✨ adding BI notes

.vscode/settings.json

@@ -24,10 +24,14 @@
   "python.linting.pylintEnabled": true,
   "git.ignoreLimitWarning": true,
   "cSpell.words": [
+    "AVERAGEX",
     "cvcqv",
     "dtypes",
+    "OLAP",
     "Pylint",
-    "tada"
+    "tada",
+    "TOTALMTD",
+    "TOTALYTD"
   ],
   "cSpell.allowCompoundWords": true,
 }

BI/OLAP.md

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+# Business Intelligence (BI)
+
+## OLAP (Online analytical processing)
+
+- What are the basic analytical operations of OLAP?
+
+1. Roll-up
+2. Drill-down
+3. Slice and dice
+4. Pivot (rotate)
+
+1) Roll-up:
+
+Roll-up is also known as “consolidation” or “aggregation.” The Roll-up operation
+can be performed in 2 ways:
+
+- Reducing dimensions
+- Climbing up concept hierarchy. Concept hierarchy is a system of grouping
+  things based on their order or level.
+
+Example: Moving from cities to countries
+
+2. Drill-down
+
+In drill-down data is fragmented into smaller parts. It is the opposite of the rollup
+process. It can be done via:
+
+- Moving down the concept hierarchy
+- Increasing a dimension
+
+E.g., Moving from quarters to months
+
+3. Slice:
+
+Here, one dimension is selected, and a new sub-cube is created.
+
+E.g., Dimension Time is Sliced with Q1 as the filter.
+
+Dice:
+
+This operation is similar to a slice. The difference in dice is you select 2 or more dimensions that result in the creation of a sub-cube.
+
+4. Pivot
+
+In Pivot, you rotate the data axes to provide a substitute presentation of data.
+
+## Data Analysis Expressions (DAX)
+
+Data Analysis Expressions is the native formula and query language for Microsoft PowerPivot, Power BI Desktop and SQL Server Analysis Services Tabular models.
+
+### Basic Aggregate and Math functions
+
+#### Total Sales, Cost, and Profit
+
+- SUM
+
+```
+total_sales = SUM('TableName'[SalesAmount])
+total_cost = SUM('TableName'[Cost])
+profit = [total_sales] - [total_cost]
+```
+
+- DIVIDE
+
+```
+profit_margin = DIVIDE([profit], [total_sales])
+```
+
+- COUNTROWS (Transactions)
+
+```
+transactions = COUNTROWS('Table')
+```
+
+- Related table count
+
+```
+transactions = COUNTROWS(RELATEDTABLE('Table'))
+```
+
+- Conditional Count
+
+```
+count = CALCULATE(
+    SUM(DISTINCTCOUNT('TableName'[ID])),
+    'TableName'[Type] = 1
+)
+```
+
+#### Month To Date Sales
+
+Month-to-date (MTD): a period starting at the beginning of the current calendar month and
+ending at the current date.
+
+Example: If today is the 15th of the month, and your manager asks you for the month to
+date sales figures, you will want to add your sales from the 1st of the month up to the 14th
+(as the 15th is not complete yet).
+
+- TOTALMTD
+
+```
+mtd_sales = TOTALMTD([total_sales], 'TableName'[date_column])
+```
+
+#### Year To Date Sales
+
+• Year To Date (YTD) sales formulas: the amount of profit (or loss) realized by an investment
+since the first trading day of the current calendar year.
+
+• YTD calculations are commonly used by investors and analysts to assess the performance of
+a portfolio or to compare the recent performance of a group of stocks.
+
+• Using the YTD period sets a common time frame for assessing the performance of securities
+against each other and their benchmarks. A YTD period is also useful for measuring price
+movements relative to other data, such as the economic indicators.
+
+• Example: Year to date value of sales could be the summary of all sales from the 1st of
+January of that year to a specified date.
+
+- TOTALYTD
+
+```
+ytd_sales = TOTALYTD([total_sales], 'TableName'[date_column])
+```
+
+```
+# with optional parameter specifying the fiscal year end date
+ytd_sales = TOTALYTD([total_sales], 'TableName'[date_column], "05/31")
+```
+
+#### Prior Year Sales
+
+• Prior Year Sales formulas: used to track your business's performance by comparing a statistic
+for a select period with the same period from the previous year.
+
+• Example: Let's say your business revenue rose 25% last month. Before you celebrate, check
+that against the income from the same month last year. Maybe your sales usually rise this time
+of year. If sales typically rise 35% this month, then at 25% your revenue is down year-over-year.
+Your business is doing worse, not better.
+
+- SAMEPERIODLASTYEAR
+
+```
+prior_year_profit = CALCULATE([profit], SAMEPERIODLASTYEAR('TableName'[date_column]))
+year_over_year_profit = [profit] - [prior_year_profit]
+lastyear_ytd_sales = CALCULATE([ytd_sales], SAMEPERIODLASTYEAR('TableName'[date_column]))
+```
+
+#### Moving Totals
+
+• The Moving Average (MA) formula: a technique to get an overall idea of the trends in a data
+set; this technique is an average of any subset of numbers.
+
+• The Moving Average is very useful for forecasting long-term trends. You can calculate it for a
+certain period of time.
+
+• For example: If you have sales data for a twenty-year period, you can calculate a five-year
+moving average, a four-year moving average, and so on.
+
+```
+# calculated measure that returns a rolling 12 months total for profit
+rolling_12_months_profit = CALCULATE(
+    [profit],
+    DATESBETWEEN('TableName'[date_column]),
+    NEXTDAY(
+        SAMEPERIODLASTYEAR(
+            LASTDATE('TableName'[date_column])
+        )
+    ),
+    LASTDATE('TableName'[date_column])
+)
+```
+
+```
+# 7 day moving average profit
+seven_day_moving_average = AVERAGEX(
+    FILTER(
+        ALL('TableName'),
+        'TableName'[FullDateAlternateKey] > MAX('TableName'[FullDateAlternateKey] - 7) &&
+        'TableName'[FullDateAlternateKey] <= MAX('TableName'[FullDateAlternateKey])
+    ),
+    [profit]
+)
+```