This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 6th Ed." by Silberschatz, Korth and Sudarshan. The chapter introduces SQL, including its history, data definition language, data types, basic query structure using SELECT, FROM, and WHERE clauses, and additional query capabilities like aggregation, subqueries and string operations. It also covers modifying the database using INSERT, DELETE, ALTER and DROP statements.
The document contains 16 sections that describe database management system experiments to be performed. Each section includes instructions to create and manipulate tables, perform queries, and implement concepts like triggers, functions, stored procedures, cursors, and embedded SQL. Students will connect to databases and design systems for payroll, banking, and a library using Visual Basic. Their work will be evaluated based on aim and description, queries, results, output, and records.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
The document provides an overview of entity-relationship (E-R) modeling concepts including:
- Entity sets represent collections of real-world entities that share common properties
- Relationship sets define associations between entity sets
- Attributes provide additional information about entities and relationships
- Keys uniquely identify entities and relationships
- Cardinalities constrain how entities can participate in relationships
- E-R diagrams visually depict entity sets, attributes, relationships and constraints.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
This document provides an overview of Pandas, a Python library used for data analysis and manipulation. Pandas allows users to manage, clean, analyze and model data. It organizes data in a form suitable for plotting or displaying tables. Key data structures in Pandas include Series for 1D data and DataFrame for 2D (tabular) data. DataFrames can be created from various inputs and Pandas includes input/output tools to read data from files into DataFrames.
This document provides an example of student records in an unnormalized form, containing repeating groups. It then demonstrates normalizing the data by removing the repeating groups into multiple tables in first normal form. Further normalization results in separating attributes with partial dependencies and non-key dependencies into their own tables, achieving second and third normal form respectively. The document explains the different normal forms and how normalization helps reduce data anomalies on insert, update and delete operations.
Comparison between ER Modeling and Dimension ModelingKaruna Kak
This document compares and contrasts dimensional modeling and E-R modeling. It provides examples of a star schema and snowflake schema dimensional model. It notes that dimensional modeling supports OLAP and online analytical processing while E-R modeling supports OLTP. Dimensional modeling uses a denormalized structure while E-R modeling is normalized. Dimensional modeling is more robust and understandable compared to E-R modeling.
The document contains 16 sections that describe database management system experiments to be performed. Each section includes instructions to create and manipulate tables, perform queries, and implement concepts like triggers, functions, stored procedures, cursors, and embedded SQL. Students will connect to databases and design systems for payroll, banking, and a library using Visual Basic. Their work will be evaluated based on aim and description, queries, results, output, and records.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
The document provides an overview of entity-relationship (E-R) modeling concepts including:
- Entity sets represent collections of real-world entities that share common properties
- Relationship sets define associations between entity sets
- Attributes provide additional information about entities and relationships
- Keys uniquely identify entities and relationships
- Cardinalities constrain how entities can participate in relationships
- E-R diagrams visually depict entity sets, attributes, relationships and constraints.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
The document discusses the entity-relationship (E-R) data model. It defines key concepts in E-R modeling including entities, attributes, entity sets, relationships, and relationship sets. It describes different types of attributes and relationships. It also explains how to represent E-R diagrams visually using symbols like rectangles, diamonds, and lines to depict entities, relationships, keys, and cardinalities. Primary keys, foreign keys, and weak entities are also covered.
This document provides an overview of Pandas, a Python library used for data analysis and manipulation. Pandas allows users to manage, clean, analyze and model data. It organizes data in a form suitable for plotting or displaying tables. Key data structures in Pandas include Series for 1D data and DataFrame for 2D (tabular) data. DataFrames can be created from various inputs and Pandas includes input/output tools to read data from files into DataFrames.
This document provides an example of student records in an unnormalized form, containing repeating groups. It then demonstrates normalizing the data by removing the repeating groups into multiple tables in first normal form. Further normalization results in separating attributes with partial dependencies and non-key dependencies into their own tables, achieving second and third normal form respectively. The document explains the different normal forms and how normalization helps reduce data anomalies on insert, update and delete operations.
Comparison between ER Modeling and Dimension ModelingKaruna Kak
This document compares and contrasts dimensional modeling and E-R modeling. It provides examples of a star schema and snowflake schema dimensional model. It notes that dimensional modeling supports OLAP and online analytical processing while E-R modeling supports OLTP. Dimensional modeling uses a denormalized structure while E-R modeling is normalized. Dimensional modeling is more robust and understandable compared to E-R modeling.
This document provides an introduction and overview of databases and the basic operations used to manage data in a database using Microsoft Access 2007. It defines what a database is, how data is organized in tables with rows and columns, and when it is appropriate to use a database. It also outlines and provides examples of the basic CRUD (create, read, update, delete) operations used in structured query language (SQL) to manipulate data, including inserting, selecting, updating, and deleting records from database tables.
This document provides an overview of SQL, including its objectives, history, data definition and manipulation languages, and basic concepts. It discusses SQL's purpose in creating database structures and performing tasks like inserts, updates, deletes, and queries. The document also covers SQL datatypes, keywords for queries, updates, and deletes, and both advantages and disadvantages of SQL.
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
Dbms Notes Lecture 9 : Specialization, Generalization and AggregationBIT Durg
This document discusses key concepts in the Extended Entity Relationship (EER) model, including specialization, generalization, attribute inheritance, and aggregation. Specialization involves dividing a higher-level entity set into lower-level subsets, while generalization groups multiple lower-level entity sets into a single higher-level set based on common attributes. Attribute inheritance allows attributes to be passed from higher to lower levels. Aggregation models relationships between relationships by treating them as higher-level entities. The document provides examples and discusses constraints like disjointness and completeness that can be applied.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Supervised Machine Learning With Types And TechniquesSlideTeam
Supervised Machine Learning with Types and Techniques is for the mid level managers giving information about what is supervised machine learning, its types, how supervised machine learning, its advantages. You can also know the difference between Supervised and Unsupervised Machine learning to understand supervised machine learning in a better way for business growth. https://bit.ly/3ewivHm
Fundamentals of database system - Database System Concepts and ArchitectureMustafa Kamel Mohammadi
In this chapter you will learn
DBMS evolution
Data model
Three schema architecture
DBMS language
DBMS interfaces
DBMS components
Classification of DBMS
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
The document discusses database design and the design process. It explains that database design involves determining the logical structure of tables and relationships between data elements. The design process consists of steps like determining relationships between data, dividing information into tables, specifying primary keys, and applying normalization rules. The document also covers entity-relationship diagrams and designing inputs and outputs, including input controls and designing report formats.
This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
This document provides information about SQL queries and joins. It begins by introducing SQL (Structured Query Language) which is used to communicate with databases and retrieve required information. It describes the basic CRUD (Create, Read, Update, Delete) functions of SQL. It then discusses different types of SQL queries - aggregate function queries, scalar function queries, and join queries. It provides the syntax and explanation of inner joins, outer joins (left, right, full) which are used to query data from multiple tables based on relationships between columns. The document is presented by Hammad, Bilal and Awais.
This slide give the basic introduction about UML diagram and it's types, and brief intro about Activity Diagram, use of activity diagram in object oriented programming language..
Fundamentals of Database Systems questions and answers with explanation for fresher's and experienced for interview, competitive examination and entrance test.
The document discusses the different types of users that interact with a database management system (DBMS). It identifies three primary categories of DBMS users: end users, software engineers, and database administrators. End users are divided into casual, naive, sophisticated, and specialized users depending on their level of experience. Software engineers include application programmers who write database access programs and systems analysts who design database solutions. The database administrator is responsible for defining the database schema, granting access permissions, and ensuring data integrity.
This document discusses SQL commands for creating tables, adding data, and enforcing integrity constraints. It covers the core SQL commands: DDL for defining schema, DML for manipulating data, DCL for controlling access, DQL for querying data, and TCL for transactions. Specific topics summarized include data types, primary keys, foreign keys, indexes, views, stored procedures, functions and triggers. Integrity constraints like NOT NULL, UNIQUE, CHECK, DEFAULT are explained. The document also covers SQL queries with filtering, sorting, patterns and ranges. Authorization using GRANT and REVOKE commands is briefly covered.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
This document provides an overview of SQL and relational database concepts. It describes the history and standards of SQL, data definition and domain types in SQL, basic query structure including the SELECT, FROM, and WHERE clauses, and DML operations like INSERT, DELETE, and ALTER TABLE. Examples of table schemas and queries involving joins, aggregation, and renaming are provided to illustrate SQL syntax and capabilities.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 7th Ed." by Silberschatz, Korth and Sudarshan. It covers the history and components of SQL, data definition and manipulation languages, basic query structure, predicates, null values, and set operations in SQL. Key topics include the CREATE TABLE statement, data types, integrity constraints, SELECT statements, joins, ordering results, and aggregate functions.
This document provides an introduction and overview of databases and the basic operations used to manage data in a database using Microsoft Access 2007. It defines what a database is, how data is organized in tables with rows and columns, and when it is appropriate to use a database. It also outlines and provides examples of the basic CRUD (create, read, update, delete) operations used in structured query language (SQL) to manipulate data, including inserting, selecting, updating, and deleting records from database tables.
This document provides an overview of SQL, including its objectives, history, data definition and manipulation languages, and basic concepts. It discusses SQL's purpose in creating database structures and performing tasks like inserts, updates, deletes, and queries. The document also covers SQL datatypes, keywords for queries, updates, and deletes, and both advantages and disadvantages of SQL.
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
Dbms Notes Lecture 9 : Specialization, Generalization and AggregationBIT Durg
This document discusses key concepts in the Extended Entity Relationship (EER) model, including specialization, generalization, attribute inheritance, and aggregation. Specialization involves dividing a higher-level entity set into lower-level subsets, while generalization groups multiple lower-level entity sets into a single higher-level set based on common attributes. Attribute inheritance allows attributes to be passed from higher to lower levels. Aggregation models relationships between relationships by treating them as higher-level entities. The document provides examples and discusses constraints like disjointness and completeness that can be applied.
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
attribute selection, constructing decision trees, decision trees, divide and conquer, entropy, gain ratio, information gain, machine leaning, pruning, rules, suprisal
Supervised Machine Learning With Types And TechniquesSlideTeam
Supervised Machine Learning with Types and Techniques is for the mid level managers giving information about what is supervised machine learning, its types, how supervised machine learning, its advantages. You can also know the difference between Supervised and Unsupervised Machine learning to understand supervised machine learning in a better way for business growth. https://bit.ly/3ewivHm
Fundamentals of database system - Database System Concepts and ArchitectureMustafa Kamel Mohammadi
In this chapter you will learn
DBMS evolution
Data model
Three schema architecture
DBMS language
DBMS interfaces
DBMS components
Classification of DBMS
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
The document discusses database design and the design process. It explains that database design involves determining the logical structure of tables and relationships between data elements. The design process consists of steps like determining relationships between data, dividing information into tables, specifying primary keys, and applying normalization rules. The document also covers entity-relationship diagrams and designing inputs and outputs, including input controls and designing report formats.
This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
This document provides information about SQL queries and joins. It begins by introducing SQL (Structured Query Language) which is used to communicate with databases and retrieve required information. It describes the basic CRUD (Create, Read, Update, Delete) functions of SQL. It then discusses different types of SQL queries - aggregate function queries, scalar function queries, and join queries. It provides the syntax and explanation of inner joins, outer joins (left, right, full) which are used to query data from multiple tables based on relationships between columns. The document is presented by Hammad, Bilal and Awais.
This slide give the basic introduction about UML diagram and it's types, and brief intro about Activity Diagram, use of activity diagram in object oriented programming language..
Fundamentals of Database Systems questions and answers with explanation for fresher's and experienced for interview, competitive examination and entrance test.
The document discusses the different types of users that interact with a database management system (DBMS). It identifies three primary categories of DBMS users: end users, software engineers, and database administrators. End users are divided into casual, naive, sophisticated, and specialized users depending on their level of experience. Software engineers include application programmers who write database access programs and systems analysts who design database solutions. The database administrator is responsible for defining the database schema, granting access permissions, and ensuring data integrity.
This document discusses SQL commands for creating tables, adding data, and enforcing integrity constraints. It covers the core SQL commands: DDL for defining schema, DML for manipulating data, DCL for controlling access, DQL for querying data, and TCL for transactions. Specific topics summarized include data types, primary keys, foreign keys, indexes, views, stored procedures, functions and triggers. Integrity constraints like NOT NULL, UNIQUE, CHECK, DEFAULT are explained. The document also covers SQL queries with filtering, sorting, patterns and ranges. Authorization using GRANT and REVOKE commands is briefly covered.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
This document provides an overview of SQL and relational database concepts. It describes the history and standards of SQL, data definition and domain types in SQL, basic query structure including the SELECT, FROM, and WHERE clauses, and DML operations like INSERT, DELETE, and ALTER TABLE. Examples of table schemas and queries involving joins, aggregation, and renaming are provided to illustrate SQL syntax and capabilities.
This document provides an overview of Chapter 3 from the textbook "Database System Concepts, 7th Ed." by Silberschatz, Korth and Sudarshan. It covers the history and components of SQL, data definition and manipulation languages, basic query structure, predicates, null values, and set operations in SQL. Key topics include the CREATE TABLE statement, data types, integrity constraints, SELECT statements, joins, ordering results, and aggregate functions.
This document discusses the SQL query language and database concepts. It covers the basic structure of SQL queries including the SELECT, FROM, and WHERE clauses. It describes how to define schemas and relations using the SQL data definition language including data types, primary keys, and foreign keys. It also discusses operations to modify databases such as INSERT, DELETE, ALTER TABLE, and DROP TABLE.
This document discusses the SQL query language and database concepts. It covers the basic structure of SQL queries including the SELECT, FROM, and WHERE clauses. It describes how to define schemas and relations using the SQL data definition language including data types, primary keys, and foreign keys. It also discusses operations to modify databases such as INSERT, DELETE, ALTER TABLE, and DROP TABLE.
This document summarizes the contents of Chapter 3 from the textbook "Database System Concepts, 5th Ed." by Silberschatz, Korth and Sudarshan. The chapter covers the basics of the SQL language, including data definition, query structure, set operations, aggregate functions, null values, views and modification of databases. Key SQL concepts are explained such as creating tables, inserting and deleting tuples, integrity constraints, joins, and aggregation. Examples are provided to illustrate SQL statements and relational algebra translations.
This document provides an overview of Chapter 3 of the textbook "Database System Concepts". It discusses the following topics in SQL:
1. Data definition language allows specification of schemas, integrity constraints, and authorization information for relations.
2. Basic queries in SQL involve SELECT, FROM, and WHERE clauses that correspond to projection, Cartesian product, and selection in relational algebra.
3. SQL supports data types, integrity constraints, insertion and deletion of tuples, and modification of tables through DROP, ALTER, and CREATE statements.
4. Advanced query features include aggregation, null values, subqueries, joins, views and modification of the database.
The document summarizes key concepts from Chapter 2 of the textbook "Database System Concepts". It introduces the relational model and relational algebra. The relational model uses relations (tables) to store data and relational algebra contains operations like select, project, join etc. to manipulate these relations. Some key points covered are the structure of relational databases, database schema, keys, relational query languages like SQL, and the core operators of relational algebra.
The document discusses SQL database concepts including:
- The SQL data definition language allows specification of schemas, integrity constraints, and other metadata.
- Relations are defined using CREATE TABLE statements which specify attributes and their data types.
- Basic queries use SELECT, FROM, and WHERE clauses to retrieve and filter tuples from one or more relations.
- Integrity constraints like PRIMARY KEY and NOT NULL can be defined to enforce data validity.
- SQL supports operations like JOIN, aggregation, sorting, and more.
The document discusses key concepts of relational databases and relational algebra. It defines what a relation is as a set of tuples with attributes, and covers attribute types, keys, relations schemas and instances. It also summarizes the core relational algebra operations of selection, projection, join, union, difference and Cartesian product and how they are used to manipulate and query relations.
The document provides an overview of the basic structure and features of the SQL language, including: select, from, where clauses; aggregate functions; set operations; null values; and more. It describes the typical components of an SQL query, how they map to relational algebra operations, and provides examples to illustrate various SQL concepts and capabilities.
Relational Algebra and relational queries .pptShahidSultan24
This document describes chapter 6 of the textbook "Database System Concepts, 6th Ed." which covers formal relational query languages. It introduces relational algebra as a procedural query language with basic operators like select, project, union, set difference, cartesian product, and rename. It also covers tuple and domain relational calculus. Examples of relational algebra queries are provided to find the largest salary or names of instructors and courses taught. Additional relational algebra concepts like composition of operations, set intersection, natural join, assignment, and outer join are also summarized.
This document contains lecture slides about Chapter 4 of the textbook "Database System Concepts, 7th Ed." by Silberschatz, Korth and Sudarshan. The chapter covers intermediate SQL topics like join expressions, integrity constraints, SQL data types and schemas, views, transactions, indexes and authorization. Specific topics discussed include natural joins, outer joins, integrity constraints, user-defined types, schemas and views.
This document discusses formal relational query languages, including relational algebra, tuple relational calculus, and domain relational calculus. Relational algebra is a procedural query language that uses operators like select, project, join, and set difference. Tuple relational calculus and domain relational calculus are nonprocedural query languages that use predicates and quantifiers to specify queries. Examples of queries written in each language are provided to illustrate their syntax and capabilities.
This document provides an overview of SQL (Structured Query Language) including its history, data definition and manipulation capabilities. Key topics covered include SQL's data types, basic queries using SELECT, FROM and WHERE clauses, joins, aggregation, null values, triggers and indexes. The document also discusses SQL standards over time and commercial database implementations of SQL features.
This chapter discusses SQL (Structured Query Language), the most popular language for interacting with relational database management systems. The chapter covers SQL's data definition language for defining schemas, domains, and integrity constraints. It also covers the basic SELECT statement structure for queries with FROM, WHERE, and JOIN clauses. Additional topics include views, data modification, and aggregation functions.
This chapter discusses the SQL (Structured Query Language) which is used for managing data in relational database management systems. It covers key topics in SQL including data definition, basic query structure using SELECT, FROM and WHERE clauses, set operations, aggregate functions, null values, nested subqueries, views, data modification and joined relations. The document provides examples of SQL statements for creating tables, defining domains, inserting data, querying, and modifying data.
The document discusses key concepts of the relational database model from Chapter 2 of the textbook "Database System Concepts, 6th Edition". It describes the structure of relations, which are tables made up of rows and columns. It defines entity types like attributes and tuples, and explains primary keys, foreign keys, and relationship types like one-to-one and one-to-many. It also introduces the algebraic operations of the relational algebra, which provides a declarative query language for relational databases including selection, projection, join, union and set differences.
This document provides an overview of formal relational query languages, including relational algebra, tuple relational calculus, and domain relational calculus. It discusses the basic operators of relational algebra like select, project, union, and difference. It also provides examples of queries expressed in both tuple relational calculus and domain relational calculus, and covers concepts like safety of expressions. The document is from the 6th edition of the textbook "Database System Concepts" and is intended to teach formal query languages for relational databases.
i. Being able to communicate effectively is perhaps the most important of all life skills. It is what enables us to pass information to other people, and to understand what is said to us. You only have to watch a baby listening intently to its mother and trying to repeat the sounds that she makes to understand how fundamental is the urge to communicate.
ii. Communication, at its simplest, is the act of transferring information from one place to another. It may be vocally (using voice), written (using printed or digital media such as books, magazines, websites or emails), visually (using logos, maps, charts or graphs) or non-verbally (using body language, gestures and the tone and pitch of voice). In practice, it is often a combination of several of these.
iii. Communication skills may take a lifetime to master—if indeed anyone can ever claim to have mastered them. There are, however, many things that you can do fairly easily to improve your communication skills and ensure that you are able to transmit and receive information effectively.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
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