moving images to an images folder for tidy-up

Author: jlooper

2-Working-With-Data/06-non-relational/README.md

@@ -6,7 +6,7 @@ Data is not limited to relational databases. This lesson focuses on non-relation
 
 Spreadsheets are a popular way to store and explore data because it requires less work to setup and get started. In this lesson you'll learn the basic components of a spreadsheet, as well as formulas and functions. The examples will be illustrated with Microsoft Excel, but most of the parts and topics will have similar names and steps in comparison to other spreadsheet software. 
 
-![An empty Microsoft Excel workbook with two worksheets](parts-of-spreadsheet.png)
+![An empty Microsoft Excel workbook with two worksheets](images/parts-of-spreadsheet.png)
 
 A spreadsheet is a file and will be accessible in the file system of a computer, device, or cloud based file system. The software itself may be browser based or an application that must be installed on a computer or downloaded as an app. In Excel these files are also defined as **workbooks** and this terminology will be used the remainder of this lesson.
 
@@ -18,30 +18,30 @@ With these basic elements of an Excel workbook, we'll use and an example from [M
 
 The spreadsheet file named "InventoryExample" is a formatted spreadsheet of items within an inventory that contains three worksheets, where the tabs are labeled "Inventory List", "Inventory Pick List" and "Bin Lookup". Row 4 of the Inventory List worksheet is the header, which describes the value of each cell in the header column.
 
-![A highlighted formula from an example inventory list in Microsoft Excel](formula-excel.png)
+![A highlighted formula from an example inventory list in Microsoft Excel](images/formula-excel.png)
 
 There are instances where a cell is dependent on the values of other cells to generate its value. The Inventory List spreadsheet keeps track of the cost of every item in its inventory, but what if we need to know the value of everything in the inventory? [**Formulas**](https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263) perform actions on cell data and is used to calculate the cost of the inventory in this example. This spreadsheet used a formula in the Inventory Value column to calculate the value of each item by multiplying the quantity under the QTY header and its costs by the cells under the COST header. Double clicking or highlighting a cell will show the formula. You'll notice that formulas start with an equals sign, followed by the calculation or operation. 
 
-![A highlighted function from an example inventory list in Microsoft Excel](function-excel.png)
+![A highlighted function from an example inventory list in Microsoft Excel](images/function-excel.png)
 
 We can use another formula to add all the values of Inventory Value together to get its total value. This could be calculated by adding each cell to generate the sum, but that can be a tedious task. Excel has [**functions**](https://support.microsoft.com/en-us/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89), or predefined formulas to perform calculations on cell values. Functions require arguments, which are the required values used to perform these calculations. When functions require more than one argument, they will need to be listed in a particular order or the function may not calculate the correct value. This example uses the SUM function, and uses the values of on Inventory Value as the argument to add generate the total listed under row 3, column B (also referred to as B3).
 
 ## NoSQL
 
 NoSQL is an umbrella term for the different ways to store non-relational data and can be interpreted as "non-SQL", "non-relational" or  "not only SQL". These type of database systems can be categorized into 4 types.
 
-![Graphical representation of a key-value data store showing 4 unique numerical keys that are associated with 4 various values](kv-db.png)
+![Graphical representation of a key-value data store showing 4 unique numerical keys that are associated with 4 various values](images/kv-db.png)
 > Source from [Michał Białecki Blog](https://www.michalbialecki.com/2018/03/18/azure-cosmos-db-key-value-database-cloud/)
 
 [Key-value](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores) databases pair unique keys, which are a unique identifier associated with a value. These pairs are stored using a [hash table](https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/) with an appropriate hashing function.
 
 
-![Graphical representation of a graph data store showing the relationships between people, their interests and locations](graph-db.png)
+![Graphical representation of a graph data store showing the relationships between people, their interests and locations](images/graph-db.png)
 > Source from [Microsoft](https://docs.microsoft.com/en-us/azure/cosmos-db/graph/graph-introduction#graph-database-by-example)
 
 [Graph](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#graph-data-stores) databases describe relationships in data and are represented as a collection of nodes and edges. A node represents an entity, something that exists in the real world such as a student or bank statement. Edges represent the relationship between two entities  Each node and edge have properties that provides additional information about each node and edges.
 
-![Graphical representation of a columnar data store showing a customer database with two column families named Identity and Contact Info](columnar-db.png)
+![Graphical representation of a columnar data store showing a customer database with two column families named Identity and Contact Info](images/columnar-db.png)
 
 [Columnar](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores) data stores organizes data into columns and rows like a relational data structure but each column is divided into groups called a column family, where the all the data under one column is related and can be retrieved and changed in one unit. 
 
@@ -75,11 +75,11 @@ You can download and install the emulator [for Windows here](https://aka.ms/cosm
 
 The Emulator launches a browser window, where the Explorer view allows you to explore documents.
 
-![The Explorer view of the Cosmos DB Emulator](cosmosdb-emulator-explorer.png)
+![The Explorer view of the Cosmos DB Emulator](images/cosmosdb-emulator-explorer.png)
 
 If you're following along, click on "Start with Sample" to generate a sample database called SampleDB. If you expand Sample DB by clicking on the arrow you'll find a container called `Persons`, a container holds a collection of items, which are the documents within the container. You can explore the four individual documents under `Items`. 
 
-![Exploring sample data in the Cosmos DB Emulator](cosmosdb-emulator-persons.png)
+![Exploring sample data in the Cosmos DB Emulator](images/cosmosdb-emulator-persons.png)
 
 #### Querying Document Data with the Cosmos DB Emulator
 
@@ -89,7 +89,7 @@ We can also query the sample data by clicking on the new SQL Query button (secon
 
 `SELECT * FROM c where c.age < 40`
 
- ![Running a SELECT query on sample data in the Cosmos DB Emulator to find documents that have an age field value that is less than 40](cosmosdb-emulator-persons-query.png)
+ ![Running a SELECT query on sample data in the Cosmos DB Emulator to find documents that have an age field value that is less than 40](images/cosmosdb-emulator-persons-query.png)
 
 The query returns two documents, notice the age value for each document is less than 40.
 

2-Working-With-Data/06-non-relational/columnar-db.png 2-Working-With-Data/06-non-relational/images/columnar-db.png

@@ -92,33 +92,33 @@ b = pd.Series(["I","like","to","play","games","and","will","not","change"],index
 df = pd.DataFrame([a,b])
 ```
 This will create a horizontal table like this:
-| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
-|---|---|---|---|---|---|---|---|---|---|
-| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
-| 1 | I | like | to | use | Python | and | Pandas | very | much |
+|     | 0   | 1    | 2   | 3   | 4      | 5   | 6      | 7    | 8    |
+| --- | --- | ---- | --- | --- | ------ | --- | ------ | ---- | ---- |
+| 0   | 1   | 2    | 3   | 4   | 5      | 6   | 7      | 8    | 9    |
+| 1   | I   | like | to  | use | Python | and | Pandas | very | much |
 
 We can also use Series as columns, and specify column names using dictionary:
 ```python
 df = pd.DataFrame({ 'A' : a, 'B' : b })
 ```
 This will give us a table like this:
 
-|  | A | B |
-|---|---|---|
-| 0 | 1 | I |
-| 1 | 2 | like |
-| 2 | 3 | to |
-| 3 | 4 | use |
-| 4 | 5 | Python |
-| 5 | 6 | and |
-| 6 | 7 | Pandas |
-| 7 | 8 | very |
-| 8 | 9 | much |
+|     | A   | B      |
+| --- | --- | ------ |
+| 0   | 1   | I      |
+| 1   | 2   | like   |
+| 2   | 3   | to     |
+| 3   | 4   | use    |
+| 4   | 5   | Python |
+| 5   | 6   | and    |
+| 6   | 7   | Pandas |
+| 7   | 8   | very   |
+| 8   | 9   | much   |
 ## 🚀 Challenge
 
 First problem we will focus on is modelling of epidemic spread of COVID-19. In order to do that, we will use the data on the number of infected individuals in different countries, provided by the [Center for Systems Science and Engineering](https://systems.jhu.edu/) (CSSE) at [Johns Hopkins University](https://jhu.edu/). Dataset is available in [this GitHub Repository](https://github.com/CSSEGISandData/COVID-19).
 
-Since we want to demonstrate how to deal with data, we invite you to open [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) and read it from top to bottom. You can also execute cells, and do some challenges that we have leaf for you along the way.
+Since we want to demonstrate how to deal with data, we invite you to open [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) and read it from top to bottom. You can also execute cells, and do some challenges that we have set for you along the way.