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# The Best estimation method in Data Analysis

In many data analysis interviews, the examiner likes to ask questions like:

“Without any public reference materials, estimate the number of newborns born this year?”

“To estimate the total weight of household garbage collected in your city each year?”

“Estimating how many people are picking their noses in the world at this very moment?

Such seemingly unreasonable questions are often seen in data analysis interviews. If the candidate does not have certain data thinking, the first reaction must be:

“Are you crazy? How can I answer this kind of question?”

If you answer that or guess a random number, then congratulations, you can go and prepare for the next interview.

Such a question may seem unanswerable, but in reality, the examiner is testing your ability to think about data and how to use some hypothetical reasoning and experience to deduce the correct answer, given the lack of clarity in the data and various constraints.

This brings us to the data analysis idea we’re sharing today, the Fermi problem.

The magical Fermi problem

Fermi is an Italian-American physicist who won the Nobel Prize in Physics in 1938, and what he is more well known to the world is a seemingly ridiculous question thrown out of thin air in his class at the University of Chicago.

“How many piano tuners are there in Chicago?”

The students who heard this question were all dazed, and Fermi suggested that if such a seemingly huge problem is encountered, this problem can be broken down into small problems that are easy to operate and recognize, and the problem is estimated based on guesses and assumptions.

This is the core of Fermi’s thinking: logical disassembly.

He says, dismantles a huge, abstract, and complex problem into tiny, concrete, and simple problems, and then dismantle these small problems further, as long as the logical relationship is guaranteed.

This method generally called the “logical tree method.

Now let’s go back and see how Fermi answered it?

First of all, Fermi dismantled this problem into two problems like as shown in the figure below

Why should it be disassembled into these two problems?

Because we need to follow a certain logical relationship when disassembling the problem, this logical relationship must ensure that it can completely cover all the scope of the Fermi problem.

For example, the disassembly logic of this problem is:

Then let’s take a look. What are the working hours of all the tuners in Chicago every year? Fermi continued to dismantle this problem into three problems:

• How many pianos are there in Chicago?
• How often is each piano tuned?
• How long is a tuner’s tuning?

The disassembly logic for this problem is:

Now the question becomes how many pianos does Chicago have and how many times do they tune each year.

Can such a problem continue to be dismantled?

The answer is no, this kind of problem belongs to the basic problem of the Fermi problem.

The determination of the basic problem involves another main idea of ​​the Fermi problem: problem estimation.

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## Procedure To Become An Air Hostess/Cabin Crew

Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

Physical and Medical standards –

• Females must be 157 cm in height and males must be 170 cm in height (for males). This parameter may vary from one airline toward the next.
• The candidate's body weight should be proportional to his or her height.
• Candidates with blemish-free skin will have an advantage.
• Physical fitness is required of the candidate.
• Eyesight requirements: a minimum of 6/9 vision is required. Many airlines allow applicants to fix their vision to 20/20!
• There should be no history of mental disease in the candidate's past.
• The candidate should not have a significant cardiovascular condition.

You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

As can be seen from the preceding information, a 10+2 pass is the minimal educational need for becoming an air hostess in India. So, if you have a 10+2 certificate from a recognized board, you are qualified to apply for an interview for air hostess positions!

You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).

So That I may recommend, joining Special Personality development courses, a learning gallery that offers aviation industry courses by AEROFLY INTERNATIONAL AVIATION ACADEMY in CHANDIGARH. They provide extra sessions included in the course and conduct the entire course in 6 months covering all topics at an affordable pricing structure. They pay particular attention to each and every aspirant and prepare them according to airline criteria. So be a part of it and give your aspirations So be a part of it and give your aspirations wings.

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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

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## How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

• Building/collecting data
• Cleaning/filtering data
• Organizing data

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## Getting Started With Data Lakes

### Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

### Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

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