This database design course will help you understand database concepts and give you a deeper grasp of database design.
Database design is the organisation of data according to a database model. The designer determines what data must be stored and how the data elements interrelate. With this information, they can begin to fit the data to the database model.
⭐️ Contents ⭐
⌨️ (0:00:00) Introduction
⌨️ (0:03:12) What is a Database?
⌨️ (0:11:04) What is a Relational Database?
⌨️ (0:23:42) RDBMS
⌨️ (0:37:32) Introduction to SQL
⌨️ (0:44:01) Naming Conventions
⌨️ (0:47:16) What is Database Design?
⌨️ (1:00:26) Data Integrity
⌨️ (1:13:28) Database Terms
⌨️ (1:28:28) More Database Terms
⌨️ (1:38:46) Atomic Values
⌨️ (1:44:25) Relationships
⌨️ (1:50:35) One-to-One Relationships
⌨️ (1:53:45) One-to-Many Relationships
⌨️ (1:57:50) Many-to-Many Relationships
⌨️ (2:02:24) Designing One-to-One Relationships
⌨️ (2:13:40) Designing One-to-Many Relationships
⌨️ (2:23:50) Parent Tables and Child Tables
⌨️ (2:30:42) Designing Many-to-Many Relationships
⌨️ (2:46:23) Summary of Relationships
⌨️ (2:54:42) Introduction to Keys
⌨️ (3:07:24) Primary Key Index
⌨️ (3:13:42) Look up Table
⌨️ (3:30:19) Superkey and Candidate Key
⌨️ (3:48:59) Primary Key and Alternate Key
⌨️ (3:56:34) Surrogate Key and Natural Key
⌨️ (4:03:43) Should I use Surrogate Keys or Natural Keys?
⌨️ (4:13:07) Foreign Key
⌨️ (4:25:15) NOT NULL Foreign Key
⌨️ (4:38:17) Foreign Key Constraints
⌨️ (4:49:50) Simple Key, Composite Key, Compound Key
⌨️ (5:01:54) Review and Key Points…HA GET IT? KEY points!
⌨️ (5:10:28) Introduction to Entity Relationship Modeling
⌨️ (5:17:34) Cardinality
⌨️ (5:24:41) Modality
⌨️ (5:35:14) Introduction to Database Normalization
⌨️ (5:39:48) 1NF (First Normal Form of Database Normalization)
⌨️ (5:46:34) 2NF (Second Normal Form of Database Normalization)
⌨️ (5:55:00) 3NF (Third Normal Form of Database Normalization)
⌨️ (6:01:12) Indexes (Clustered, Nonclustered, Composite Index)
⌨️ (6:14:36) Data Types
⌨️ (6:25:55) Introduction to Joins
⌨️ (6:39:23) Inner Join
⌨️ (6:54:48) Inner Join on 3 Tables
⌨️ (7:07:41) Inner Join on 3 Tables (Example)
⌨️ (7:23:53) Introduction to Outer Joins
⌨️ (7:29:46) Right Outer Join
⌨️ (7:35:33) JOIN with NOT NULL Columns
⌨️ (7:42:40) Outer Join Across 3 Tables
⌨️ (7:48:24) Alias
⌨️ (7:52:13) Self Join
#Database #SQL #WebDev #database
A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.
This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!
In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2
#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials
In this course, we’ll be looking at database management basics and SQL using the MySQL RDBMS.
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (2:36) What is a Database?
⌨️ (23:10) Tables & Keys
⌨️ (43:31) SQL Basics
⌨️ (52:26) MySQL Windows Installation
⌨️ (1:01:59) MySQL Mac Installation
⌨️ (1:15:49) Creating Tables
⌨️ (1:31:05) Inserting Data
⌨️ (1:38:17) Constraints
⌨️ (1:48:11) Update & Delete
⌨️ (1:56:11) Basic Queries
⌨️ (2:08:37) Company Database Intro
⌨️ (2:14:05) Creating Company Database
⌨️ (2:30:27 ) More Basic Queries
⌨️ (2:26:24) Functions
⌨️ (2:45:13) Wildcards
⌨️ (2:53:53) Union
⌨️ (3:01:36) Joins
⌨️ (3:11:49) Nested Queries
⌨️ (3:21:52) On Delete
⌨️ (3:30:05) Triggers
⌨️ (3:42:12) ER Diagrams Intro
⌨️ (3:55:53) Designing an ER Diagram
⌨️ (4:08:34) Converting ER Diagrams to Schemas
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=HXV3zeQKqGY&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=8
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In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.
If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.
[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b)Packages into Memory
#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials
How to make a good database design? Why we should create a good design database? Database design is an essential skill of a software engineer. In some interviews, the interviewer can ask you a few questions about it. As far as I know, we have some database principles. There are a lot of definitions about them and you can search on google for more details. Based on my experience, I’ll write it simply.
After reading this article, you will understand things:
Firstly, What is database design?
“Database Design is the organization of data according to a database model. The designer determines what data must be stored and how the data elements interrelate.” Source: wikipedia.org
Database design is a part of the Design Process when we develop software. Before doing database design, we have to complete software architecture (N-tier layer, Microservice, …) at the high-level. Database design is a very important step at the low-level. Design Process often creates by Senior Software Engineer or Software Architect who has a lot of experience in the IT field.
Development Process. Source: Internet.
With a medium or big system, we usually choose and combine some databases to achieve our purpose. We need to support transactions and relationships: MySQL or PostgreSQL or SQL Server. We need to save flexible data: MongoDB(unstructured data). Support caching (Redis: key-value, sorted set, list, …), support full-text searching(Elastic Search, …), and so on.
Depends on your project, you should choose and combine some databases appropriately and wisely. There’s not the best database, only have database appropriately. We should take advantage of databases and know the limit/issues of them. In this article, I’ll only write about DBMS(Database Management System): MySQL. The reason is it’s complex more than NoSQL database such as MongoDB, Redis, and so on.
In some projects, the Senior Software Engineer or Solution Architect could request to make a Class Diagram and ERD (Entity Relationship Diagram). What the difference between the Class Diagram and ERD?
In my opinion, we should make ERD and don’t create Class Diagrams unless we have some special reasons. This depends on your project.
#erd #database-design #good-database-design #design-db-process #database
What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:
Common symptoms of messy data include data that contain:
In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:
As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!
Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package
readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse
readr package function
read_csv() is the function to use (we’ll cover that later).
Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:
The Brooklyn Excel file
Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the
readxlpackage. We specify the function argument
skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:
library(readxl) # Load Excel files brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)
Note we saved this dataset with the variable name
brooklyn for future use.
The tidyverse offers a user-friendly way to view this data with the
glimpse() function that is part of the
tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:
Once the package is installed, load it to memory:
tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:
glimpse(brooklyn) ## Observations: 20,185 ## Variables: 21 ## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "… ## $ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA… ## $ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 … ## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "… ## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6… ## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,… ## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,… ## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "… ## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T… ## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,… ## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112… ## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1… ## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0… ## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1… ## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3… ## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22… ## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1… ## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "… ## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "… ## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, … ## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…
glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.
#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials