Jacky  Hoeger

Jacky Hoeger

1591600560

Types of Studies — Observational Studies In Statistics

Types of Studies — Observational Studies In Statistics
There are broadly two types of studies in the statistics, classified according to how data is collected. Observational studies and Experimental studies. Conversely, there are two types of studies according to how data is analysed; Exploratory Data Analysis and Confirmatory Data Analysis.

#data-analysis #statistics #data-science #data #programming

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Types of Studies — Observational Studies In Statistics

Benefits of Taking Education Loan to Study Abroad : edu-visa

As the cost of education is getting higher rapidly, a lot of students have to give up on their dreams to study abroad. Canada is known as the best country to study abroad for Indian students. You will need approximately between INR 12,50,000 to 19,00,000 a year if you’re an Indian citizen and looking forward to studying in Canada…Read more

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Arvel  Parker

Arvel Parker

1593156510

Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object

a**=25+**85j

type**(a)**

output**:<class’complex’>**

b**={1:10,2:“Pinky”****}**

id**(b)**

output**:**238989244168

Built-in data types in Python

a**=str(“Hello python world”)****#str**

b**=int(18)****#int**

c**=float(20482.5)****#float**

d**=complex(5+85j)****#complex**

e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**

f**=tuple((“python”,“easy”,“learning”))****#tuple**

g**=range(10)****#range**

h**=dict(name=“Vidu”,age=36)****#dict**

i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**

j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**

k**=bool(18)****#bool**

l**=bytes(8)****#bytes**

m**=bytearray(8)****#bytearray**

n**=memoryview(bytes(18))****#memoryview**

Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger

age**=**18

print**(age)**

Output**:**18

Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).

String

The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.

“Hello”+“python”

output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

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Jacky  Hoeger

Jacky Hoeger

1591600560

Types of Studies — Observational Studies In Statistics

Types of Studies — Observational Studies In Statistics
There are broadly two types of studies in the statistics, classified according to how data is collected. Observational studies and Experimental studies. Conversely, there are two types of studies according to how data is analysed; Exploratory Data Analysis and Confirmatory Data Analysis.

#data-analysis #statistics #data-science #data #programming

Study in France Student Visa, Universities,Tuition Cost, Admission Process

There are many places to study worldwide. If you are looking for a country with great education along with cultural experience, then France is the place for you. Every year many students go to study in France. You can pursue various programs, bachelor’s o and even your masters in France. There are many recognized universities to study in France for international students. Here at Abroad Admission we provide quality education for foreign studies. So if you are planning to study abroad in france, then we are there to assist you professionally!

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Using Statistical Distance Metrics for Machine Learning Observability

Introduction

Statistical Distances are used to quantify the distance between two distributions and are extremely useful in ML observability. This blog post will go into statistical distance measures and how they are used to detect common machine learning model failure modes.

Why Statistical Distance Checks?

Data problems in Machine Learning can come in a wide variety that range from sudden data pipeline failures to long-term drift in feature inputs. Statistical distance measures give teams an indication of changes in the data affecting a model and insights for troubleshooting. In the real world post model-deployment, these data distribution changes can come in a myriad of different ways and cause model performance issues.

Here are some real-world data issues that we’ve seen in practice.

  • Incorrect data indexing mistake — breaks upstream mapping of data
  • Bad text handling — causes new tokens model has never seen
  • Mistakes Handling Case
  • Problems with New Text String
  • Different sources of features with different coordinates or indexing
  • 3rd Party data source makes a change dropping a feature, changing format, or moving data
  • Newly deployed code changes an item in a feature vector
  • Periodic daily collection of data fails, causing missing values or lack of file
  • Software engineering changes the meaning of a field
  • 3rd Party Library Functionality Changes
  • Presumption of valid format that changes and is suddenly not valid
  • Date string changes format
  • System naturally evolves and feature shifts
  • Outside world drastically changes (e.g., the COVID-19 pandemic) and every feature shifts
  • Drastic increase in volume skews statistics

These are examples of data issues that can be caught using statistical distance checks.

Where To Use Statistical Distance Checks

Statistical distances can be used to analyze

  • Model Inputs: Changes in inputs into a model, especially critical most important features or features that might be the output of an upstream model.
  • Model Outputs: Changes in outputs of a model
  • Actuals: Changes in actuals (ground truth received). In some cases, the ground truth might not be available within a short time horizon after prediction. In these cases, teams often use proxy metrics/data.

These checks are extremely insightful for model performance troubleshooting and they allow teams to get in front of major model issues before these problems affect business outcomes. In this image below, there are statistical checks that can be done on model inputs (features) and model outputs (predictions).

#statistics #observability #distribution #artificial-intelligence #machine-learning