Data and Business Analytics

• Databases
• Excel files
• SharePoint
• Online services
• Cloud platforms

Data Bars: Apply data bars to visually represent the magnitude or relative values of your data. 

Color Scales: Use color scales to apply gradient colors to your data based on their values. This allows you to quickly identify high or low values and spot trends or outliers.

Icon Sets: Apply icon sets to display symbols or icons based on the values in your data. This is useful for visualizing data categories or indicating performance levels.

Top/Bottom Rules: Highlight the top or bottom values in a range. You can use this to identify the top-selling products or the lowest-performing regions in a sales dataset.

Data Validation: Apply data validation rules to ensure data consistency and accuracy. 

Highlight Duplicates: Identify and highlight duplicate values in a dataset. This helps in identifying data quality issues or spotting repeated entries.

Formula-based Rules: Create custom rules using formulas to apply conditional formatting based on complex conditions. 

Heat Maps: Use conditional formatting to create heat maps by applying color gradients to a range of values. This allows you to visualize data density or distribution across a range.

Sparklines: Add sparklines to display small inline charts within cells, representing trends or variations in the data. 

Highlight Errors: Use conditional formatting to highlight cells with errors, such as #DIV/0! or #N/A, making it easier to spot and fix issues in your data.

Summarize data: How to use Pivot tables to summarize large data sets by aggregating and calculating totals, averages, counts, percentages, and other statistical measures. You can quickly obtain insights into your data by organizing and summarizing it in a meaningful way.

Group and categorize data: How to use Pivot tables to group data based on specific criteria, such as dates, text values, or numerical ranges. This grouping can help you analyze trends, patterns, or segments within your data.

Analyze data distributions: How to use Pivot tables to create frequency distributions and histograms to visualize the distribution of values within a data set. This allows you to understand the spread and concentration of data points, which can be helpful for data analysis.

Drill down into data: How to use Pivot tables to drill down into the underlying data behind summarized values. You can double-click on any cell in a pivot table to see the detailed records that make up that value. This allows you to explore and investigate data anomalies or outliers.

Compare and contrast data: How to use Pivot tables to compare data across different categories or dimensions. You can easily create multiple levels of row or column labels to analyze data from various angles. This feature is particularly useful for identifying patterns, variances, or relationships within your data.

Filter and sort data: How to use use Pivot tables to filter and sort data based on specific criteria or conditions. You can focus on subsets of data that meet certain requirements and analyze them separately. This capability helps you isolate relevant information and gain deeper insights into your data.

Calculate proportions and ratios: How to use Pivot tables to calculate proportions, percentages, and ratios between different values. You can compare the relative contribution of various categories or assess the impact of different factors on your data.

Creating pivot charts

Removing duplicates: How to use Excel built-in tools to identify and remove duplicate values in a dataset. 

Handling missing values: How to use Excel functions like ISBLANK, ISNA, and COUNTBLANK to identify missing values. You can use filtering or sorting to isolate and handle missing values by either deleting rows, replacing them with appropriate values, or using techniques like imputation.

Data type conversion: How to use Excel to change the data type of a column to ensure consistency and compatibility. 

Splitting and merging columns: How to use Excel functions for text manipulation, such as Text to Columns, CONCATENATE, LEFT, RIGHT, and MID.

Correcting inconsistent data: How to use Excel\'s Find and Replace feature to find specific values or patterns and replace them with corrected values. 

Handling outliers: How to use Excel functions like AVERAGE, STDEV, and Z.TEST to identify and deal with outliers. You can calculate z-scores, apply thresholds, and replace or remove outlier values based on your analysis requirements.

Formatting and cleansing data: Excel offers various formatting options to improve data readability. You can use tools like conditional formatting to highlight values meeting specific conditions, remove leading or trailing spaces with TRIM, or remove non-printable characters with CLEAN.

Removing unnecessary columns: How to remove redundant columns. This streamlines your dataset and improves analysis efficiency.

Handling inconsistent or incorrect data entries: How to use Excel tools like Data Validation to define rules and restrictions for data entry. You can set up dropdown lists, apply custom formulas, or limit values within specific ranges. This helps ensure data consistency and validity.

Data merging: How to use VLOOKUP to merge data from multiple tables based on a common key. 

Data validation: How to use VLOOKUP to validate data by checking if a value exists in a reference table. For instance, if you have a list of product codes, you can use VLOOKUP to verify if a given code is valid or exists in a product master table.

Data enrichment: How to use VLOOKUP  to enrich your data by retrieving additional information from a lookup table. Suppose you have a table with customer names and their corresponding zip codes, and you want to populate the city and state information for each customer. You can use VLOOKUP to search for the zip code in a reference table and return the associated city and state.

Error handling: How to use VLOOKUP to identify errors or inconsistencies in your data. For example, if you have a reference table of valid employee IDs, you can use VLOOKUP to check if a given ID exists in the table and flag any discrepancies or missing values.

Indexing and ranking: How to use VLOOKUP to assist in indexing and ranking data based on certain criteria. For instance, you can use VLOOKUP to find the rank of a particular value in a sorted dataset or extract the top or bottom values based on specific conditions.

Data reconciliation: How to use VLOOKUP for reconciling data between different sources. If you have two datasets with similar information but different formats or structures, you can use VLOOKUP to compare the data and identify any discrepancies or missing records.

Lookup and retrieve data: How to use XLOOKUP to search for a specific value in a column or row and return a corresponding value from another column or row. This is useful for retrieving specific data points based on certain criteria.

Dynamic data extraction: How to use XLOOKUP to extract data dynamically based on changing criteria. For example, you can use XLOOKUP to pull the latest sales figures for a specific product or retrieve the highest or lowest values from a dataset.

Approximate matching: How to use XLOOKUP  to perform approximate matching. This is particularly helpful when dealing with large datasets or when you need to find an approximate match for a specific value.

Handling errors and non-existent values: How to use XLOOKUP for handling errors or non-existent values. For instance, you can use the "if_not_found" argument to specify a default value to return when the lookup value is not found, avoiding errors and allowing for smoother data analysis.

Multiple criteria lookup: How to use XLOOKUP to perform complex lookups using multiple conditions. This can be useful when you need to retrieve data based on multiple criteria simultaneously.

Creating interactive dashboards: How to use XLOOKUP, in combination with other Excel features like data validation and drop-down lists,  to create interactive dashboards. Users can select criteria from the drop-down lists, and XLOOKUP will dynamically update the data based on the chosen criteria.

Index and match replacement

Filter data: How to use Timelines and slicers to filter data in your Excel worksheets. You can use them to quickly narrow down data based on specific criteria such as date ranges, categories, or any other relevant fields.

Time-based analysis: How to use Timelines for analyzing time-series data. You can create a timeline based on a date field and easily adjust the time range you want to analyze. This feature enables you to perform trend analysis, track changes over time, and identify patterns or seasonal variations in your data.

Visualize data: How to use Slicers to create visually appealing dashboards and reports. By connecting slicers to various data elements, you can provide an interactive way for users to explore different dimensions of your data. 

Comparative analysis: How to use Slicers to perform comparative analysis by selecting multiple items simultaneously. You can compare different regions, products, or any other dimensions of your data by simply selecting them in the slicer. 

PivotTable filtering: How to apply Timelines and slicers  to PivotTables. 

Dynamic reporting: How to use Timelines and slicers to create dynamic reports in Excel. Instead of manually updating charts or tables, you can use slicers to control the data displayed in your reports. 

• String Indexing and Slicing
• Strings Methods

• Creating Histograms
• Creating Boxplots
• Countplots
• Line plots for Time-Series Data

• Creating Side-by-Side Boxplot
• Learn how to create and interpret Scatter plot
• Understand correlations in data using Heatmap

Introduction to the Query Editor interface:  Learn how to navigate the different tabs, preview data, and access various data transformation options.

Importing and loading data: Know how to import data into Power Query from different sources and load it into Power BI. You can choose to load the entire dataset or specify transformations before loading.

Data cleaning: Use Power Query\'s data cleaning capabilities to remove duplicates, filter rows, handle missing values, and apply data quality checks. This involves tasks like removing blank rows, replacing null values, and handling errors.

Column transformations: Perform various transformations on columns, such as renaming columns, changing data types, splitting columns based on delimiters, merging columns, and extracting specific parts of text.

Data shaping: Utilize shaping techniques like pivoting, unpivoting, and transposing data to reshape it according to your requirements. This involves reorganizing data between rows and columns.

Conditional transformations: Apply conditional logic to transform data based on specific criteria. This includes using IF statements, filtering data based on conditions, and creating custom conditional columns.

Merging and appending data: Combine data from multiple tables or data sources using merging and appending operations. Learn how to merge tables based on common columns or append tables vertically.

Grouping and aggregation: Group data based on specific criteria and perform aggregations such as sum, average, count, minimum, and maximum. Understand how to use the Group By and summarization functions in Power Query.

Custom columns and functions: Create custom columns using Power Query\\\'s formula language (M). Learn how to write custom functions to perform complex transformations and calculations on your data.

Parameterization: Parameterize queries to make them more flexible and reusable. This allows you to dynamically adjust values or filters within the query based on user input or external parameters.

Understand the different types of relationships: such as one-to-one, one-to-many, and many-to-many.

Cardinality and cross-filtering: Learn about cardinality, which defines the relationship between tables based on the number of unique values in a column. Understand how to set up cross-filtering behavior to control how filters flow between related tables.

Hierarchies: Define hierarchies within your data model to organize data into meaningful levels. Hierarchies are useful for drill-down analysis and enable users to navigate data at different levels of granularity easily.

Time intelligence: Learn how to incorporate time intelligence into your data model. Leveraging Power BI built-in functions and capabilities to handle time-based calculations such as year-to-date, quarter-to-date, or moving averages.

Data modeling best practices: Understand and implement data modeling best practices to optimize performance, reduce data redundancy, and ensure data accuracy. This includes techniques such as avoiding circular dependencies, minimizing calculated columns, and optimizing relationships.

Choosing the right chart types: Common chart types include bar charts, line charts, pie charts, scatter plots, area charts, and maps. Understand the strengths and limitations of each chart type to make informed choices.

Simplifying and declutter: Keep your visualizations clean and simple to avoid overwhelming the viewer. Minimize the use of unnecessary elements, such as excessive gridlines or labels. Emphasize the key information and remove distractions.

Providing context: Provide context and reference points to help viewers interpret the data accurately. Include axis labels, legends, and units of measurement. Use reference lines or benchmarks to compare data against a standard or target.

Interactivity: Leveraging interactive features in Power BI to enhance data exploration and analysis. Using tooltips, drill-through functionality, slicers, and filters to allow users to interact with the visualizations and dive deeper into the data.

Advanced visualizations: Exploring advanced visualizations like treemaps, heat maps, waterfall charts, box plots, or custom visualizations available through Power BI marketplace or development.

Clear and concise structure: Learn how to organize your report with a clear and logical structure. Use sections, pages, and visuals to present information in a way that is easy to navigate and understand.

Visual hierarchy: Utilize visual hierarchy to guide users\\\' attention and emphasize important information. Arrange visual elements based on their relative importance and using formatting options such as font size, color, and formatting to highlight key insights.

Consistent theme and branding: Apply a consistent theme and branding to your report. Using color schemes, fonts, and logo placement that align with your organization\\\'s brand guidelines to create a cohesive and professional look.

White space and alignment: Using white space strategically to improve readability and avoid clutter. Ensure proper alignment of visuals, text, and other elements to create a neat and organized appearance.

Grids and guides: Using grids and guides to align visuals and maintain a consistent layout throughout the report. This helps create a clean and structured design.

Visual selection and formatting: Choose appropriate visualizations based on the type of data and the insights you want to convey. Apply appropriate formatting options such as colors, labels, tooltips, and data labels to enhance the clarity and visual appeal of your visuals.

Drill-through and interactivity: Utilize drill-through functionality and interactive elements to enable users to explore data in more detail. This allows users to interact with visuals, drill into specific data points, and gain deeper insights.

Report navigation: Implement intuitive and user-friendly navigation within your report. Using bookmarks, buttons, and links to enable users to move between different sections, pages, or drill-through levels.

Syntax and operators: Understand the syntax of DAX formulas, including operators (+, -, *, /), parentheses, and logical operators (AND, OR, NOT).

Calculated columns: Use DAX to create calculated columns within your tables. 

DAX Measures: Learn how to create Measures using DAX functions such as SUM, AVERAGE, COUNT, MAX, MIN, and more.

DAX functions: Gain familiarity with a variety of DAX functions available for various purposes. These include mathematical functions (e.g., ABS, SQRT), statistical functions (e.g., AVERAGEX, STDEV), time intelligence functions (e.g., SAMEPERIODLASTYEAR, DATESYTD), text functions (e.g., CONCATENATE, LEFT), and many more.

Contexts: Understand the concept of row context and filter context in DAX. 

Aggregation functions: Learn about DAX functions used for aggregations, such as SUMX, AVERAGEX, and COUNTX. These functions allow you to perform calculations across multiple rows and tables, applying filters and iterating through data.

Time intelligence functions: Leveraing Power BI time intelligence functions like TOTALYTD, PREVIOUSMONTH, and DATEADD for analyzing data over time.

Filtering and CALCULATE function: Learn how to use the CALCULATE function to modify the filter context and apply specific filters within a DAX expression.

Variables: Utilize variables to store intermediate results or expressions within DAX formulas.