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Citizen Data Scientists: Who Are They & What Makes Them Special?

Citizen Data Scientists: Who Are They & What Makes Them Special?

Companies across the globe are focusing their attention on data science to unlock the potentials of their data. But, what remains crucial is finding well trained data scientists for building such advanced systems.

Today, a lot many organizations are seeking citizen data scientists – though the notion isn’t something new, the practice is fairly picking up pace amongst the industries. Say thanks to a number of factors, including perpetual improvement in the quality of tools and difficulty in finding properly skilled data scientists!

Gartner, a top notch analyst firm has been promoting this virgin concept for the past few years. In 2014, the firm predicted that the total number of citizen data scientists would expand 5X faster than normal data scientists through 2017. Although we are not sure if the number forecasted panned out right but what we know is that the proliferating growth of citizen data scientists exceeded our expectations.

Recently, Gartner analyst Carlie Idoine explained a citizen data scientist is one who “creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.” They are also termed as “power users”, who’ve the ability to perform cutting edge analytical tasks that require added expertise. “They do not replace the experts, as they do not have the specific, advanced data science expertise to do so. But they certainly bring their OWN expertise and unique skills to the process,” she added.

Of late, citizen data scientists have become critical assets to an organization. They help businesses discover key big data insights and in the process are being asked to derive answers from data that’s not available from regular relational database. Obviously, data can’t be queried through SQL, either. As a result, citizen data scientists are found leveraging machine learning models that end up generating predictions from a large number of data types. No wonder, SQL always sounds effective, but Python statistical libraries and Jupyter notebooks helps you further.

 A majority of industries leverages SQL; it has been data’s lingua franca for years. The sheer knowledge of how to write a SQL query to unravel a quiver of answers out of relational databases still remains a crucial element of company’s data management system as a whole lot of business data of companies are stored in their relational databases. Nevertheless, advanced machine learning tools are widely gaining importance and acceptance.

A wide array of job titles regarding citizen data scientists exists in the real world, and some of them are mutation of business analyst job profile. Depending on an organization’s requirements, the need for experienced analysts and data scientists varies.

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DataRobot, a pioneering proprietary data science and machine learning automation platform developer is recently found helping citizen data scientists through the power of automation. “There’s a lot happening behind the scenes that folks don’t realize necessarily is happening,” Jen Underwood, a BI veteran and the recently hired DataRobot’s director of product marketing said. “When I was doing data science, I would run one algorithm at a time. ‘Ok let’s wait until it ends, see how it does, and try another, one at a time.’ [With DataRobot] a lot of the steps I was taking are now automated, in addition to running the algorithms concurrently and ranking them.”

To everyone’s knowledge, Big Data Analytics is progressing, capabilities that were once restricted within certain domains of professionals are now being accessible by a wider pool of interested parties. So, if you are interested in this new blooming field of opportunities, do take a look at our business analyst training courses in Gurgaon. They would surely help you in charting down a successful analyst career.

 

The blog has been sourced fromdatanami.com/2018/08/13/empowering-citizen-data-science

 

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Business Intelligence: How to Enhance User Adoption?

Business Intelligence: How to Enhance User Adoption?

For business modernization, smart business intelligence solution is the key. Getting to the crux and leveraging vast pools of data that companies gain access to triggers encompassing digital transformation. BI tools not only let companies grasp the data but also develop actionable insights to smoothen the impactful decision-making capabilities and take companies towards future progress.

It’s not an out of ordinary kind of concept, for half a decade, companies have been utilizing these kinds of tools for better efficiency and productive outcomes, yet user adoption for BI tool remains relatively low.

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Reasons behind Lower User Adoption of Business Intelligence:

Guys at the helm of company affairs, including Chief Information Officers, Chief Technology Officers and Chief Data Officers may think it’s high time to incorporate Business Intelligence tools for smarter operations, but it may not have the same effect on the employees. Employees may not be much inspired!

It holds truer, especially for those employees, who have been in the workforce for long and haven’t for once used such intricate, new-age tools to decipher what data says. For them, old is gold – they prefer to continue their own kind of data analysis in the same way they have been doing for so many years.

How Companies Can Improve Data-Driven Mindsets?

In order to be ahead of the curve, the data mindset of the workforce needs to be changed. If businesses have to be completely data-driven, they can’t just take Business Intelligence lightly.

Here are a few ways business can drive user adoption of BI:

Introduce BI as a necessity, not luxury

Once understanding company data was considered as an added advantage to normal work procedures. But, in this age of digital transformation, it’s no longer a luxury but a necessity. And sooner the employees realize this, the better it becomes.

Employees across organizations should have thorough access to data. It boosts decision-making. By going completely data-driven, business intelligence user adoption will automatically improve. Along with employees, businesses too will benefit a lot from such adoption.

Promote Favorable Impacts of BI

Putting light on success stories of BI implementation helps! It’s being regarded as a powerful way to encourage budding data scientists and already in-workforce employees: the powerful impression of BI and its significant impacts on key performance indicators will tell a different story to the world.

The best way of doing it would be by developing an internal case study that will elucidate how a team after incorporating Business Intelligence fulfilled their desired organizational goals. For best results, let a manager or C-level employee present the case study to the workforce. Surely, this will enhance levels of user adoption of BI.

Continuous Training is a Must

Business Intelligence calls for no one-track solutions; the concept deals with almost endless opportunities, which means continuous training initiatives should be taken up to explore every facet of this cutting edge tool.

When an employee have deeper knowledge about a particular tool, they are more likely to derive maximum benefits out of it. So, by giving continuous training, through various FAQs, webinars and video tutorials, employees can now become very easily completely data-driven.

Now, following these easy yet effective tips, business leaders can increase their lower rates of BI adoption and stride towards full digital transformation of their companies, triggering impactful future goals.

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The blog has been sourced from — www.sisense.com/blog/make-business-intelligence-necessity-drive-user-adoption
 

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Top 5 Reasons to Feel Excited about Data Analytics This Year

TOP 5 REASONS TO FEEL EXCITED ABOUT DATA ANALYTICS THIS YEAR

‘Tis the year to be super excited about data analytics! Without further ado, let’s find out why:-

Cloud Infrastructure is Expanding and Fostering Fast-paced Innovations

Considering the recent trends in cloud data and related applications, 2018 is a critical time for cloud analytics. Businesses must steadily transition to a cloud environment and for that a robust and flexible analytics strategy is to be adopted. Through cloud analytics platforms businesses can leverage common data logic and unlock new analytic capabilities to plan, predict, discover, visualize, simulate and manage. In short, what businesses need is a hybrid mode that includes data, analytics and applications spread across multi-cloud and on-premise environments. Research suggests that by employing analytics that are built to work together businesses can increase the total cost of ownership (TCO) by 3-5 times and the return on investment (ROI) can be as high as 171%.

Source: ZDNet

The Power of Machine Learning Unleashed

Machine learning and artificial intelligence have made big progress in the last one year. Hence, automated and AI powered tools are becoming central in decision-making. The rapid growth in automation has profound effect on the way analytics is used. It can be said that machine learning is perking up analytics big time. With the help of automated technologies users can develop contextual insights with ease and uncover patterns from massive volumes of data. And data scientists are harnessing these automated technologies to drive scalable insights for smarter business processes.

Source: Tech Carpenter

The Spreadsheet is Nearing Retirement

The spreadsheet has come a long way since its inception. But, for many businesses it is time to move to better alternatives that are free from some of the inefficiencies and inaccuracies of spreadsheets. For these businesses the solution is shifting to cloud-based models that help connect operational plans to financial plans.

Source: GCN.com

Customer Experience is the Current Competitive Battleground

According to the Harris Interactive study, 88% customers prefer purchasing products or services from a company that offers great customer service over a company that provides the latest innovations. Quality customer experience is crucial for business growth. And for that companies must invest in CEM (customer experience management). CEM technology collects data from varied sources and uses advanced analytics to leverage historical experiences and access data fast. This platform ensures that customers are satisfied, their grievances are addressed and there’s an improvement in sales, profits and brand image.

Source: StoryMiners

Big data Industry to Grow 7 times in 7 years!

Studies suggest that the big data industry in India is likely to become a 20 billion dollar industry by 2015. It is expected that analytics and data science market will grow by 7 times in the next 7 years. Currently, the analytics and big data industry is worth an estimated $2.71 billion in annual revenues and is growing rapidly at a rate of 33.5% CAGR.

Source: Analytics India

Do you know that this year over 16,000 freshers have been hired in the analytics workforce of India? That’s an increase by 33% from last year’s 12,000! Join the big data bandwagon with a professional certificate from this reputed data analyst training institute in Delhi. One of the unique features of this data analyst course in Gurgaon is that it includes trainers who are industry-experts in this field and hence bring with them excellent domain experience.

 

References:

digitalistmag.com/cio-knowledge/2018/01/03/top-10-trends-for-analytics-in-2018-05668659

360logica.com/blog/10-reasons-excited-data-analytics-2018

analyticsindiamag.com/analytics-data-science-industry-in-india-study-2018-by-analytixlabs-aim

getcloudcherry.com/blog/competition-customer-experience

 

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5 Trends Shaping the Future of Data Analytics

5 Trends Shaping the Future of Data Analytics

Data Analytics is popular. The future of data science and analytics is bright and happening. Terms like ‘artificial intelligence’ and ‘machine learning’ are taking the world by storm.

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020, says Forbes, a leading business magazine.

 

Last year, at the DataHack Summit Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton shared some slivers of knowledge in the illuminating field of data science. He believes that the following trends will shape up the world of data analytics, and we can’t agree more.

Dive down to pore over a definitive list – thank us later!

Internet of Things (IoT)

Does IoT ring any bell? Yes, it does, because it’s nothing but evolved wireless networks. The market of this fascinating new breed of tech is expected to grow from $170.57 billion in 2017 to $561.04 billion by 2022 – reasons being advanced analytics and superior data processing techniques.

Artificial Intelligence

An improved version of AI is Augmented Intelligence – instead of replacing human intelligence, this new sophisticated AI program largely focuses on AI’s assistive characteristic, enhancing human intelligence. The word ‘Augmented’ stands for ‘to improve’ and together it reinforces the idea of amalgamating machine intelligence with human conscience to tackle challenges and form relationships.

Augmented Reality

Look forward to better performances and successful models? Data is the weapon of all battles. Augmented Reality is indeed a reality now. The recent launch of Apple ARkit is a pivotal development in bulk manufacturing of AR apps. The power of AR is now in the fingertips of all iPhone users, and the development of Google’s Tango is an added thrust.

Hyper Personalization

#KnowYourCustomer, it has become an indispensable part of today’s retail marketing; the better you know your customers, the higher are the chances of selling a product. Yes, you heard that right. And Google Home and Amazon Echo is boosting the ongoing operations.

Graph Analytics

Mapping relationships across wide volumes of well connected critical data is the essence of graph analytics. It’s an intricate set of analytics tools used for unlocking insightful questions and delivering more accurate results. A few use cases of graph analytics is as follows:

  • Optimizing airline and logistic routes
  • Extensive life science researches
  • Influencer analysis for social network communities
  • Crime detection, including money laundering

 
Advice: Be at the edge of data accumulation – because data is power, and data analytics is the power-device.

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The article has been sourced from – yourstory.com/2017/12/data-analytics-future-trends

 

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A Comprehensive Study on Analytics and Data Science India Jobs 2018

A Comprehensive Study on Analytics and Data Science India Jobs 2018

India accounts for 1 in 10 data science job openings worldwide – with about 90,000 vacancies, India ranks as the second-biggest analytics hub, next to the US – according to a recent study compiled by two renowned skilling platforms. The latest figure shows a 76% jump from the last year.

With the advent of artificial intelligence and its overpowering influence, the demand for skill-sets in machine learning, data science and analytics is increasing rapidly. Job creation in other IT fields has hit a slow-mode in India, making it imperative for people to look towards re-skilling themselves with new emerging technologies… if they want to stay relevant in the industry. Some newer roles have also started mushrooming, with which we are not even acquainted now.

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Top trends in analytics jobs in 2018 as follows:

  • The total number of data science and analytics jobs nearly doubled from 2017 to 2018.
  • There’s been a sharp contrast in the percentage increase of analytics job inventory in the past years – from 2015 to 2016, the number of analytics jobs increased by 52%, which increased by only 40% from 2014 to 2015.
  • Currently, if we go by the reports, nearly 50000 analytics job positions are currently available to get filled by suitable candidates. Although the exact numbers are difficult to ascertain.
  • Amazon, Goldman Sachs, Citi, E&Y, Accenture, IBM, HCL, JPMorgan Chase, KPMG and Capgemini – are 10 top-tier organizations with the highest number of analytics opening in India.

City Figures

Bengaluru is the IT hub of India and accounts for the largest share of the data science and analytics jobs in India. Approximately, it accounted for 27% of jobs till the quarter of the last year.

Tier-II cities also witnessed a surging trend in such roles from 7% to 14% in between 2017 and 2018 – as startups started operating out of these locations.

Delhi/NCR ranks second contributing 22% analytics jobs in India, followed by Mumbai with 17%.

Industry Figures

Right from hospitality, manufacturing and finance to automobiles, job openings seem to be in every sector, and not just limited to hi-tech industries.

Banking and financial sector continued to be the biggest job drivers in analytics domain. Almost 41% of jobs were posted from the banking sector alone, though the share fell from last year’s 46%.

Ecommerce and media and entertainment followed the suit and contributed to analytics job inventory. Also, the energy and utilities seem to have an uptick in analytics jobs, contributing to almost 15% of all analytics jobs, 4% hike from the last year’s figure.

Education Requirement Figures

In terms of education, almost 42% of data analytics job requirements are looking for a B.Tech or B.E degree in candidates. 26% of them prefer a postgraduate degree, while only 10% seeks an MBA or PGDM.

In a nutshell, 80% of employers resort to hiring analytics professionals who have an engineering degree or a postgraduate degree.

As a result, Data analyst course has become widely popular. It’s an intensive, in-demand skill training that is intended for business, marketing and operations managers, data analyst and professionals and financial industry professionals. Find a reputable data analyst training institute in Gurgaon and start getting trained from the experts today.

 

The article has been sourced from:

https://qz.com/1297493/india-has-the-most-number-of-data-analytics-jobs-after-us

https://analyticsindiamag.com/analytics-and-data-science-india-jobs-study-2017-by-edvancer-aim

 

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How Aspiring Data Scientists Should Choose a Suitable Programming Language for Data Science

How Aspiring Data Scientists Should Choose a Suitable Programming Language for Data Science

Data science is a fascinating and one of the fastest growing fields in the world to work in. This is why it’s becoming increasingly popular for data scientists to consider the potentials of programming languages-they form an integral part of data science.

Possessing incredible skills of programming instantly pumps up the chances of bagging a high-profile data science job, whereas the novices, who have never studied programming in their entire life have to struggle hard.

However, this is not all – only a sack of all-round programming skills won’t help you grab the sexiest job of 21st century, there are several things to consider before you set off on becoming a successful data scientist. And they are as follows:

Generality

For a true blue data scientist, it’s not enough to possess encompassing programming skills but also the aptitude for crunching numbers. Remember, a data scientist’s day is largely spent on sourcing and processing raw data for the purpose of data cleaning – no amount of smart set of programming languages or machine learning models would be of any help.

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Specificity

In advanced data science, learning knows no bounds – each time you get to reinvent something new. Learn to ace a wide array of packages and modules available in a chosen language. However, the extent of the use and application is subject to the domain-particular packages you are working on.

Performance

In few cases, optimizing the performance of the codes is essential, especially when tackling huge volumes of crucial data. Compiled languages are normally faster as compared to interpreted ones; in the same way, statically typed languages are more fail-proof than dynamically typed. As a result, an apparent trade-off exists against productivity.

With all these in mind, it’s time to delve into the most popular languages used in the field of data science – let’s start with R – it’s the most powerful open source language used for a gamut of statistical and data visualization applications, including neural networks, advanced plotting, non-linear regression, phylogenetics and lot more.

Next, we can’t help but brag about an excellent all-rounder – Python – a top notch programming language choice for all types of data scientists, seasoned and freshers. A large chunk of the data science process revolves around the cutting edge ETL process – this makes Python a universal language to excel at. Google’s Tensorflow is an added bonus point.

Lastly, SQL tops rank as a leading data processing language instead of being just an advanced analytical tool. Owing to its longevity and efficiency, SQL is deemed to be one of the most powerful weapons that modern data scientist should know of.

Parting Thoughts

In the end of the discussion, we now have a set of languages to consider for excelling data science – what you need to do is comprehend your usage requirements and compare generality, specificity and performance factors. This will help you surge towards a successful career minus the complexities associated.

DexLab Analytics offers top of the line Data Science Courses in Delhi for data enthusiasts. If you are interested in a data analyst course in Noida, drop by this esteemed institute and navigate through our in-demand courses.

 

The blog has been sourced from – 

https://medium.freecodecamp.org/which-languages-should-you-learn-for-data-science-e806ba55a81f

https://towardsdatascience.com/what-programming-language-should-aspiring-data-scientists-learn-875017ad27e0

http://bigdata-madesimple.com/how-i-chose-the-right-programming-language-for-data-science

 

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How Data Science Is Getting Better, Day by Day?

HOW DATA SCIENCE IS GETTING BETTER, DAY BY DAY?

In the latest Star Wars movie, the character of Rose Tico – a humble maintenance techie with a talent for tinkering is relatable; her role expands and responsibilities increase as the movie gets going, just like our data scientists. A chance encounter with Finn puts her into the frontlines of action, and by the end of the movie, she’s flying ski-speeders in the new galactic civil war, one of the most critical battles in the movie – with time, her role becomes more complex and demanding, but she never quivers and embraces the challenges to get the job done.

A lot many data scientists draw similarities with Rose’s character. In the last 5 years, the job role and responsibility of data analysts has undergone an unrecognizable change – as data proliferation is increasing in capacity and complexity, the responsibility is found shifting base from dedicated consultants to cross-functional, highly-skilled data teams, proficient enough in integrating skills together. Today’s data consultants need to complete tasks collaboratively to formulate trailblazing analysis that let businesses predict future success and growth pattern, effectively.

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Quite conventionally, the intense role of prediction falls on the sophisticated crop of data scientists, while business analysts are more oriented towards measuring churn. On the other hand, intricate tasks, like model construction or natural language processing are performed by an elite team of data professionals, armed with strong engineering expertise.

Said differently, the emergence of data manipulation languages, such as R and Python is surging – owing to their extensive usage and adaptability, businesses are biased towards implementing these languages for advanced analysis. Drawing inspiration from Rose’s character, each data scientist should adapt to newer technology and expectations, and enhance expertise and skills that’s needed for the new role.

However, acing the cutting edge programming languages and tools isn’t enough for the challenge – today, data teams need to visualize their results, like never before. The insights churned out of advanced machine learning are curated for consumption by business pioneers and operation teams. Thus, the results have to be crisp, clear and creatively presented. As a result, predictive tools are being combined with effective capability of Python and R with which analysts and stakeholders are quite familiar.

The whole big data industry is changing, and the demand for skilled big data analysts is sky-rocketing. In this tide of change, if you are not relying on advanced data analysis tools and predictive analytics, you are going to lag behind. Companies that analyze data, boost decision-making, and observe social media trends – changing with time – will have immense advantages over companies that don’t pay attention to these crucial parameters.

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No second thoughts, it’s an interesting time for data aspirants to make significant impacts in the whole data community and trigger fabulous business results. For professional training or to acquire new skills – drop by DexLab Analytics – their data Science Courses in Noida are outstanding.

The blog has been sourced from  dataconomy.com/2018/02/whole-new-world-data-teams

 

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Estimator Procedure under Simple Random Sampling: EXPLAINED

Estimator Procedure under Simple Random Sampling: EXPLAINED

In continuation with the previous introductory blog on sampling: An ABC Guide to Sampling Theory, we will take a closer look into the concept of the estimator procedure under Simple Random Sampling with the help of mathematical examples. It will help us understand the underlying phenomenon, the manner to be precise in which the estimator function of sampling works.

Simple random sampling (SRS) is a method of selecting a sample comprising ‘n’ number of sampling units out of the population of ‘N’ number of sampling units such that every sampling unit has an equal chance of being chosen.

The Estimator Procedure under Simple Random Sampling

The process of selection of a sample under SRS (Simple Random Sampling) is random. This means, each number of the population has an equal probability of getting selected, which makes each of the observation identical and independently distributed.

The statistic chosen by the investigation of estimation of random samples need to satisfy a set of certain properties given below:

  1. Unbiasedness
  2. Consistency
  3. Sufficiency
  4. Efficiency

As a matter of fact, investigation is always about coming up with an idea regarding the population parameters based on the sample observations. The best part would be to formulate an unbiased, consistent estimator, which is also efficient. Normally, a sample mean for a set of sample observations is considered to be a very desirable estimator to form ideas about population parameters.

In detail, let’s examine the relevance of each of the properties of an estimator:

Unbiasedness of an estimator

Take a look at the below examples to understand the very idea of unbiasedness.

Example 1:

Answer:-

According to the problem, we have

Adding (1) & (2), we get,

So, from (3), we get:-

 is called an unbiased estimators for .

Now, subtracting (2) & (1), we get –

Example 2:

Assume that an investigator draws a sample from this population using SRSWR. Then show that the sample mean is an unbiased estimator for the population mean.

Now, by specification we have:-

We are redefined to show that:-

L.H.S  :

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DexLab Analytics Presents #BigDataIngestion

 

Data sampling is the key to business analytics and data science. On that note, DexLab Analytics offers state of the art Data Science Certification for all data enthusiasts. Recently, they have organized a new admission drive #BigDataIngestion offering exclusive 10% off on in-demand courses, including big data, machine learning and data science courses.

 

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An ABC Guide to Sampling Theory

An ABC Guide to Sampling Theory

Sampling theory is a study involving collection, analysis and interpretation of data accumulated from random samples of a population. It’s a separate branch of statistics that observes the relationship existing between a population and samples drawn from the population.

In simple terms, sampling means the procedure of drawing a sample out of a population. It aids us to draw a conclusion about the characteristics of the population after carefully studying only the objects present in the sample.

Here we’ve whisked out a few sampling-related terms and their definitions that would help you understand the nuanced notion of sampling better. Let’s have a look:

Sample – It’s the finite representative subset of a population. It’s chosen from a population with an aim to scrutiny its properties and principles.

Population – When a statistical investigation focuses on the study of numerous characteristics involving items on individuals associated with a particular group, this group under study is known as the population or the universe. A group containing a finite number of objects is known as finite population, while a group with infinite or large number of objects is called infinite population.

Population parameter – It’s an obscure numerical factor of the population. It’s no brainer that the primary objective of a survey is to find the values of different measures of population distribution; and the parameters are nothing but a functional variant inclusive of all population units.

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Estimator – Calculated based on sample values, an estimator is a functional measure.

Sampling fluctuation of an estimator – When you draw a particular sample from a given population, it contains different set of population members. As a result, the value of the estimator varies from one sample to another. This difference in values of the estimator is known as the sampling fluctuations of an estimator.

Next, we would like to discuss about the types of sampling:

There are mainly two types of random sampling, and they are as follows:

Simple Random Sampling with Replacement

In the first case, the ‘n’ units of the sample are drawn from the population in such a way that at each drawing, each of the ‘n’ numbers of the population gets the same probability 1⁄N of being selected. Hence, this methods is called the simple random sampling with replacement, clearly, the same unit of population may occur more than once inj a simple. Hence, there are N^n samples, regard being to the orders in which ‘n’ sample unit occur and each such sample has the probability 1/N^n .

Simple Random Sampling Without Replacement

In the second case each of the ‘n’ members of the sample are drawn one by one but the members once drawn are not returned back to the population and at each stage remaining amount of the population is given the same probability of being includes in the sample. This method of drawing the sample is called SRSWOR therefore under SRSWOR at any r^th number of draw there remains (N-r+1) units. And each unit has the probability of 1/((N-r+1) ) of being drawn.

Remember, if we take ‘n’ individuals at once from a given population giving equal probability to each of the observations, then the total number of possible example in (_n^N)C i.e.., combination of ‘n’ members out of ‘N’ numbers of the population will from the total no. of possible sample in SRSWOR.

The world of statistics is huge and intensively challenging. And so is sampling theory.

But, fret now. Our data science courses in Noida will help you understand the nuances of this branch of statistics. For more, visit our official site.

P.S: This is our first blog of the series ‘sampling theory’. The rest will follow soon. Stay tuned.

 

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