power BI?
The “COUNT” function in Power BI counts the number of rows in a table or column where the specified column contains numbers, dates, or text.
What is a column profile in Power BI?
A column profile in Power BI provides descriptive statistics about a column’s data, like the count of unique, distinct values, the top error, and the distribution of values.
What does the “AVERAGE” function do in Power BI?
The “AVERAGE” function in Power BI returns the arithmetic mean of all the numbers in a column.
What is the procedure to visualize a trend in the data using Power BI?
To visualize a trend in the data using Power BI, one can use a line chart or a scatter plot. One needs to select the visualization type and then select the appropriate measure or column for the x and y-axis.
What is the “MIN” function in Power BI, and what does it do?
The “MIN” function in Power BI returns the smallest number in a column of numbers.
How can you identify outliers in your data using Power BI?
Outliers in Power BI can be identified by using visuals such as scatter plots or box and whisker plots. These plots help visually determine data points that fall significantly above or below the expected range of values.
How does Power BI handle null values in columns?
Power BI treats null values in columns either as blanks or as errors, depending on the context. For example, when calculating averages, Power BI excludes the null values.
What is the distinct count in Power BI?
The distinct count in Power BI is a measure which counts the number of distinct values in a column. It is used to find the number of unique values.
How can you group data in Power BI?
Data grouping in Power BI can be performed using the “Group by” option in the context menu of the desired column. This option allows grouping data by multiple levels and creating aggregations like sum, average, count, etc.
What is a stacked column chart in Power BI?
A stacked column chart in Power BI is a bar chart that places multiple data series on top of one another in a single column. This type of chart is useful for showing the relationship of individual items to the whole, comparing multiple data series, and displaying part-to-whole relationships.
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