Description Master Index of EDGAR Dissemination FeedL.docx

14|edgar/data/1000045/0001037389-18-000160.txt
1000045|NICHOLAS FINANCIAL INC|SC 13G|2018-02-
09|edgar/data/1000045/0001258897-18-001316.txt
13|edgar/data/1000045/0000315066-18-001444.txt
1000097|KINGDON CAPITAL MANAGEMENT, L.L.C.|13F-
HR|2018-02-14|edgar/data/1000097/0000919574-18-001804.txt
1000097|KINGDON CAPITAL MANAGEMENT, L.L.C.|SC
13G/A|2018-01-02|edgar/data/1000097/0000919574-18-
000008.txt
13G/A|2018-02-14|edgar/data/1000097/0000919574-18-
001760.txt
13G/A|2018-02-14|edgar/data/1000097/0000919574-18-
001765.txt
13G/A|2018-02-14|edgar/data/1000097/0000919574-18-
001773.txt
13G/A|2018-02-14|edgar/data/1000097/0000919574-18-
001777.txt
13G/A|2018-02-14|edgar/data/1000097/0000919574-18-
001785.txt
13G|2018-02-14|edgar/data/1000097/0000919574-18-001790.txt
1000177|NORDIC AMERICAN TANKERS Ltd|6-K|2018-02-
28|edgar/data/1000177/0000919574-18-002148.txt
1000177|NORDIC AMERICAN TANKERS Ltd|CORRESP|2018-
01-19|edgar/data/1000177/0000919574-18-000510.txt
02-08|edgar/data/1000177/0000919574-18-000929.txt
02-22|edgar/data/1000177/0000919574-18-001989.txt

# Program Version 3.0.138
CREATE DATABASE IF NOT EXISTS `class`;
USE `class`;
#
# Table structure for table 'Department'
#
DROP TABLE IF EXISTS `Department`;
CREATE TABLE `Department` (
`Dept_Code` VARCHAR(3) NOT NULL,
`Dept_Name` VARCHAR(20),
INDEX (`Dept_Code`),
PRIMARY KEY (`Dept_Code`)
) ENGINE=myisam DEFAULT CHARSET=utf8;

SET autocommit=1;
#
# Dumping data for table 'Department'
#
INSERT INTO `Department` (`Dept_Code`, `Dept_Name`)
VALUES ('Act', 'Accounting');
VALUES ('Exe', 'Executive');
VALUES ('Fin', 'Finance');
VALUES ('Mkt', 'Marketing');
VALUES ('Per', 'Personnel');
VALUES ('Sal', 'Sales');
VALUES ('Shp', 'Shipping');
# 7 records

#
# Table structure for table 'Employee'
#
DROP TABLE IF EXISTS Èmployee`;
CREATE TABLE Èmployee` (
Èmp_ID` INTEGER NOT NULL,
Èmp_FirstName` VARCHAR(10),
Èmp_LastName` VARCHAR(10),
`Dept_Code` VARCHAR(3),
Èmp_HireDate` DATETIME,
Èmp_CreditLimit` DECIMAL(19,4),
Èmp_Phone` VARCHAR(4),
Èmp_MgrID` INTEGER,
INDEX (`Dept_Code`),
INDEX (Èmp_ID`),
INDEX (Èmp_MgrID`),

PRIMARY KEY (Èmp_ID`)
SET autocommit=1;
#
# Dumping data for table 'Employee'
#
INSERT INTO Èmployee` (Èmp_ID`, Èmp_FirstName`,
Èmp_LastName`, `Dept_Code`, Èmp_HireDate`,
Èmp_CreditLimit`, Èmp_Phone`, Èmp_MgrID`) VALUES
(201, 'Susan', 'Brown', 'Exe', '1992-06-01 00:00:00', 30, '3484',
NULL);
(202, 'Jim', 'Kern', 'Sal', '1995-08-15 00:00:00', 26, '8722', 201);
(203, 'Martha', 'Woods', 'Shp', '1997-02-01 00:00:00', 25, '7591',
201);

(204, 'Ellen', 'Owens', 'Sal', '1996-07-01 00:00:00', 15, '6830',
202);
(205, 'Henry', 'Perkins', 'Sal', '1998-03-01 00:00:00', 25, '5286',
202);
(206, 'Carol', 'Rose', 'Act', '1997-10-15 00:00:00', 15, '3829',
201);
(207, 'Dan', 'Smith', 'Shp', '1996-12-01 00:00:00', 25, '2259',
203);
(208, 'Fred', 'Campbell', 'Shp', '1997-04-01 00:00:00', 25,
'1752', 203);
(209, 'Paula', 'Jacobs', 'Mkt', '1998-03-17 00:00:00', 15, '3357',
201);

(210, 'Nancy', 'Hoffman', 'Sal', '1996-02-15 00:00:00', 25,
'2974', 203);
# 10 records
#
# Table structure for table 'Item'
#
DROP TABLE IF EXISTS Ìtem`;
CREATE TABLE Ìtem` (
Ìtem_Number` INTEGER NOT NULL,
Ìtem_Desc` VARCHAR(20),
Ìtem_Price` DECIMAL(19,4),
Ìtem_PriceIncrease` DECIMAL(19,4),
`Supplier_ID` VARCHAR(3),
PRIMARY KEY (Ìtem_Number`),

INDEX (`Supplier_ID`)
SET autocommit=1;
#
# Dumping data for table 'Item'
#
INSERT INTO Ìtem` (Ìtem_Number`, Ìtem_Desc`,
Ìtem_Price`, Ìtem_PriceIncrease`, `Supplier_ID`) VALUES (1,
'Fresh Salad', 2, .25, 'Asp');
'Soup of the Day', 1.5, NULL, 'Asp');
'Sandwich', 3.5, .4, 'Asp');
'Grilled steak', 6, .7, 'Cbc');

'Hamburger', 2.5, .3, 'Cbc');
'Broccoli', 1, .05, 'Frv');
'French Fries', 1.5, NULL, 'Frv');
'Soda', 1.25, .25, 'Jbr');
'Coffee', .85, .15, 'Jbr');
`Item_Price`, `Item_PriceIncrease`, `Supplier_ID`) VALUES
(10, 'Dessert', 3, .5, 'Vsb');
# 10 records
#
# Table structure for table 'Lunch'
#
DROP TABLE IF EXISTS `Lunch`;

CREATE TABLE `Lunch` (
`Lunch_ID` INTEGER NOT NULL,
`Lunch_Date` DATETIME,
Èmp_ID` INTEGER,
INDEX (Èmp_ID`),
INDEX (`Lunch_ID`),
PRIMARY KEY (`Lunch_ID`)
SET autocommit=1;
#
# Dumping data for table 'Lunch'
#
INSERT INTO `Lunch` (`Lunch_ID`, `Lunch_Date`, Èmp_ID`)
VALUES (1, '1998-11-16 00:00:00', 201);

VALUES (2, '1998-11-16 00:00:00', 202);
VALUES (3, '1998-11-16 00:00:00', 203);
VALUES (4, '1998-11-16 00:00:00', 207);
VALUES (5, '1998-11-16 00:00:00', 206);
VALUES (6, '1998-11-16 00:00:00', 210);
VALUES (7, '1998-11-25 00:00:00', 201);
VALUES (8, '1998-11-25 00:00:00', 205);
VALUES (9, '1998-11-25 00:00:00', 204);
VALUES (10, '1998-11-25 00:00:00', 207);
VALUES (11, '1998-11-25 00:00:00', 208);
VALUES (12, '1998-12-04 00:00:00', 201);
VALUES (13, '1998-12-04 00:00:00', 203);

VALUES (14, '1998-12-04 00:00:00', 205);
VALUES (15, '1998-12-04 00:00:00', 210);
VALUES (16, '1998-12-04 00:00:00', 208);
# 16 records
#
# Table structure for table 'Lunch_item'
#
DROP TABLE IF EXISTS `Lunch_item`;
CREATE TABLE `Lunch_item` (
`Lunch_ID` INTEGER NOT NULL,
`Item_Number` INTEGER NOT NULL,
`LI_Quantity` INTEGER,
INDEX (`Lunch_ID`),
PRIMARY KEY (`Lunch_ID`, `Item_Number`)

SET autocommit=1;
#
# Dumping data for table 'Lunch_item'
#
INSERT INTO `Lunch_item` (`Lunch_ID`, `Item_Number`,
`LI_Quantity`) VALUES (1, 1, 1);

# 71 records
#
# Table structure for table 'Supplier'
#
DROP TABLE IF EXISTS `Supplier`;
CREATE TABLE `Supplier` (
`Supplier_ID` VARCHAR(3) NOT NULL,
`Supplier_Name` VARCHAR(30),
PRIMARY KEY (`Supplier_ID`),

INDEX (`Supplier_ID`)
SET autocommit=1;
#
# Dumping data for table 'Supplier'
#
INSERT INTO `Supplier` (`Supplier_ID`, `Supplier_Name`)
VALUES ('Arr', 'Alice & Ray's Restaurant');
VALUES ('Asp', 'A Soup Place');
VALUES ('Cbc', 'Certified Beef Company');
VALUES ('Frv', 'Frank Reed's Vegetables');
VALUES ('Fsn', 'Frank & Sons');
VALUES ('Jbr', 'Just Beverages');

VALUES ('Jps', 'Jim Parker's Shop');
VALUES ('Vsb', 'Virginia Street Bakery');
# 8 records
Description: Master Index of EDGAR Dissemination
Feed
Last Data Received: March 31, 2018
Comments: [email protected]
Anonymous FTP: ftp://ftp.sec.gov/edgar/
Cloud HTTP: https://www.sec.gov/Archives/
CIK|Company Name|Form Type|Date Filed|Filename
---------------------------------------------------------------------------
-----
1000032|BINCH JAMES G|4|2018-02-
16|edgar/data/1000032/0000913165-18-000034.txt
1000045|NICHOLAS FINANCIAL INC|10-Q|2018-02-
09|edgar/data/1000045/0001193125-18-037381.txt
15|edgar/data/1000045/0001000045-18-000004.txt
08|edgar/data/1000045/0001000045-18-000005.txt
20|edgar/data/1000045/0001609591-18-000001.txt

1000184|SAP SE|DFAN14A|2018-01-
30|edgar/data/1000184/0001104659-18-005126.txt
Module 5: Using Databases with Python
• File-based systems
• Database systems
• Relational databases
• Structured Query Language
ISM 6405- Dr. Milad Baghersad 1
Reference: Database Systems: Design, Implementation and
Management, 12th edition by Carlos
Coronel and Steven Morris (Cengage Learning, ISBN: 978-1-
305-62748-2)
History: traditional file-based systems
• Computerized manual filing systems
• Individual files for each purpose
• Each business unit had its own file system
• Organized to facilitate the expected use of the data

• Data processing specialist required to retrieve data and run
reports
Example: file-based systems
• Suppose, as a FAU student, that you need to do the following
things:
1. Register for courses
2. Pay tuition
3. Work part-time on campus
File Based Systems Illustration
TuitionRec (ID, name,
address, amtPaid,
balance, …)
RegRec (ID, name,
address, courses, …)
EmpRec (ID, name,

address, wage, …)
Tuition
payment
entry and
reports
File
handling
routines
File
definition
Course
registration
entry and
reports
File
handling
routines
File

definition
Work-study
data entry
and reports
File
handling
routines
File
definition
Tuition payment application programs
Course registration application programs
Work-study application programs
Payment file(s)
Registration file(s)
Work-study file(s)
Issues with file-based systems

• Data redundancy
• Different files contain same information (ID, name, address,
etc…)
• Isolation of data in separate systems
• Data inconsistency (e.g., different ID values for the same
person)
• Lack of data integrity
• Data anomalies
• All changes may not be made successfully
• Update / Insertion / Deletion anomalies
Better approach: database systems
• Database: a collection of interrelated data
organized in such a way that it corresponds to the
needs and structure of an organization and can be
used by more than one person for more than one
application.
Example: database system

Tuition
payment
entry and
reports
Course
registration
entry and
reports
Work-study
data entry
and reports
Tuition payment application programs
Course registration application programs
Work-study application programs
DBMS
Database

Advantages of database systems
• Minimal data redundancy
• Data consistency
• Integration of data
• Improved data sharing
• Enforcement of standards
• Ease of application development
• Uniform security, privacy, and integrity
• Data independence from applications
• "self-describing" data stored in a data dictionary (metadata)
File-based systems often imply "flat" data files:
RecNo Name Address City State Zip Product Units Amount
1 John Smith 221 Main St. New York NY 08842
Television 1 $500
2 William Chin 43 1st Ave. Redmond WA 98332
Refrigerator 1 $800

3 William Chin 43 1st Ave. Redmond WA 98332 Toaster
1 $80
4 Marta Dieci 2 West Ave. Reno NV 92342 Television 1
$500
5 Marta Dieci 2 West Ave. Reno NV 92342 Radio 1 $40
6 Marta Dieci 2 West Ave. Reno NV 92342 Stereo 1 $200
7 Peter Melinkoff 53 NE Rodeo Miami FL 18332
Computer 1 $1500
8 Martin Sengali 1234 5th St. Boston FL 03423
Television 1 $500
9 Martin Sengali 1234 5th St. Boston FL 03423 Stereo 1
$200
10 Martin Sengali 1234 5th St. Boston FL 03423 Radio 1
$40
Refrigerator 1 $80
Subset of the problems with a file-based
system:
• Redundancy (i.e. data duplication)
• Update anomalies

• Update a single address requires you to update multiple entries
• Insertion anomalies
• You cannot insert information about customer until they have
actually
purchased something
• Deletion anomalies
• If there is a single instance of a given product being sold,
and for some
reason we decide to remove the customer who purchased it, we
will lose
information about the product also
Improving data management: Types of database
models
• Hierarchical database model
• Tree-based approach developed in 1960's
• Based on parent-child relationships (1:M)
• Network database model
• Created to improve on hierarchical model
• Allows records to have more than one parent

• Can access data from multiple points
• Relational database model
Relational database model
Turing Award Winner, Edgar F. Codd’s landmark paper, “A
Relational Model of
Data for Large Shared Data Banks" (1970) laid out a new way
to organize and
access data: the Relational Model.
12
Customer(CustomerID, Name, …
Order(OrderID, CustomerID, OrderDate, …
ItemsOrdered(OrderID, ItemID, Quantity, …
Items(ItemID, Description, Price, …
This Photo by Unknown
Author is licensed under
CC BY-SA
https://en.wikipedia.org/wiki/Edgar_F._Codd
https://creativecommons.org/licenses/by-sa/3.0/

Flat-file database
RecNo Name Address City State Zip Product Units Amount
Television 1 $500
2 William Chin 43 1st Ave. Redmond WA 98332
Refrigerator 1 $800
3 William Chin 43 1st Ave. Redmond WA 98332 Toaster
1 $80
4 Marta Dieci 2 West Ave. Reno NV 92342 Television 1
$500
5 Marta Dieci 2 West Ave. Reno NV 92342 Radio 1 $40
6 Marta Dieci 2 West Ave. Reno NV 92342 Stereo 1 $200
Computer 1 $1500
Television 1 $500
9 Martin Sengali 1234 5th St. Boston FL 03423 Stereo 1
$200
10 Martin Sengali 1234 5th St. Boston FL 03423 Radio 1
$40
Refrigerator 1 $80

13
Example: relational database
14
Customer table
CusNo Name Address City State Zip
2 William Chin 43 First Ave. Redmond WA 98332
3 Marta Dieci 2 West Ave. Reno NV 92342
5 Martin Sengali 1234 5th St. Boston MA 03423
CusNo Product Units Amount
1 Television 1 $500
2 Refrigerator 1 $800
2 Toaster 1 $80
3 Television 1 $500
3 Radio 1 $40
3 Stereo 1 $200

4 Computer 1 $1500
5 Television 1 $500
5 Stereo 1 $200
5 Radio 1 $40
5 Refrigerator 1 $800
OrderItem table
Another database in relational form
ISBN Title PubID Price
0-103-45678-9 Iliad 1 $25.00
0-11-345678-9 Moby Dick 3 $49.00
0-12-333433-3 On Liberty 1 $25.00
0-12-345678-9 Jane Eyre 3 $49.00
0-123-45678-0 Ulysses 2 $34.00
0-321-32132-1 Balloon 3 $34.00
0-55-123456-9 Main Street 3 $22.95
0-555-55555-9 MacBeth 2 $12.00
0-91-045678-5 Hamlet 2 $20.00

0-91-335678-7 Fairie Queene 1 $15.00
0-99-777777-7 King Lear 2 $49.00
0-99-999999-9 Emma 1 $20.00
1-1111-1111-1 C++ 1 $29.95
1-22-233700-0 Visual Basic 1 $25.00
AuID AuName AuPhone
1 Austen 111-111-1111
10 Jones 123-333-3333
11 Snoopy 321-321-2222
12 Grumpy 321-321-0000
13 Sleepy 321-321-1111
2 Melville 222-222-2222
3 Homer 333-333-3333
4 Roman 444-444-4444
5 Shakespeare 555-555-5555
6 Joyce 666-666-6666
7 Spencer 777-777-7777
8 Mill 888-888-8888

9 Smith 123-222-2222
ISBN AuID
0-103-45678-9 3
0-11-345678-9 2
0-12-333433-3 8
0-12-345678-9 1
0-123-45678-0 6
0-321-32132-1 11
0-321-32132-1 12
0-321-32132-1 13
0-55-123456-9 9
0-55-123456-9 10
0-555-55555-9 5
0-91-045678-5 5
0-91-335678-7 7
0-99-777777-7 5
0-99-999999-9 1
1-1111-1111-1 4

1-22-233700-0 4
PubID PubName PubPhone
1 Big House 123-456-7890
2 Alpha Press 999-999-9999
3 Small House 714-000-0000
BOOK
AUTHOR
PUBLISHER
BOOK/AUTHOR
15
Install MySQL Server and Workbench
Use the video: How To Install MySQL (Server and
Workbench) [05:57]
Please note as you are installing MySql, when you are
selecting Products and Features (see [02:30] in the video),
click on MySQL Connectors and add Python connector also.
If you can not add Python connector, later I will teach you
another method to connect Python and MySQL.

16
https://www.youtube.com/watch?v=u96rVINbAUI
Manipulating data in databases
Structured Query Language (SQL)
• creating database and table structures
• performing data manipulation and administration
• querying the database to extract useful information
17
Categories of SQL commands
• Data Definition Language (DDL)
– Commands that define a database, including creating, altering,
and
dropping tables and stored procedures, and establishing
constraints
• CREATE TABLE, set PRIMARY KEY
• Data Manipulation Language (DML)
– Commands that are used to manipulate data and extract
information
• SELECT, UPDATE, INSERT, DELETE

18
Data types
• ANSI/ISO SQL data types:
– INTEGER / SMALLINT
– DECIMAL(precision, scale)
– CHAR(n) - fixed length character data
– VARCHAR(n) - variable length character data
– DATE - Julian date format
– plus several more…
• Other DBMS add additional data types.
19
MySQL data types (cont.)
Primary date and time types:
• DATE 'YYYY-MM-DD' format
range: '1000-01-01' to '9999-12-31'
• DATETIME 'YYYY-MM-DD HH:MM:SS' format

range: '… 00:00:00' to '… 23:59:59'
Invalid dates and times are converted to zero values: '0000-00-
00'
Some built-in functions:
NOW( ), CURDATE( ), DATEDIFF( ), INTERVAL
DATE( ), TIME( ), DAY( ), YEAR( ), MONTH( ), etc.
20
Sample Database
Posted on course site:
• class.sql (MySQL 5.0)
• We will be running examples of queries on this database as
we cover the material, to help illustrate how the different
SQL statements work.
• I strongly recommend working through the examples on your
own as we go, in order to better understand them.
21
Sample Database (continued)

6 Tables:
22
StackOverflow- Mocking data for Angular2/typescript with my
own
model type properties Licensed by Stack Exchange Network
https://stackoverflow.com/questions/41765329/mocking-data-
for-angular2-typescript-with-my-own-model-type-properties
https://stackoverflow.com/legal/acceptable-use-policy
GROUP BY
Ex: For each Dept_Code, list:
The number of employees (as count) and the total credit limit
(as
total)
23

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