The document discusses creating a multicore database project. It recommends taking the following steps:
1. Define what the project is about, what it aims to achieve, and who it is for.
2. Identify information resources and develop a basic data model.
3. Design a user interface mockup without technical constraints, thinking creatively.
Moving data about library resources among systems often engenders data cleanup processes. What is the best way to clean up data? Which tools and skills for non-programmers can help? See how University of California, Riverside Libraries tackle this issue, then share tips and techniques in an open forum.
Moving data about library resources among systems often engenders data cleanup processes. What is the best way to clean up data? Which tools and skills for non-programmers can help? See how University of California, Riverside Libraries tackle this issue, then share tips and techniques in an open forum.
Intro to SQL by Google's Software EngineerProduct School
Intro to SQL, by Roman Polonsky, software engineer on Google's Global Tools Team.
SQL provides powerful but reasonably simple tools for data analysis and handling. This workshop will take absolute beginners through the basics of SQL. You’ll learn SQL queries needed to collect data from a database, even if it lives in different places and analyze it to find the answers you’re looking for.
Take away from this workshop the understanding of essential SQL skills that allow developers to write queries against single and multiple tables, manipulate data in tables, and create database objects.
At the end of this Lesson (Part 1) the students should be able to know the following
Introduction
Data Entry
Variable and Value Label
Entering Data
File management
Descriptive statistics
Editing and modifying the data
From this power point you can get the details about Advanced Filter, Use of Macros with Advanced Filter, Data Validation, Creation of data validation Drop-Down List, Handling of External Data, Goal Seek, What-if analysis,
SPSS for beginners, a short course about how novices can use SPSS to analyze their research findings. With this tutorial anyone becomes able to use SPSS for basic statistical analysis. No need to be a professional to use SPSS.
Cassandra data modeling presentation given at the Cassandra DC Meetup by AddThis engineer Ben Knear. Learn about data types, how to optimize querying and data storage, and best practices for Cassandra!
Video of meetup: https://www.youtube.com/watch?v=wXXTQZ0JS1U
Intro to SQL by Google's Software EngineerProduct School
Intro to SQL, by Roman Polonsky, software engineer on Google's Global Tools Team.
SQL provides powerful but reasonably simple tools for data analysis and handling. This workshop will take absolute beginners through the basics of SQL. You’ll learn SQL queries needed to collect data from a database, even if it lives in different places and analyze it to find the answers you’re looking for.
Take away from this workshop the understanding of essential SQL skills that allow developers to write queries against single and multiple tables, manipulate data in tables, and create database objects.
At the end of this Lesson (Part 1) the students should be able to know the following
Introduction
Data Entry
Variable and Value Label
Entering Data
File management
Descriptive statistics
Editing and modifying the data
From this power point you can get the details about Advanced Filter, Use of Macros with Advanced Filter, Data Validation, Creation of data validation Drop-Down List, Handling of External Data, Goal Seek, What-if analysis,
SPSS for beginners, a short course about how novices can use SPSS to analyze their research findings. With this tutorial anyone becomes able to use SPSS for basic statistical analysis. No need to be a professional to use SPSS.
Cassandra data modeling presentation given at the Cassandra DC Meetup by AddThis engineer Ben Knear. Learn about data types, how to optimize querying and data storage, and best practices for Cassandra!
Video of meetup: https://www.youtube.com/watch?v=wXXTQZ0JS1U
A talk given by Julian Hyde at DataCouncil SF on April 18, 2019
How do you organize your data so that your users get the right answers at the right time? That question is a pretty good definition of data engineering — but it is also describes the purpose of every DBMS (database management system). And it’s not a coincidence that these are so similar.
This talk looks at the patterns that reoccur throughout data management — such as caching, partitioning, sorting, and derived data sets. As the speaker is the author of Apache Calcite, we first look at these patterns through the lens of Relational Algebra and DBMS architecture. But then we apply these patterns to the modern data pipeline, ETL and analytics. As a case study, we look at how Looker’s “derived tables” blur the line between ETL and caching, and leverage the power of cloud databases.
An introduction to SQL standard language for beginners and non-technical information people. Mostly covers SELECT statement using standard clauses, Joins, Sub-Queries and ...
Data Exploration with Apache Drill: Day 2Charles Givre
Study after study shows that data scientists and analysts spend between 50% and 90% of their time preparing their data for analysis. Using Drill, you can dramatically reduce the time it takes to go from raw data to insight. This course will show you how.
The course material for this presentation are available at https://github.com/cgivre/data-exploration-with-apache-drill
James Colby Maddox Business Intellignece and Computer Science Portfoliocolbydaman
This portfolio covers the business intelligence course work I have completed at Set Focus, and some of the course work I have completed at Kennesaw State University
Queries module in course Accelerated Introduction to Microsoft Access. Only retrieval is covered in this module. See the Automating Access module for the Action Queries.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
4. A Symptoms checker - Disease
B Metabolic engineering
C Cancer Neoantigens
Familienaam Voornaam E-mail Opleidingsprogramma
Davey Lucas Lucas.Davey@UGent.be C CMBIOISB
David Sven Sven.David@UGent.be B IMCELB
Engelen Yanou Yanou.Engelen@UGent.be ? IMCELB
Ezquerro Marrodán Elsa Elsa.EzquerroMarrodan@UGent.be C IXGAEX
Georis Raphaël Raphael.Georis@UGent.be B IMCELB
Gilis Jeroen Jeroen.Gilis@UGent.be B CMBIOISB
Lashkari Samira Samira.Lashakri@UGent.be A CMBIOISB
Recer Karmen Karmen.Recer@UGent.be C IXGAEX
Schindfessel Cédric Cedric.Schindfessel@UGent.be B IMCELB
Silva Marta Marta.Silva@UGent.be B CMBIOISB
Silva Meneses Rodrigo Rodrigo.Meneses@UGent.be C CMBIOISB
Strybol Pieter-Paul PieterPaul.Strybol@UGent.be B/C CMBIOIBE
Taelman Steff Steff.Taelman@UGent.be A CMBIOIBE
Tóth Máté István MateIstvan.Toth@UGent.be C EXGAEX
Toulmé Coralyne Coralyne.Toulme@UGent.be C IXGAEX
Van hoyweghen Sergej Sergej.Vanhoyweghen@UGent.be ? IMCELB
Willems Thomas Thomas.Willems@UGent.be ? IMCELB
Wojciulewitsch Coralie Coralie.Wojciulewitsch@UGent.be A IMCELB
Yekimov Illya Illya.Yekimov@UGent.be A IMCELB
6. Selecta Statement (General)
Select componentsb (MySQL version 4.0.X)
1. Select (required)
2. Expression (required)
3. From Table (Only optional if table data is
not used)
4. Where Condition (optional)
5. Group By (optional)
6. Having (optional)
7. Order By (optional)
8. Limit (optional)
aWords listed in blue above are key (reserved) words in MySQL, except
“Expression”
bThere are many more components in the select statement, but only the most
common are discussed
7. Select
• Is a MySQL keyword at the
beginning of a Structured Query
Language (SQL) statement or more
simply a query that retrieves data
from a database.
• It can be used in conjunction with the
MySQL command insert, replace,
and create table.
8. Expression
The “expression”
1. Selects which columns (by column name)
will be retrieved
2. Apply MySQL functions on the columns
of data
Examples—
* (Selects all columns in a table)
temperature * 1.8 + 32a
dt, temperature, relative_humidityb
aApply arithmetic operations on a column
bChoose multiple columns in single query
Note: columns are in green, tables are in red,
MySQL functions are in magenta.
9. Expression Cont
More Examples--
-1 * speed * sin( direction * pi() / 180 )c
-1 * speed * cos( direction * pi() / 180 )
as Vd
avg(temperature)e
tableX.temperature,
tableY.temperaturef
cCan use built in mathematical functions.
dThe ‘as’ keyword can be used to set the output column name.
eCan retrieve a statistical value from rows of data. Must be used with ‘group by’.
fThe select can retrieve or make relationships from multiple tables from a single
query. Note the ‘.’ that separates the table name and column name.
10. From
Tells MySQL which table(s) to select
froma
aIf multiple tables are used in the
expression, they all must be listed
here seperated by a ‘,’
11. Where
Sets the condition of which rows to select. Useful
for eliminating unwanted data from your results.
Conditional
> Greater than
< Less than
>= Greater than or equal to
<= Less than or equal to
= Equals
!= Not equals
Logical
and True if both are true
or True if only one is true
() Useful for grouping or
ordering multiple logical
statements
String pattern matching
like a MySQL keyword in-between column and pattern
% a wildcard that matches all instances in a string
_ a wildcard that matches only the character location
12. Group By
Aggregates rows by distinct values in the columns
listed in the ‘group by’ when using statistical
functions (e.g., avg, std, max, min, sum, count, etc.)
Example--
group by site_ida
group by site_id, month(dt)b
aSingle column
bMultiple columns and apply functions on a column
13. Having
Similar to where, except that it must follow
‘group by’ group, and only eliminates results
after the results have been aggregated. See
where for more details.
14. Order By
• Orders the result rows defined by the columns in the
‘order by’ group
• The keyword asc (ascending) and desc (descending)
to change the order of the result rows, and is always
at the end of the order by component. desc is the
default.
Examples—
order by dt
order by dt, site_ida
aThe first column is ordered first, second column is ordered second
on the ordering of the first column, and so on
15. Order By Example
mysql>select … order by site_id, month asc
site_id month ave_temp
1 1 32.3
1 2 40.2
1 3 49.5
2 1 35.6
2 2 41.3
2 3 53.5
16. Limit
Limits the number of rows from the result set
limit row_counta
limit offset, row_countb
aStarts at the first row
bStarts at the offset row
17. Outerjoins
Explicit joins in SQL:
Product(name, category)
Purchase(prodName, store)
Same as:
But Products that never sold will be lost !
SELECT Product.name, Purchase.store
FROM Product JOIN Purchase ON
Product.name = Purchase.prodName
SELECT Product.name, Purchase.store
FROM Product, Purchase
WHERE Product.name = Purchase.prodName
18. Outer Joins
• Left outer join:
– Include the left tuple even if there’s no
match
• Right outer join:
– Include the right tuple even if there’s no
match
• Full outer join:
– Include the both left and right tuples even
if there’s no match
20. Modifying the Database
Three kinds of modifications
• Insertions
• Deletions
• Updates
Sometimes they are all called “updates”
21. Insertions
General form:
Missing attribute NULL.
May drop attribute names if give them in order.
INSERT INTO R(A1,…., An) VALUES (v1,…., vn)
INSERT INTO Purchase(buyer, seller, product, store)
VALUES (‘Joe’, ‘Fred’, ‘wakeup-clock-espresso-machine’,
‘The Sharper Image’)
Example: Insert a new purchase to the database:
22. Insertions
INSERT INTO PRODUCT(name)
SELECT DISTINCT Purchase.product
FROM Purchase
WHERE Purchase.date > “10/26/01”
The query replaces the VALUES keyword.
Here we insert many tuples into PRODUCT
23. Insertion: an Example
INSERT INTO Product(name)
SELECT DISTINCT prodName
FROM Purchase
WHERE prodName NOT IN (SELECT name FROM Product)
name listPrice category
gizmo 100 Gadgets
camera - -
24. Deletions
DELETE FROM PURCHASE
WHERE seller = ‘Joe’ AND
product = ‘Brooklyn Bridge’
Factoid about SQL: there is no way to delete only a single
occurrence of a tuple that appears twice
in a relation.
Example:
25. Updates
UPDATE PRODUCT
SET price = price/2
WHERE Product.name IN
(SELECT product
FROM Purchase
WHERE Date =‘Oct, 25, 1999’);
Example:
26. Indexes
REALLY important to speed up query processing time.
Suppose we have a relation
Person (name, age, city)
Sequential scan of the file Person may take long
SELECT *
FROM Person
WHERE name = “Smith”
27. • Create an index on name:
• B+ trees have fan-out of 100s: max 4 levels !
Indexes
Adam Betty Charles …. Smith ….
29. Creating Indexes
Indexes can be created on more than one attribute:
CREATE INDEX doubleindex ON
Person (age, city)
SELECT *
FROM Person
WHERE age = 55 AND city = “Seattle”
SELECT *
FROM Person
WHERE city = “Seattle”
Helps in:
But not in:
Example:
30. Creating Indexes
Indexes can be useful in range queries too:
B+ trees help in:
Why not create indexes on everything?
CREATE INDEX ageIndex ON Person (age)
SELECT *
FROM Person
WHERE age > 25 AND age < 28
31. Views are relations, except that they are not physically stored.
For presenting different information to different users
Employee(ssn, name, department, project, salary)
Payroll has access to Employee, others only to Developers
CREATE VIEW Developers AS
SELECT name, project
FROM Employee
WHERE department = “Development”
32. A Different View
Person(name, city)
Purchase(buyer, seller, product, store)
Product(name, maker, category)
We have a new virtual table:
Seattle-view(buyer, seller, product, store)
CREATE VIEW Seattle-view AS
SELECT buyer, seller, product, store
FROM Person, Purchase
WHERE Person.city = “Seattle” AND
Person.name = Purchase.buyer
33. A Different View
SELECT name, store
FROM Seattle-view, Product
WHERE Seattle-view.product = Product.name AND
Product.category = “shoes”
We can later use the view:
34. 34
Types of Views
• Virtual views:
– Used in databases
– Computed only on-demand – slower at
runtime
– Always up to date
• Materialized views
– Used in data warehouses
– Precomputed offline – faster at runtime
– May have stale data
36. For the project I suggest to take the following
steps (individual or in group - maybe setup a
collaborative tool like slack)
1. What it is about ? What do you want to
achieve ? For who ?
2. identify information resources - think about
a basic data-model
3. Draw (mockup) an interface, don't be
constrained by technical consideration - think
ouside the box:)