1580738513

Complete MySQL course. MySQL Tutorial - Learn MySQL from scratch and go from beginner to advanced in MySQL.

Welcome to this course “Complete MySQL Course: Beginner to Advanced”. In this course you will learn from scratch. We will assume that you are a complete beginner and by the end of the course you will be at advanced level. This course contain Real-World examples and Hands On practicals. We will guide you step by step so that you can understand better. This course will allow you to work on the Real-World as a professional.

What you’ll learn

- Learn The Basics
- Learn Advanced Methods
- Step By Step Instructions So That You Can Go From Zero To Hero
- A Complete Tutorial Explaining Everything You Need To Know
- Real-World Examples With Hands On Tutorials
- Get Answers To Your Every Single Questions

#mysql #sql #databases #web-development

1595905879

HTML to Markdown

MySQL is the all-time number one open source database in the world, and a staple in RDBMS space. DigitalOcean is quickly building its reputation as the developers cloud by providing an affordable, flexible and easy to use cloud platform for developers to work with. MySQL on DigitalOcean is a natural fit, but what’s the best way to deploy your cloud database? In this post, we are going to compare the top two providers, DigitalOcean Managed Databases for MySQL vs. ScaleGrid MySQL hosting on DigitalOcean.

At a glance – TLDR

ScaleGrid Blog - At a glance overview - 1st pointCompare Throughput

ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads. Read now

ScaleGrid Blog - At a glance overview - 2nd pointCompare Latency

On average, ScaleGrid achieves almost 30% lower latency over DigitalOcean for the same deployment configurations. Read now

ScaleGrid Blog - At a glance overview - 3rd pointCompare Pricing

ScaleGrid provides 30% more storage on average vs. DigitalOcean for MySQL at the same affordable price. Read now

MySQL DigitalOcean Performance Benchmark

In this benchmark, we compare equivalent plan sizes between ScaleGrid MySQL on DigitalOcean and DigitalOcean Managed Databases for MySQL. We are going to use a common, popular plan size using the below configurations for this performance benchmark:

Comparison Overview

ScaleGridDigitalOceanInstance TypeMedium: 4 vCPUsMedium: 4 vCPUsMySQL Version8.0.208.0.20RAM8GB8GBSSD140GB115GBDeployment TypeStandaloneStandaloneRegionSF03SF03SupportIncludedBusiness-level support included with account sizes over $500/monthMonthly Price$120$120

As you can see above, ScaleGrid and DigitalOcean offer the same plan configurations across this plan size, apart from SSD where ScaleGrid provides over 20% more storage for the same price.

To ensure the most accurate results in our performance tests, we run the benchmark four times for each comparison to find the average performance across throughput and latency over read-intensive workloads, balanced workloads, and write-intensive workloads.

Throughput

In this benchmark, we measure MySQL throughput in terms of queries per second (QPS) to measure our query efficiency. To quickly summarize the results, we display read-intensive, write-intensive and balanced workload averages below for 150 threads for ScaleGrid vs. DigitalOcean MySQL:

ScaleGrid MySQL vs DigitalOcean Managed Databases - Throughput Performance Graph

For the common 150 thread comparison, ScaleGrid averages almost 40% higher throughput over DigitalOcean for MySQL, with up to 46% higher throughput in write-intensive workloads.

#cloud #database #developer #digital ocean #mysql #performance #scalegrid #95th percentile latency #balanced workloads #developers cloud #digitalocean droplet #digitalocean managed databases #digitalocean performance #digitalocean pricing #higher throughput #latency benchmark #lower latency #mysql benchmark setup #mysql client threads #mysql configuration #mysql digitalocean #mysql latency #mysql on digitalocean #mysql throughput #performance benchmark #queries per second #read-intensive #scalegrid mysql #scalegrid vs. digitalocean #throughput benchmark #write-intensive

1595054220

In chapter 1 and chapter 2 , we got an introduction to PyTorch, some interesting functions used in PyTorch, different algorithms used in machine learning and a brief but solid introduction to linear regression. In this chapter we are going to build a Linear Regression model from scratch that is, without the use of any PyTorch built-ins. Without further ado, let’s begin.

Predict the yield of apples and oranges (dependent or target variables) by observing the average temperature, rainfall and humidity (independent or predictor variables ) for a region.

We know that in linear regression target variables are considered as the weighted sum of independent variables, offset by some constant called bias (we add this offset so that when all the independent variables become zero the output should not become zero).

This statement can be represented mathematically as :-

These equations show that dependent variables share a linear relation with dependent variables.

We should find the set of weights and bias that is, w11,w12,…w23 and b1 and b2 by analyzing training data to predict the yield of apples and oranges of a new region based on avg. temp. , rainfall and humidity. This is done by adjusting the weights and biases slightly.

Training data can be considered as two numpy arrays (matrices), input and target one row per observation.

Generated by Author

We now convert the numpy arrays to tensors for this purpose we use torch.from_numpy(), this method takes a numpy array as input and output tensor.

Generated by Author

Weights (w11,w12,…w23) and biases (b1,b2) can be represented as matrices and first row and first bias element is used to predict the yield of apples and the second row and element the yield of oranges.

Generated by Author

In this code snippet we create two tensors w and b using torch.randn() which will act as our weight and bias matrices.The torch.randn() function creates two matrices with element picked randomly from a normal distribution with mean=0 and standard deviation=1.

#deep-learning #linear-regression #scratch #tutorial #machine-learning #deep learning

1596728880

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.

- 1. Install R
- 2. Install RStudio
- 3. First Look at RStudio
- 4. The Console
- 5. The Global Environment
- 6. Install the
`[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb26184b)`

Packages - 7. Load the
`[tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b)`

Packages into Memory - 8. Identify Loaded Packages
- 9. Get Help on a Package
- 10. Get Help on a Function
- 11. RStudio Projects
- 12. Save Your “Real” Work. Delete the Rest.
- 13. R Scripts
- 14. Run Code
- 15. Access Built-in Datasets
- 16. Style
- 17. Reproducible Reports with R Markdown
- 18. Use RStudio Cloud
- 19. Get Your Hands Dirty!
- Additional Resources
- Bonus: Cheatsheets

#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials

1599097440

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

1596513720

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:

- Free of duplicate rows/values
- Error-free (e.g. free of misspellings)
- Relevant (e.g. free of special characters)
- The appropriate data type for analysis
- Free of outliers (or only contain outliers have been identified/understood), and
- Follows a “tidy data” structure

Common symptoms of messy data include data that contain:

- Special characters (e.g. commas in numeric values)
- Numeric values stored as text/character data types
- Duplicate rows
- Misspellings
- Inaccuracies
- White space
- Missing data
- Zeros instead of null values

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:

- rollingsales_bronx.xls
- rollingsales_brooklyn.xls
- rollingsales_manhattan.xls
- rollingsales_queens.xls
- rollingsales_statenisland.xls

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 `readxl`

package. 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:

```
install.packages("tidyverse")
```

Once the package is installed, load it to memory:

```
library(tidyverse)
```

Now that `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…
```

The `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