1598905320

One of the most important concepts discussed in the context of inferential data analysis is the idea of sampling distributions. Understanding sampling distributions helps us better comprehend and interpret results from our descriptive as well as predictive data analysis investigations. Sampling distributions are also frequently used in decision making under uncertainty and hypothesis testing.

You may already be familiar with the idea of probability distributions. A probability distribution gives us an understanding of the probability and likelihood associated with values (or range of values) that a random variable may assume. A random variable is a quantity whose value (outcome) is determined randomly. Some examples of a random variable include, the monthly revenue of a retail store, the number of customers arriving at a car wash location on any given day, the number of accidents on a certain highway on any given day, weekly sales volume at a retail store, etc. Although the outcome of a random variable is random, the probability distribution allows us to gain and understanding about the likelihood and probabilities of different values occurring in the outcome. Sampling distributions are probability distributions that we attach to sample statistics of a sample.

A sample statistic (also known simply as a statistic) is a value learned from a sample. Here is an example, suppose you collect the results of a survey filled out by 250 randomly selected individuals who live in a certain neighborhood. Based on the survey results you realize that the average annual income of the individuals in this sample is $82,512. This is a sample statistic and is denoted by _x̅ = $82,512. _The sample mean is also a random variable (denoted by X̅) with a probability distribution. The probability distribution for X̅ is called the sampling distribution for the sample mean. Sampling distribution could be defined for other types of sample statistics including sample proportion, sample regression coefficients, sample correlation coefficient, etc.

You might be wondering why X̅ is a random variable while the sample mean is just a single number! The key to understanding this lies in the idea of *sample to sample variability*. This idea refers to the fact that samples drawn from the same population are not identical. Here’s an example, suppose in the example above, instead of conducting only one survey of 250 individuals living in a particular neighborhood, we conducted 35 samples of the same size in that neighborhood. If we calculated the sample mean _x̅ _for each of the 35 samples, you would be getting 35 different values. Now suppose, hypothetically, we conducted many many surveys of the same size in that neighborhood. We would be getting many many (different) values for sample means. The distribution resulting from those sample means is what we call the sampling distribution for sample mean. Thinking about the sample mean from this perspective, we can imagine how X̅ (note the big letter) is the random variable representing sample means and _x̅ _(note the small letter)is just one realization of that random variable.

#hypothesis-testing #python #distribution #sampling-distribution #statistics

1624298520

In a series of weekly articles, I will be covering some important topics of statistics with a twist.

The goal is to use Python to help us get intuition on complex concepts, empirically test theoretical proofs, or build algorithms from scratch. In this series, you will find articles covering topics such as random variables, sampling distributions, confidence intervals, significance tests, and more.

At the end of each article, you can find exercises to test your knowledge. The solutions will be shared in the article of the following week.

Articles published so far:

- Bernoulli and Binomial Random Variables with Python
- From Binomial to Geometric and Poisson Random Variables with Python
- Sampling Distributions with Python

As usual, the code is available on my GitHub.

#statistics #distribution #python #machine-learning #sampling distributions with python #sampling distributions

1595334123

I consider myself an active StackOverflow user, despite my activity tends to vary depending on my daily workload. I enjoy answering questions with angular tag and I always try to create some working example to prove correctness of my answers.

To create angular demo I usually use either plunker or stackblitz or even jsfiddle. I like all of them but when I run into some errors I want to have a little bit more usable tool to undestand what’s going on.

Many people who ask questions on stackoverflow don’t want to isolate the problem and prepare minimal reproduction so they usually post all code to their questions on SO. They also tend to be not accurate and make a lot of mistakes in template syntax. To not waste a lot of time investigating where the error comes from I tried to create a tool that will help me to quickly find what causes the problem.

```
Angular demo runner
Online angular editor for building demo.
ng-run.com
<>
```

Let me show what I mean…

There are template parser errors that can be easy catched by stackblitz

It gives me some information but I want the error to be highlighted

#mean stack #angular 6 passport authentication #authentication in mean stack #full stack authentication #mean stack example application #mean stack login and registration angular 8 #mean stack login and registration angular 9 #mean stack tutorial #mean stack tutorial 2019 #passport.js

1598905320

One of the most important concepts discussed in the context of inferential data analysis is the idea of sampling distributions. Understanding sampling distributions helps us better comprehend and interpret results from our descriptive as well as predictive data analysis investigations. Sampling distributions are also frequently used in decision making under uncertainty and hypothesis testing.

You may already be familiar with the idea of probability distributions. A probability distribution gives us an understanding of the probability and likelihood associated with values (or range of values) that a random variable may assume. A random variable is a quantity whose value (outcome) is determined randomly. Some examples of a random variable include, the monthly revenue of a retail store, the number of customers arriving at a car wash location on any given day, the number of accidents on a certain highway on any given day, weekly sales volume at a retail store, etc. Although the outcome of a random variable is random, the probability distribution allows us to gain and understanding about the likelihood and probabilities of different values occurring in the outcome. Sampling distributions are probability distributions that we attach to sample statistics of a sample.

A sample statistic (also known simply as a statistic) is a value learned from a sample. Here is an example, suppose you collect the results of a survey filled out by 250 randomly selected individuals who live in a certain neighborhood. Based on the survey results you realize that the average annual income of the individuals in this sample is $82,512. This is a sample statistic and is denoted by _x̅ = $82,512. _The sample mean is also a random variable (denoted by X̅) with a probability distribution. The probability distribution for X̅ is called the sampling distribution for the sample mean. Sampling distribution could be defined for other types of sample statistics including sample proportion, sample regression coefficients, sample correlation coefficient, etc.

You might be wondering why X̅ is a random variable while the sample mean is just a single number! The key to understanding this lies in the idea of *sample to sample variability*. This idea refers to the fact that samples drawn from the same population are not identical. Here’s an example, suppose in the example above, instead of conducting only one survey of 250 individuals living in a particular neighborhood, we conducted 35 samples of the same size in that neighborhood. If we calculated the sample mean _x̅ _for each of the 35 samples, you would be getting 35 different values. Now suppose, hypothetically, we conducted many many surveys of the same size in that neighborhood. We would be getting many many (different) values for sample means. The distribution resulting from those sample means is what we call the sampling distribution for sample mean. Thinking about the sample mean from this perspective, we can imagine how X̅ (note the big letter) is the random variable representing sample means and _x̅ _(note the small letter)is just one realization of that random variable.

#hypothesis-testing #python #distribution #sampling-distribution #statistics

1596094635

**What is MEAN Stack Developer?**

**MEAN Stack** Developer is a programmer who operates on the MEAN stack. He works on the backend plus the front end of the application. They are all JavaScript based and therefore a MEAN developer should have excellent JS knowledge, for which you can join MEAN Stack Online Training Program.

**Skillets of MEAN Stack develope**r

• Knowledge of working on the Front-end and Back-end processes

• Work with HTML & CSS

• Understanding of programming templates and architecture design guidelines

• Knowledge of continuous integration, web development, and cloud technologies

• Excellent understanding of DB architecture

• Knowledge of SDLC and experience developing in an Agile environment

• Collaborate with the IT team to build robust systems to support business objectives

• Hands-on experience on Mongo, Angular, Express, Node.

Future of MEAN stack Developer

Being, a Mean stack developer is a highly desirable, challenging vocation. So, if you are ready to work on the diverse skill set and have the experience of working with various code languages and application, then you will become successful MEAN stack developer.

**Scope of MEAN stack developer**

MEAN Stack Development is the best career prospect today with unlimited growth and scope. The national Indian median salary was around 76K $ pa according to Glassdoor.com. All you need to do is get cracking on your skill set by joining MEAN Stack course in Delhi, earn your certification and through your job experience and continued experiential learning keep pace with newer iterations in technology.

Developers are available in various process streams like Backend, Frontend, Database, Testing, and Mobile Apps. Older practices were that as you gain experience and expertise you would pursue specialization and progress your career in that direction.

How Can I Start Learning Mean Stack Course Step By Step? Modern best practices have changed the trend.

**Skill upgrades and building proficiency in:**

• CSS

• HTML

• JavaScript

• Ruby, PHP, or Python which are all-purpose languages.

• Postgres, Oracle, or MySQL, relational-database systems.

• Any web-server which includes Nginx or Apache

• FreeBSD, Ubuntu, or CentOS deployment OS.

• Any system for instance GIT for version-control

By mastering one software technology required for every stack-part you will be in a position to create your own software and use it to add business value at your job.

#mean stack #mean stack training #mean stack certification online #mean stack online course #mean stack online training

1623263280

This blog is an abridged version of the talk that I gave at the Apache Ignite community meetup. You can download the slides that I presented at the meetup here. In the talk, I explain how data in Apache Ignite is distributed.

Inevitably, the evolution of a system that requires data storage and processing reaches a threshold. Either too much data is accumulated, so the data simply does not fit into the storage device, or the load increases so rapidly that a single server cannot manage the number of queries. Both scenarios happen frequently.

Usually, in such situations, two solutions come in handy—sharding the data storage or migrating to a distributed database. The solutions have features in common. The most frequently used feature uses a set of nodes to manage data. Throughout this post, I will refer to the set of nodes as “topology.”

The problem of data distribution among the nodes of the topology can be described in regard to the set of requirements that the distribution must comply with:

- Algorithm. The algorithm allows the topology nodes and front-end applications to discover unambiguously on which node or nodes an object (or key) is located.
- Distribution uniformity. The more uniform the data distribution is among the nodes, the more uniform the workloads on the nodes is. Here, I assume that the nodes have approximately equal resources.
- Minimal disruption. If the topology is changed because of a node failure, the changes in distribution should affect only the data that is on the failed node. It should also be noted that, if a node is added to the topology, no data swap should occur among the nodes that are already present in the topology.

#tutorial #big data #distributed systems #apache ignite #distributed storage #data distribution #consistent hashing