1.1: How Can Learning Power BI Help You in Your Career?

1.2: Downloading: Power BI Desktop

1.3: Adjusting Options and Settings

1.4: Power BI Desktop Interface & Workflow

2.1: Power BI Front-End vs. Back-End

2.2: Types of Data Connectors (Files, Databases, Folders)

2.3: Basic Table Transformations

2.4: Data QA & Profiling Tools

2.5: Text Tools

2.6: Numerical Tools

2.7: Date & Time Tools

2.8: Conditional Columns

2.9: Grouping & Aggregating

2.10: Merging Queries

2.11: Appending Queries

2.12: Parameters

3.1: Database Normalization

3.2: Explaining Data Normalization (Need) in Excel

3.3: Expanded Tables, Context Transition and Evaluation Order

3.4: Fact & Dimension Tables

3.5: Primary & Foreign Keys

3.6: Star & Snowflake Schemas

3.7: Active & Inactive Relationships

3.8: Relationship Cardinality

4.1: Intro to DAX Calculated Columns

4.2: Common Text Functions

4.3: Basic Date & Time Functions

4.4: Conditional & Logical Functions

4.5: The SWITCH Functions

5.1: Intro to DAX Measures

5.2: Implicit vs. Explicit Measures

5.3: Understanding Filter Context and Filter Flow

5.4: Basic Math & Stats Functions

5.5: Counting Functions

5.6: Related Functions

5.7: The CALCULATE Function

5.8: The ALL Function

5.9: The FILTER Function

5.10: Iterator (X) Functions

6.1: Understand the concept of Date-Based Analysis and its importance in the corporate world.

6.2: Conduct analysis based on Dates Previous Period Analysis (Previous Month, Previous Quarter & Previous Year)

6.3: Growth Analysis (DoD %, MoM %, QoQ % & YoY %)

6.4: Same Period Previous Year (Feb 24 Vs Feb 25, Q1 2024 Vs Q1 2025, etc.)

6.5: To Date Analysis (MTD, QTD, or YTD)

7.1: Importance & usage of DAX Variables in corporate reporting

7.2: Mastering DAX functions: FORMAT and UNICHAR

7.3: Advanced visualizations for Variance Analysis with Color Formatting

7.4: Comparative analysis by Category & Sub-Category: Current vs. Previous Year amounts

7.5: Comparative analysis by Category & Sub-Category: Absolute Variance with data bars (red/green)

7.6: Comparative analysis by Category & Sub-Category: Relative Variance with dynamic data bars (red/green)

8.1: Leveraging What‑if Parameters for dynamic business insights.

8.2: Conduct Quadrant Analysis (Keep, Develop, Evaluate, Retain)

8.3: Integrate What‑if Parameters for Target Sales & Target Sales Variance with slicers

8.4: Add dynamic Reference Lines

8.5: Build measures for Conditional Formatting (dynamic color coding: Green, Grey, Orange, Red)

8.6: Utilizing Field Parameters to drive interactive dashboards

8.7: Create dynamic slicers for switching Calculations, Periods, and Information

8.8: Visualize trends with Line Charts and detailed views with Table Charts

9.1: Grasp the significance of data source settings

9.2: Convert fixed M language elements into dynamic parameters

9.3: Use parameters to change data sources dynamically in Power Query

9.4: Group columns for improved analysis

9.5: Execute text and numeric transformations

9.6: Create a calendar table for time-based analysis

9.7: Add columns using custom formulas and example-based techniques

9.8: Automate reporting with conditional columns

9.9: Utilize pivot and un-pivot functions for data restructuring

9.10: Connect to folders for automated reporting processes

10.1: Perform necessary data cleaning on two datasets for bank reconciliation.

10.2: Merge datasets using a common column (e.g., transaction ID or date) with an appropriate join type.

10.3: Add a new column to calculate the variance between bank balances after merging.

10.4: Clean and merge the Operating Expenses table with the Product Details table using a common column.

10.5: Add a custom column to calculate allocated expenses for each product.

10.6: Summarize allocated expenses in a new Power Query table.

10.7: Present the allocated operating expenses using visuals such as tables, charts, and graphs.

11.1: Bifurcate the annual product-wise budget by considering the percentage of budgeted sales in four user-defined seasons and different weekly sales patterns.

11.2: Load the three tables and calculate seasonal sales for each product.

11.3: Create three parameters and a dynamic table to compute the daily budget for one product.

11.4: Use the “Column by functions” option to dynamically split the annual budget at the daily level.

12.1: Compare 9 months of actual data with 12 months of budget data.

12.2: Calculate the last day of sales using a DAX function.

12.3: Develop a dynamic DAX measure to ensure that budget data is compared for the same period as actual data.

12.4: Add dynamic report titles that include the ending date.

12.5: Display big numbers with enhanced visualization techniques for actual sales, absolute and relevant variance (with up/down arrows), and dynamic color formatting (red/green).

12.6: Create a decomposition tree analysis using field parameters for sales variance and sales variance percentage.

12.7: Use a marketplace visual to create a waterfall chart that shows the journey from budget to actual, highlighting both positive and negative contributors.

13.1: Create measures for maximum point, green maximum point, and red maximum point.

13.2: Utilize advanced column-layout features to provide spacing between categories and series.

13.3: Apply advanced data labels to add user-defined labels with dynamic colors (green and red).

13.4: Incorporate advanced error bars to display both green and red maximum points for IBCS-inspired visuals.

14.1: Understand data modeling concepts and their advantages over lookup functions.

14.2: Recognize the three types of relationships: one-to-one, one-to-many, and many-to-one.

14.3: Understand cardinalities including down side, up-side, and both sides.

14.4: Create inactive relationships between necessary tables.

14.5: Develop measures for days left, due date, invoice date, invoice value, outstanding invoices, and selected date.

14.6: Create dynamic groups in a separate table to categorize invoices into buckets (01–30 days, 31–60 days, 61–90 days, 90+ days).

15.1: Integrate multiple tables such as cash, bank, income, and expenses.

15.2: Create calculated columns or measures to compute KPIs like net cash flow, beginning cash balance, ending cash balance, total cash inflows, and total cash outflows.

16.1: Understand the financial database structure including the chart of accounts and general ledger tables.

16.2: Develop DAX measures to calculate revenue, cost of sales, gross profit, operational expenses, EBITDA, depreciation and amortization, EBIT, tax and interest, and net profit.

16.3: Calculating income statement ratios such as gross profit margin, EBITDA percentage, EBIT percentage, and net profit margin.

16.4: Utilize KPI cards to display income-related and expense-related figures.

17.1: Understand the role of variables in DAX functions.

17.2: Use an income statement template that clearly distinguishes top and bottom lines.

17.3: Set up tables and matrices with appropriate formatting for financial reporting.

17.4: Apply DAX functions to perform financial calculations.

17.5: Integrate and dynamically transform the income statement budget table.

17.6: Choose visuals to display actual figures, previous year data, variances with previous year, budget figures, and variances with budget.

18.1: Understand how filter context can cause incorrect balance sheet calculations.

18.2: Amend the filter context using techniques such as max date, min date, and amended min date calculations.

18.3: Create DAX measures for current and non-current assets, current and non-current liabilities, and owners’ equity.

18.4: Calculate balance sheet ratios such as current ratio, cash ratio, and debt to equity.

18.5: Utilize a balance sheet template to display values in tables and matrices.

19.1: Develop measures to calculate cash flow from operating, investing, and financing activities.

19.2: Use a cash flow statement template to present the calculated values in tables and matrices.