ITEC 3610 - Introduction to Information Technology and Applications

• Review syllabus
• Faculty introductions
• Student introductions
• Get Chromebooks
• Ice-Breaker Exercises
• Bruce Bauer's Website: brucebauer.info

• GCFLearnFree.org

Overview of the Internet - Watch: There and Back Again

• Google Docs
• Types of Google Docs
  • Google Documents (similiar to Microsoft Word)
  • Google Sheets (similiar to Microsoft Excel)
  • Google Presentations (similiar to Microsoft Powerpoint)
  • Google Drawings (similiar to Microsoft Paint
  • Google Forms (Online fillable form that populates a Google Sheet)
• Create Your First Doc
• Managing Files
  • Search
  • Sort
  • Filter
  • Create Folder
  • Move, Delete, Rename Files
• Sharing and Collaborating
  • Sharing a file with asmall group
  • Sharing a link
  • Publishing to the internet
  • The owner of a file automatically has all rights to the file
  • Note: - When you create a file, by default it inherits the sharing defined in the containing folder. You can always change the sharing of any file that you own.
• Download and Converting Documents
• Uploading and Converting Documents

• Text Basics
  • Insert, Delete, Cut/Paste, Drag/Drop
  • Special Characters
  • Spelling, Thesaurus
  • Find/Replace
• Formatting Text
  • Font, Size, Color, Italics, Underline, Strikethough, Superscript, Subscript
  • Alignment
  • Line/Paragraph Spacing
  • Hyperlinks
• Tabs, Margins, Indentation
• Headers, Footers, Page Break
• Tables
• Images

• Getting Started with Your Presenation
• Slide Basics
• Text Basics
• Adding Pictures and Shapes
• Adding Transitions and Animations
• Presenting Your Slide Show
• Editting Master Slides and Layouts

Be sure to attent the EIT Career Fair on 3/14/2018. Register at http://ualr.edu/eit/career-fair/

• Getting Started with Google Sheets
  • Naming the Sheet
  • Cell Naming and Cell Range
  • Cell Content
    • Text, Number, Dates
    • Formatting Cells
    • Formulas and Functions
  • Select Cell, Insert Content
  • Copy and Paste
  • Drag/Drop
  • Fill Handle
• Modifying Columns, Rows, and Cells
  • Modify Cell Width and Height, Autosize
  • Insert/Delete Row/Column
  • Cell Naming and Cell Range
  • Drag Row/Colunn
  • Text Wrapping
  • Merge Cells
  • Freezing Rows/Columns
• Formatting Cells
  • Change Font and Size
  • Change Text and Color; Bold; Italic
  • Horizontal/Vertical Text Alignment
  • Cell Borders
  • Number Formats
• Simple Formulas

• Commas Separated Values (.csv) Files - Standard
• Generating Test Data - Mockaroo.com
• Import CSV File or Excel File
  • Check your automatic conversion setting in Settings and make sure that files are automatically converted
  • .CSV files contain data only. They do not contain formulas or formatting. Download this .CSV file and open it with a text editor. Now upload the file into a Google Sheet
  • .XLS or .XLST Excel files can be imported with the formulas and formatting. Try downloading your spreadsheet as a .XLSX file, then uploading and converting the file back to a Google Sheet
• Charts in Google Sheets - Starter File

• Complex Formulas
• Types of Cell References (Go down to the "Challenge" section and make a copy the example file.)

• Using Multiple Sheets
• You can perform calculations using data on another sheet by using the notation: ='SheetName'!A1

• Google Sheets Functions
• Google Sheets Function List
• Starter File
• You can sum an entire column regardless of length using the notation =SUM(A:A) where A is any column name. This way you can continually add data without changing the formula. By putting the sum in a sidebar instead of the bottom of a column, you will not have change your formulas that use the value of the SUM. If you have column headers, you can start from below the headers to the bottom of the spreadsheet using =SUM(A4:A), indicating that you want to sum from column A4 to the bottom of the sheet.

You should have the following assignments completed:

• South University Autorama Sales
• South University Autorama Safety Products
• Ticket Sales
• Schools
• Jewelry (In Progress)

Today's Lecture

• Starter File
• Freezing Rows or Columns
• If Statements
• Conditional Format
• Sparklines
• Sorting and Filters

• In Class - Make a local copy of this Spreadsheet
• String Function List
  • PROPER(text_to_capitalize) - Capitalizes each word in a specified string.
  • VALUE(text) - Converts a string in any of the date, time or number formats that Google Sheets understands into a number.
  • TRIM(text) - Removes leading and trailing spaces in a specified string.
  • TEXT(number, format) - Converts a number into text according to a specified format.
  • SUBSTITUTE(text_to_search, search_for, replace_with, occurrence_number) - Replaces existing text with new text in a string.
  • SPLIT(text, delimiter, split_by_each) - Divides text around a specified character or string, and puts each fragment into a separate cell in the row.
  • SEARCH(search_for, text_to_search, [starting_at]) - Returns the position at which a string is first found within text.
  • RIGHT(string, [number_of_characters]) - Returns a substring from the end of a specified string.
  • REPT(text_to_repeat, number_of_repetitions) - Returns specified text repeated a number of times.
  • REPLACE(text, position, length, new_text) - Replaces part of a text string with a different text string.
  • UPPER(text) - Converts a specified string to uppercase.
  • MID(string, starting_at, extract_length) - Returns a segment of a string.
  • LOWER(text) - Converts a specified string to lowercase.
  • LEN(text) - Returns the length of a string.
  • LEFT(string, [number_of_characters]) - Returns a substring from the beginning of a specified string.
  • FIXED(number, number_of_places, [suppress_separator]) - Formats a number with a fixed number of decimal places.
  • FIND(search_for, text_to_search, starting_at) - Returns the position at which a string is first found within text.
  • EXACT(string1, string2) - Tests whether two strings are identical.
  • DOLLAR(number, number_of_places) - Formats a number into the locale-specific currency format.
  • CONCATENATE(string1, string2) - Appends strings to one another.
  • CLEAN(text) - Returns the text with the non-printable ASCII characters removed.
• Examples of Extracting Strings
• Starter File

• In Class - Make a local copy of this Spreadsheet
• Date Function List
  • NOW() - Returns the current date and time as a date value.
  • TODAY() - Returns the current date as a date value.
  • HOUR(time) - Returns the hour component of a specific time, in numeric format.
  • MINUTE(time) - Returns the minute component of a specific time, in numeric format.
  • SECOND(time) - Returns the second component of a specific time, in numeric format.
  • MONTH(date) - Returns the month of the year a specific date falls in, in numeric format.
  • DAY(date) - Returns the day of the month that a specific date falls on, in numeric format.
  • YEAR(date) - Returns the year specified by a given date.
  • DATE(year, month, day) - Converts a provided year, month, and day into a date.
  • TIME(hour, minute, second) - Converts a provided hour, minute, and second into a time.
  • DATEVALUE(date_string) - Converts a provided date string in a known format to a date value.
  • TIMEVALUE(time_string) - Returns the fraction of a 24-hour day the time represents.
  • WEEKDAY(date, type) - Returns a number representing the day of the week of the date provided.
  • WEEKNUM(date, [type]) - Returns a number representing the week of the year where the provided date falls.
  • DATEDIF - (start_date, end_date, unit) - Calculates the number of days, months, or years between two dates.

• Hints for Cells (Notes)
• Validation
• Protecting Sheets See Example (Make a local copy)
• Using a named range of cells

• In your shared directory, make a copy of the Starter File

• VLookup - Vertical lookup. Searches down the first column of a range for a key and returns the value of a specified cell in the row found.
  • Pros
    • Data does not have to be sorted
    • With sorted data, returns the next smaller value if value not found
  • Cons
    • Breaks if you add a column to the spreadsheet
    • VLOOKUP can only look for data from left to right
    • Easy to make a mistake when counting columns
• Lookup - Looks through a sorted row or column for a key and returns the value of the cell in a result range located in the same position as the search row or column.
  • Pros
    • Does not break if you add a column to the spreadsheet
    • Easier to specify data column
    • Returns the next smaller value if value not found
  • Cons
    • Data has to be sorted
• Index - Returns the content of a cell, specified by row and column offset.
• Match - Returns the relative position of an item in a range that matches a specified value.
• Using Index and Match
  • Pros
    • Does not break if you add a column to the spreadsheet
    • Easier to specify data column
    • Data does not have to be sorted
    • If data is sorted, then the next or previous value can be returned. See search_type in MATCH function.
    • If data is sorted, returns the next smaller value if value not found
    • Better processing speed
  • Cons
    • User may not be familiar with this technique

Identifying Entities

A good database design starts with a list of the data that you want to include in your database and what you want to be able to do with the database later on.

Click to view figure

The types of information that are saved in the database are called 'entities'. These entities exist in four kinds: people, things, events, and locations. Everything you could want to put in a database fits into one of these categories. If the information you want to include doesn't fit into these categories, than it is probably not an entity but a property of an entity, an attribute.

For example, imagine that you are creating a website for a store, what kind of information do you have to deal with? In a store you sell your products to customers. The "Store" is a location; "Sale" is an event; "Products" are things; and "Customers" are people. These are all entities that need to be included in your database.

But what other things are happening when selling a product? A customer comes into the store, approaches the salesperson, asks a question and gets an answer. "Salespersons" also participate, and because salespersons are people, we need a salespersons entity.

Identifying Relationships

The next step is to determine the relationships between the entities and to determine the cardinality of each relationship. The relationship is the connection between the entities, just like in the real world: what does one entity do with the other, how do they relate to each other? For example, customers buy products, products are sold to customers, a sale comprises products, a sale happens in a store.

The cardinality shows how much of one side of the relationship belongs to how much of the other side of the relationship. First, you need to state for each relationship, how much of one side belongs to exactly 1 of the other side. For example: How many customers belong to 1 sale?; How many sales belong to 1 customer?; How many sales take place in 1 store?

Click to view figure

Did we mention all relationships? There are four entities and each entity has a relationship with every other entity, so each entity must have three relationships, and also appear on the left end of the relationship three times. Above, 12 relationships were mentioned, which is 4*3, so we can conclude that all relationships were mentioned.

Now we'll put the data together to find the cardinality of the whole relationship. In order to do this, we'll draft the cardinalities per relationship. To make this easy to do, we'll adjust the notation a bit, by noting the 'backward'-relationship the other way around:

• Customers → Sales; 1 customer can buy something several times
• Sales → Customers; 1 sale is always made by 1 customer at the time

The second relationship we will turn around so it has the same entity order as the first. Please notice the arrow that is now faced the other way!

• Customers ← Sales; 1 sale is always made by 1 customer at the time

Cardinality exists in four types: one-to-one, one-to-many, many-to-one, and many-to-many. In a database design this is indicated as: 1:1, 1:N, M:1, and M:N. To find the right indication just leave the '1'. If there is a 'many' on the left side, this will be indicated with 'M', if there is a 'many' on the right side it is indicated with 'N'.

• Customers → Sales; 1 customer can buy something several times; 1:N.
• Customers ← Sales; 1 sale is always made by 1 customer at the time; 1:1.

The true cardinality can be calculated through assigning the biggest values for left and right, for which 'N' or 'M' are greater than '1'. In this example, in both cases there is a '1' on the left side. On the right side, there is a 'N' and a '1', the 'N' is the biggest value. The total cardinality is therefore '1:N'. A customer can make multiple 'sales', but each 'sale' has just one customer.

If we do this for the other relationships too, we'll get:

• Customers → Sales; → 1:N
• Customers → Products; → M:N
• Customers → Stores; → M:N
• Sales → Products; → M:N
• Stores → Sales; → 1:N
• Stores → Products; → M:N

Glossary

• Attributes - detailed data about an entity, such as price, length, name
• Cardinality - the relationship between two entities, in figures. For example, a person can place multiple orders.
• Entities - abstract data that you save in a database. For example: customers, products.
• Foreign key (FK) - a referral to the Primary Key of another table. Foreign Key-columns can only contain values that exist in the Primary Key column that they refer to.
• Key - a key is used to point out records. The most well-known key is the Primary Key (see Primary Key).
• Normalization - A flexible data model needs to follow certain rules. Applying these rules is called normalizing.
• Primary key (PK) - one or more columns within a table that together form a unique combination of values by which each record can be pointed out separately. For example: customer numbers, or the serial number of a product.
• Primary key/Foreign key (PF) - The foreign key of an entity can also be part of the primary key, in that case the attribute will be indicated with 'PF' behind its name. This is usually the case with the link-entities, because you usually link two instances only once together.

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Cardinality - Crowsfeet Notation

Relationships have two indicators. The first one (often called multiplicity) refers to the maximum number of times that an instance of one entity can be associated with instances in the related entity. It can be one or many.

The second describes the minimum number of times one instance can be related to others. It can be zero or one, and accordingly describes the relationship as optional or mandatory.

The combination of these two indicators is always in a specific order. Placed on the outside edge of the relationship, the symbol of multiplicity comes first. The symbol indicating whether the relationship is mandatory or optional is shown after the symbol of multiplicity.

Click to view figure

In crow’s foot notation:

• A multiplicity of one and a mandatory relationship is represented by a straight line perpendicular to the relationship line.
• A multiplicity of many is represented by the three-pronged ‘crow-foot’ symbol.
• An optional relationship is represented by an empty circle.

This result in four possible relationships:

• zero or many
• one or many
• one and only one
• zero or one

A good database design starts with a list of the data that you want to include in your database and what you want to be able to do with the database later on.

Click to view figure

Between the entities there may be a mutual dependency. This means that the one item cannot exist if the other item does not exist. For example, there cannot be a sale if there are no customers, and there cannot be a sale if there are no products.

The relationships Sales → Customers, and Sales → Products are mandatory, but the other way around this is not the case. A customer can exist without sale, and also a product can exist without sale. This is of importance for the next step.

Redundant Relationships

Sometimes in your model you will get a 'redundant relationship'. These are relationships that are already indicated by other relationships, although not directly.

In the case of our example there is a direct relationships between customers and products. But there are also relationships from customers to sales and from sales to products, so indirectly there already is a relationship between customers and products through sales. The relationship 'Customers ↔ Products' is made twice, and one of them is therefore redundant. In this case, products are only purchased through a sale, so the relationships 'Customers ↔ Products' can be deleted. The model will then look like this:

Click to view figure

Solving Many-to-Many Relationships

Many-to-many relationships (M:N) are not directly possible in a database. What a M:N relationship says is that a number of records from one table belongs to a number of records from another table. Somewhere you need to save which records these are and the solution is to split the relationship up in two one-to-many relationships.

This can be done by creating a new entity that is in between the related entities. In our example, there is a many-to-many relationship between sales and products. This can be solved by creating a new entity: sales-products. This entity has a many-to-one relationship with Sales, and a many-to-one relationship with Products. In logical models this is called an associative entity and in physical database terms this is called a link table or junction table.

Click to view figure

In the example there are two many-to-many relationships that need to be solved: 'Products ↔ Sales', and 'Products ↔ Stores'. For both situations there needs to be created a new entity, but what is that entity?

For the Products ↔ Sales relationship, every sale includes more products. The relationship shows the content of the sale. In other words, it gives details about the sale. So the entity is called 'Sales details'. You could also name it 'sold products'.

The Products ↔ Stores relationship shows which products are available in which the Stores, also known as 'stock'. Our model would now look like this:

Click to view figure

Identifying Attributes

The data elements that you want to save for each entity are called 'attributes'.

About the products that you sell, you want to know, for example, what the price is, what the name of the manufacturer is, and what the type number is. About the customers you know their customer number, their name, and address. About the Stores you know the location code, the name, the address. Of the sales you know when they happened, in which store, what products were sold, and the sum total of the sale. Of the Salesperson you know his staff number, name, and address. What will be included precisely is not of importance yet; it is still only about what you want to save.

Derived Data

Derived data is data that is derived from the other data that you have already saved. In this case the 'sum total' is a classical case of derived data. You know exactly what has been sold and what each product costs, so you can always calculate how much the sum total of the sales is. So really it is not necessary to save the sum total.

So why is it saved here? Well, because it is a sale, and the price of the product can vary over time. A product can be priced at $10 today and at $8 next month, and for your administration you need to know what it cost at the time of the sale, and the easiest way to do this is to save it here.

Glossary

• Attributes - detailed data about an entity, such as price, length, name
• Cardinality - the relationship between two entities, in figures. For example, a person can place multiple orders.
• Entities - abstract data that you save in a database. For example: customers, products.
• Foreign key (FK) - a referral to the Primary Key of another table. Foreign Key-columns can only contain values that exist in the Primary Key column that they refer to.
• Key - a key is used to point out records. The most well-known key is the Primary Key (see Primary Key).
• Normalization - A flexible data model needs to follow certain rules. Applying these rules is called normalizing.
• Primary key (PK) - one or more columns within a table that together form a unique combination of values by which each record can be pointed out separately. For example: customer numbers, or the serial number of a product.
• Primary key/Foreign key (PF) - The foreign key of an entity can also be part of the primary key, in that case the attribute will be indicated with 'PF' behind its name. This is usually the case with the link-entities, because you usually link two instances only once together.

Primary Keys

A primary key (PK) is one or more data attributes that uniquely identify an entity. A key that consists of two or more attributes is called a composite key. All attributes part of a primary key must have a value in every record (which cannot be left empty) and the combination of the values within these attributes must be unique in the table.

In the example there are a few obvious candidates for the primary key. Customers all have a customer number, products all have a unique product number and the sales have a sales number. Each of these data is unique and each record will contain a value, so these attributes can be a primary key. Often an integer column is used for the primary key so a record can be easily found through its number.

Link-entities usually refer to the primary key attributes of the entities that they link. The primary key of a link-entity is usually a collection of these reference-attributes. For example in the Sales_details entity we could use the combination of the PK's of the sales and products entities as the PK of Sales_details. In this way we enforce that the same product (type) can only be used once in the same sale. Multiple items of the same product type in a sale must be indicated by the quantity.

In the ERD the primary key attributes are indicated by the text 'PK' behind the name of the attribute. In the example only the entity 'store' does not have an obvious candidate for the PK, so we will introduce a new attribute for that entity: storenumber.

Foreign Keys

The Foreign Key (FK) in an entity is the reference to the primary key of another entity. In the ERD that attribute will be indicated with 'FK' behind its name. The foreign key of an entity can also be part of the primary key, in that case the attribute will be indicated with 'PF' behind its name. This is usually the case with the link-entities, because you usually link two instances only once together (with 1 sale only 1 product type is sold 1 time).

If we put all link-entities, PK's and FK's into the ERD, we get the model as shown below. Please note that the attribute 'products' is no longer necessary in 'Sales', because 'sold products' is now included in the link-table. In the link-table another field was added, 'quantity', that indicates how many products were sold. The quantity field was also added in the stock-table, to indicate how many products are still in store.

Click to view figure

Defining the Attribute's Data Type

Now it is time to figure out which data types need to be used for the attributes. There are a lot of different data types. A few are standardized, but many databases have their own data types that all have their own advantages. Some databases offerthe possibility to define your own data types, in case the standard types cannot do the things you need.

Okay, time for a crash course:

• Crash Course in Binary Numbers
• ASCII Character Set
• Unicode Character Set

The standard data types that every database knows, and are most-used, are: CHAR, VARCHAR, TEXT, FLOAT, DOUBLE, and INT. Normally, we use signed integers instead of unsigned integers.

Text:

• CHAR(length) - includes text (characters, numbers, punctuations...). CHAR has as characteristic that it always saves a fixed amount of positions. If you define a CHAR(10) you can save up to ten positions maximum, but if you only use two positions the database will still save 10 positions. The remaining eight positions will be filled by spaces (max 255 characters).
• VARCHAR(length) - includes text (characters, numbers, punctuation...). VARCHAR is the same as CHAR, the difference is that VARCHAR only takes as much space as necessary (max 255 characters).
• TEXT - can contain large amounts of text (max 65,535 characters).
• MEDIUMTEXT - can contain large amounts of text (max 16,777,215 characters).
• LONGTEXT - can contain large amounts of text (max 4,294,967,295 characters).

Numbers:

• TINYINT - contains a positive or negative whole number -128 to 127 SIGNED or 0 to 255 UNSIGNED (1 byte)
• SMALLINT - contains a positive or negative whole number -32768 to 32767 SIGNED or 0 to 65535 UNSIGNED (2 bytes)
• MEDIUMINT - contains a positive or negative whole number -8388608 to 8388607 SIGNED or 0 to 16777215 UNSIGNED (3 bytes)
• INT - contains a positive or negative whole number -2147483648 to 2147483647 SIGNED or 0 to 4294967295 UNSIGNED (4 bytes)
• BIGINT - contains a positive or negative whole number -9223372036854775808 to 9223372036854775807 SIGNED or 0 to 18446744073709551615 UNSIGNED (8 bytes)
• FLOAT(size,d) - A number with a floating decimal point. The maximum number of digits may be specified in the size parameter. The maximum number of digits to the right of the decimal point is specified in the d parameter (4 bytes, approximately 7 significant digits).
• DOUBLE(size,d) - A number with a floating decimal point. The maximum number of digits may be specified in the size parameter. The maximum number of digits to the right of the decimal point is specified in the d parameter (8 bytes, approximately 14 significant digits).

Date and Time:

• DATE - A date. Format: YYYY-MM-DD Note: The supported range is from '1000-01-01' to '9999-12-31' (3 bytes)
• DATETIME - A date and time combination. Format: YYYY-MM-DD HH:MI:SS Note: The supported range is from '1000-01-01 00:00:00' to '9999-12-31 23:59:59' (5 bytes)
• TIMESTAMP - number of seconds since the Unix epoch ('1970-01-01 00:00:00' UTC). Format: YYYY-MM-DD HH:MI:SS Note: The supported range is from '1970-01-01 00:00:01' UTC to '2038-01-09 03:14:07' UTC (4 bytes). In an INSERT or UPDATE query, the TIMESTAMP automatically set itself to the current date and time.
• TIME - A time. Format: HH:MI:SS Note: The supported range is from '-838:59:59' to '838:59:59' (3 bytes)
• YEAR - A year in two-digit or four-digit format. Note: Values allowed in four-digit format: 1901 to 2155. Values allowed in two-digit format: 70 to 69, representing years from 1970 to 2069 (1 byte)

Other types:

• BLOB - for binary data such as files (max 65,535 characters).
• MEDIUMBLOB - for binary data such as files (max 16,777,215 characters).
• LARGEBLOB - for binary data such as files (max 4,294,967,295 characters).

For our example the data types are as follows:

Click to view figure

Glossary

• Attributes - detailed data about an entity, such as price, length, name
• Cardinality - the relationship between two entities, in figures. For example, a person can place multiple orders.
• Entities - abstract data that you save in a database. For example: customers, products.
• Foreign key (FK) - a referral to the Primary Key of another table. Foreign Key-columns can only contain values that exist in the Primary Key column that they refer to.
• Key - a key is used to point out records. The most well-known key is the Primary Key (see Primary Key).
• Normalization - A flexible data model needs to follow certain rules. Applying these rules is called normalizing.
• Primary key (PK) - one or more columns within a table that together form a unique combination of values by which each record can be pointed out separately. For example: customer numbers, or the serial number of a product.
• Primary key/Foreign key (PF) - The foreign key of an entity can also be part of the primary key, in that case the attribute will be indicated with 'PF' behind its name. This is usually the case with the link-entities, because you usually link two instances only once together.

Normalization

Normalization makes your data model flexible and reliable. It does generate some overhead because you usually get more tables, but it enables you to do many things with your data model without having to adjust it.

Normalization, the First Form

The first form of normalization states that there may be no repeating groups of columns in an entity. We could have created an entity 'sales' with attributes for each of the products that were bought. This would look like this:

Click to view figure

What is wrong about this is that now only 3 products can be sold. If you would have to sell 4 products, than you would have to start a second sale or adjust your data model by adding 'product4' attributes. Both solutions are unwanted. In these cases you should always create a new entity that you link to the old one via a one-to-many relationship.

Click to view figure

Normalization, the Second Form

The second form of normalization states that all attributes of an entity should be fully dependent on the whole primary key. This means that each attribute of an entity can only be identified through the whole primary key. Suppose we had the date in the Sales_details entity:

Click to view figure

This entity is not according the second normalization form, because in order to be able to look up the date of a sale, I do not have to know what is sold (productnumber), the only thing I need to know is the sales number. This was solved by splitting up the tables into the sales and the Sales_details table:

Click to view figure

Now each attribute of the entities is dependent on the whole PK of the entity. The date is dependent on the sales number, and the quantity is dependent on the sales number and the sold product.

Normalization, the Third Form

The third form of normalization states that all attributes need to be directly dependent on the primary key, and not on other attributes. This seems to be what the second form of normalization states, but in the second form is actually stated the opposite. In the second form of normalization you point out attributes through the PK, in the third form of normalization every attribute needs to be dependent on the PK, and nothing else.

Click to view figure

In this case the price of a loose product is dependent on the ordering number, and the ordering number is dependent on the product number and the sales number. This is not according to the third form of normalization. Again, splitting up the tables solves this.

Click to view figure

Normalization, More Forms

There are more normalization forms than the three forms mentioned above, but those are not of great interest for the average user. These other forms are highly specialized for certain applications. If you stick to the design rules and the normalization, you will create a design that works great for most applications.

Normalized Data Model

If you apply the normalization rules, you will find that the 'manufacturer' in the product table should also be a separate table:

Click to view figure

Glossary

• Attributes - detailed data about an entity, such as price, length, name
• Cardinality - the relationship between two entities, in figures. For example, a person can place multiple orders.
• Entities - abstract data that you save in a database. For example: customers, products.
• Foreign key (FK) - a referral to the Primary Key of another table. Foreign Key-columns can only contain values that exist in the Primary Key column that they refer to.
• Key - a key is used to point out records. The most well-known key is the Primary Key (see Primary Key).
• Normalization - A flexible data model needs to follow certain rules. Applying these rules is called normalizing.
• Primary key (PK) - one or more columns within a table that together form a unique combination of values by which each record can be pointed out separately. For example: customer numbers, or the serial number of a product.
• Primary key/Foreign key (PF) - The foreign key of an entity can also be part of the primary key, in that case the attribute will be indicated with 'PF' behind its name. This is usually the case with the link-entities, because you usually link two instances only once together.

• Spring Break

• Spring Break

Files you will need for this exercise:

• Orders.csv
• OrderDetails.csv
• Suppliers.csv
• Products.csv
• Categories.csv
• Customers.csv
• Shippers.csv
• Employees.csv
• EmployeesTerritories.csv
• Territories.csv
• Region.csv
• Suppliers.csv

When analyzing data, you want to develop a spreadsheet that can be used for different periods of data. Often you get data monthly, and you would like to import the data without having to re-enter the calculations.

In this analysis, we are going to look at the number of products sold and the dollar amount of the products sold. Look at your ERD diagram and see the OrderDetailt table. It has the counts and unit price of each item sold.

1. Create a spreadsheet called "Advanced Analysis"
2. Create a sheet called "Report"
3. Create a sheet called "Temp"
4. Import the OrderDetails.csv file into a sheet called OrderDetails

As previously stated, don't want to do any direct calculations in the data, so we will make a copy of the data in the Temp sheet. Enter the following in the Temp Sheet

1. Cell A1 - OrderDetails.OrderID
2. Cell A2 - =OrderDetails!A2
3. Cell B1 - OrderDetails.ProductID
4. Cell B2 - =OrderDetails!B2
5. Cell C1 - OrderDetails.UnitPrice
6. Cell C2 - =OrderDetails!C2
7. Cell D1 - OrderDetails.Quantity
8. Cell D2 - =OrderDetails!D2
9. Cell E1 - OrderDetails.Discount
10. Cell E2 - =OrderDetails!E2

Now we will calculate the Amount of the Order Detail. It is the Quantity times the Unit Price minus the discount:

1. Cell F1 - OrderDetails.Amount
2. Cell F2 - =(C2*D2) - ((C2*D2)*E2)

Let's work on the category of the product. We already have the Product ID (Column B in Temp sheet). We can then go to the Products sheet, lookup a product using the Product ID (Colunn A in Products sheet) and find the CategoryID (Column D in the Products sheet). The CategoryID is now in column G of the Temp sheet.

1. Import the Products.csv file into a sheet called Products
2. Cell G1 - Product.CategoryID
3. Cell G2 - =INDEX(Products!$D:$D,MATCH(B2,Products!$A:$A))

Now that we have the CategoryID in column G of the Temp sheet, we can find the CategoryName. We can then go to the Categories sheet, lookup a category using the CategoryID (Colunn G in Categories sheet) and find the CategoryName (Column B in the Categories sheet). The CategoryName is now in column H of the Temp sheet.

1. Import the Categories.csv file into a sheet called Categories
2. Cell H1 - Categories.CategoryName
3. Cell H2 - =INDEX(Categories!$B:$B,MATCH(G2,Categories!$A:$A))

Now duplicate all of your calculations in the Temp sheet, down to row 2156.

Let's put together the body of the report. Go to the Report sheet and enter the following:

1. Cell A1 - Category
2. Cell B1 - Units
3. Cell C1 - Sales
4. Cell D1 - Sales Rank
5. Cell A2 - =SORT(Categories!B2:B,1,true)
6. Cell B2 - =SUMIFS(Temp!$D$2:$D,temp!$H$2:$H,"="&A2)
7. Cell C2 - =SUMIFS(Temp!$F$2:$F,temp!$H$2:$H,"="&A2)
8. Cell D2 - =RANK(C2,$C$2:$C) - Also, add a conditional format that highlights the 1st, 2nd, and 3rd highest ranking
9. Duplicate the formulas in B2:D2 through B9:D9
10. Cell F8 - Total Units
11. Cell G8 - =SUM($B$2:$B)
12. Cell F9 - Total Sales
13. Cell G9 - =SUM($C$2:$C)

We have a great looking report, but now we would like to add a date range based on the order date. We can go to the Temp sheet, lookup a an order using the OrderID (Column A in Orders sheet) and find the OrderDate (Column D in the Orders sheet). The OrderDate is now in column I of the Temp sheet.

1. Import the Orders.csv file into a sheet called Orders
2. Cell I1 - Orders.OrderDate
3. Cell I2 - =INDEX(Orders!$D:$D,MATCH(A2,Orders!$A:$A))
4. Duplicate all of your calculations in the Temp sheet, down to row 2156.

Let's go back to the Report Sheet and add the following to use the OrderDate

1. Cell F2 - Start Date
2. Cell G2 - 7/1/1996 - add Validation to ensure that a date has been entered
3. Cell F3 - End Date
4. Cell G3 - 7/1/1997 - add Validation to ensure that a date has been entered
5. Cell B2 - =SUMIFS(Temp!$D$2:$D,Temp!$H$2:$H,"="&A2,Temp!$I$2:$I,">="&$G$2,Temp!$I$2:$I,"<="&G$3)
6. Cell C2 - =SUMIFS(Temp!$F$2:$F,Temp!$H$2:$H,"="&A2,Temp!$I$2:$I,">="&$G$2,Temp!$I$2:$I,"<="&G$3)

Almost done. We would also limit the data to specific customer. We can go to the Temp sheet, lookup a an order using the OrderID (Column A in Orders sheet) and find the CustomerID (Column B in the Orders sheet). The CustomerID is now in column J of the Temp sheet. While it is nice to have the CustomerID, it would be more readable to have the CompanyName. We can go to the Temp sheet, lookup an Customer using the CustomerID (Column A in Customers sheet) and find the CompanyName (Column B in the Customers sheet). The CompanyName is now in column K of the Temp sheet.

1. Cell J1 - Orders.CustomerID
2. Cell J2 - =INDEX(Orders!$B:$B,MATCH(A2,Orders!$A:$A))
3. Import the Customers.csv file into a sheet called Customers
4. Cell K1 - Customers.CompanyName
5. Cell K2 - =INDEX(Customers!$B:$B,MATCH(J2,Customers!$A:$A))
6. Duplicate all of your calculations in the Temp sheet, down to row 2156.
7. Report Sheet Cell F4 - Customer Name
8. Report Sheet Cell G4 - Create a data validation dropdown using a list of value from Temp!K2:K2156
9. Report Sheet Cell B2 - =SUMIFS(Temp!$D$2:$D,Temp!$H$2:$H,"="&A2,Temp!$I$2:$I,">="&$G$2,Temp!$I$2:$I,"<="&G$3,Temp!$K$2:$K,"="&$G$4)
10. Report Sheet Cell C2 - =SUMIFS(Temp!$F$2:$F,Temp!$H$2:$H,"="&A2,Temp!$I$2:$I,">="&$G$2,Temp!$I$2:$I,"<="&G$3,temp!$K$2:$K,"="&$G$4)

Everything looks good, except that the dropdown for CompanyNames are not sorted. Let's create a sorted column:

1. Cell L1 - Customers.SortedCompanyName
2. Cell K2 - =Sort(K2:K,1,true)
3. Duplicate all of your calculations in the Temp sheet, down to row 2156.
4. Report Sheet Cell G4 - Modify a data validation dropdown using a list of value from Temp!L2:L2156

One final enhancement. Add a sheet called "Chart". Create a pie chart that displays the categories and sales amount.

Practice Assessment Solution

1. Create a spreadsheet called "Practice Assessment Solution"
2. Create a sheet called "Report"
3. Create a sheet called "Temp"
4. Import the OrderDetails.csv file into a sheet called OrderDetails

Order Detail Calculations

1. Cell A1 - OrderDetails.OrderID
2. Cell A2 - =OrderDetails!A2
3. Cell B1 - OrderDetails.ProductID
4. Cell B2 - =OrderDetails!B2
5. Cell C1 - OrderDetails.UnitPrice
6. Cell C2 - =OrderDetails!C2
7. Cell D1 - OrderDetails.Quantity
8. Cell D2 - =OrderDetails!D2
9. Cell E1 - OrderDetails.Discount
10. Cell E2 - =OrderDetails!E2
11. Cell F1 - OrderDetails.Amount
12. Cell F2 - =(C2*D2) - ((C2*D2)*E2)

Use Orders sheet to get the EmployeeID.

1. Import the Orders.csv file into a sheet called Orders
2. Cell G1 - Orders.Employee
3. Cell G2 - =INDEX(Orders!$C:$C,MATCH(A2,Orders!$A:$A))

Use Employees sheet to get the employee's LastName.

1. Import the Employees.csv file into a sheet called Employees
2. Cell H1 - Employee.LastName
3. Cell H2 - =INDEX(Employees!$B:$B,MATCH(G2,Employees!$A:$A))

Use Orders sheet to get the Order Date

1. Cell I1 - Orders.OrderDate
2. Cell I2 - =INDEX(Orders!$D:$D,MATCH(A2,Orders!$A:$A))

Use Products sheet to get the ProductName.

1. Import the Products.csv file into a sheet called Products
2. Cell J1 - Products.ProductName
3. Cell J2 - =INDEX(Products!$B:$B,MATCH(B2,Products!$A:$A))
4. Cell K1 - Products.SortedProductName
5. Cell k2 - =SORT(J2:J,1,true)

Duplicate Columns A-J in the Temp sheet, down to row 2156.

Report Sheet:

1. Cell A1 - Category
2. Cell B1 - Units
3. Cell C1 - Sales
4. Cell D1 - Sales Rank
5. Cell A2 - =Sort(Employees!B2:B,1,true)
6. Cell B2 - =SUMIFS(Temp!$D$2:$D,Temp!$H$2:$H,"="&A2,Temp!$I$2:$I,">="&$G$2,Temp!$I$2:$I,"<="&$G$3,Temp!$J$2:$J,"="&$G$4)
7. Cell C2 - =SUMIFS(Temp!$F$2:$F, Temp!$H$2:$H,"="&A2,Temp!$I$2:$I,">="&$G$2,Temp!$I$2:$I,"<="&$G$3,Temp!$J$2:$J,"="&$G$4)
8. Cell D2 - =RANK(C2,$C$2:$C) - Also, add a conditional format that highlights the 1st, 2nd, and 3rd highest ranking
9. Duplicate the formulas in B2:D2 through B9:D9
10. Cell F2 - Starting Date
11. Cell G2 - 7/1/1996
12. Cell F3 - Ending Date
13. Cell G3 - 7/1/1997
14. Cell F4 - Product
15. Report Sheet Cell G4 - Modify a data validation dropdown using a list of value from Temp!K2:K2156
16. Cell F8 - Total Units
17. Cell G8 - =SUM($B$2:$B)
18. Cell F9 - Total Sales
19. Cell G9 - =SUM($C$2:$C)

One final enhancement. Add a sheet called "Chart". Create a pie chart that displays the categories and sales amount.

Files you will need for this exercise:

• Album.csv
• Artist.csv
• Customer.csv
• Employee.csv
• Genre.csv
• Invoice.csv
• InvoiceItem.csv
• MediaType.csv
• Playlist.csv
• PlaylistTrack.csv
• Track.csv

• Login to you email and locate the invitation to join Cloud 9. Click on the link.
• You will be asked to enter a username, email, and password
• Click on add a new workspace. Give the workspace a name of itecsapphire. Use a type of "PHP, Apache, and MySQL"
• Note the files in your directory structure. Since Apache is running, you can access the webserver at https://<yourworkspace>-<yourusername>.c9users.io, such as https://itecsapphire-blbauer.c9users.io
• On the "BASH" tab, enter the following: see https://community.c9.io/t/setting-up-phpmyadmin/1723 for more documentation.
  • mysql-ctl install - This will install the MySQL database. Be sure you run this only one time, else it will delete all of the MySQL databases. You will get a prompt, but just be aware of what it is doing.
  • phpmyadmin-ctl install - This will install phpmyadmin, which is a web tool to administer the MySQL database.
  • mysql-ctl start - Make sure that MySQL is started
  • You can access the control panel at https://<yourworkspace>-<yourusername>.c9users.io/phpmyadmin, such as https://itecsapphire-blbauer.c9users.io/phpmyadmin. When prompted for credentials, use your username and a blank password.
• Do not delete any of the existing databases in MySQL. They are needed to run Cloud 9 and MySQL.
• To close your workspace, click on your avatar and select logoff.

Review the following:

• SQL Create Database
• SQL Drop Database
• SQL Create Table
• SQL Drop Table
• SQL Alter Table
• SQL Constraints
• SQL Not Null
• SQL Unique
• SQL Primary Key
• SQL Foreign Key
• SQL Check
• SQL Default
• SQL Create Index
• SQL Auto Increment
• SQL Dates
• SQL DB Data Types
• Mockaroo - a great way to generate test data

• SQL Home
• SQL Introduction
• SQL Syntax
• SQL Select
• SQL Distinct
• SQL Where
• SQL And/Or
• SQL Order By
• SQL Select Top
• SQL Like
• SQL Wildcards
• SQL In Operator
• SQL Between Operator

Hint: When using phpMyAdmin, use the "back tic" mark (key is located to the left of the Number 1 on your keyboard) when the table name or the column name contains a blank, as in:

• Select * from `Order Details`

Try to create the following SQL statements

• Find the customers that are in Stuttgart Germany or Berlin Germany. Show the Customer Name and Contact Name. Sort by city, then contact name
• Find the products that are packaged 24 to a box and has a price greater than $15 and less than $20. Show the Product Name and Price. Sort by descending price.
• Find the Employees that have a BA and were born beform 1970. Sort by Last Name then First Name. Show the Name of the Employee

• SQL Insert Into
• SQL Select Into
• SQL Insert Into Select
• SQL Update
• SQL Delete

Try to create the following SQL Statements:

• Insert a new employee, John Smith, born on July 4, 1976
• Update John Smith's Notes Field with "Graduated BA Accounting from UALR"
• Delete John Smith from the Employees

• SQL Joins
• SQL Inner Join
• SQL Left Join - Remember that you get ALL of the records from the left argument
• SQL Right Join - Remember that you get ALL of the records from the right argument
• SQL Null Values

Try to create the following SQL Statements:

• Join all tables related to an Order
• Find all of the Customers that have not made an order

• SQL Min Max Functions
• SQL Count, Sum, and Average Functions
• SQL Group By
• SQL Having

Try to create the following SQL Statements:

• Find the Dates where more than one order was made
• Calculate the Total for Each Order
• Calculate the Total Products Sold for Each Day in December 1996

• Speech Workshop

• Speeches