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

Posted on July 11, 2018July 11, 2018 by Dexlab

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 21^st 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.

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

Data Aspirants, Consider These 4 Career Options & Jazz-up Number Games!

Posted on July 9, 2018July 9, 2018 by Dexlab

Data Aspirants, Consider These 4 Career Options & Jazz-up Number Games!

Is crunching numbers your favorite hobby?

Are you interested in deciphering how many people use smartphones, regularly?

Do you feel fascinated by the way businesses use data to frame decisions?

If yes, then you are at the right place – a career, where you could leverage this inquisitiveness and knack for numbers is just carved for you. Not necessarily it has to be data science career option, but we’ve charted down top 5 career choices for the data curious you!

Data Scientist

Tagged as the sexiest job of 21^st century, data scientist jobs are irresistible. First of all, the field of data science is expanding steadfastly – IBM prediction says the demand for data scientists will increase by 28% by the end of 2020. This brings good news for job seekers, who are on toes to enter the fascinating world of data science, where the salaries are pumping up – already they have touched six figures.

The main objective of data scientists is to collect meaningful data to help businesses formulate strategic decisions. Cleaning up and structuring the data is of primary importance – followed by cutting edge tool implementation, such as algorithms, statistical models and deep learning structures – all of them aids in extracting insights out of relevant data.

Statistician

Other than data geeks, very few love the very idea of becoming a statistician. But for guys who love churning data, the role of statistician is the most fascinating in the world. They help solve the toughest problem with data, while finding and providing answers to crucial questions.

Statisticians’ aptitude for numbers knows no bounds – and the range of projects on which they work is diverse. From ascertaining unemployment rates to nabbing the discerning the effectiveness of prescription drugs to calculating the number of endangered animals living in a given area – from designing the strategies for data collection to nabbing the latest trends, statisticians need to juggle between a lot of tasks, and solve crucial problems.

Computer Scientist

The computers are lifeline of today’s businesses – so jobs related to computing power is selling like hot cakes. The field of computer science is encompassing – nerds in love with data can discover a treasure trove of career options under this umbrella term. If you are a true blue crime buff, choose computer forensics as your leading career option. Or else, are you a major computer game aficionado? Then aspire to become a game developer or architect.

Today, software developers and architects are witnessing surging demand, and most of the jobs in this technology domain help draw salaries over $100000 annually. So, what you waiting for?!

Database Administrator

Data is next to oil; of late, it’s been treated as a valuable resource. Thus, we should look for ways to keep it safe and well-protected. Database administrators are ideal for this defensive job. They not only toil to set up fortified databases but also are responsible for maintenance, model up-keeping and implementing security measures. Undeniably, it’s one of the most challenging jobs in the world of data but at the same time, it’s also the most rewarding one – at present, it ranks as the world’s #7 best technology job, according to a notable US tabloid.

Done reading? Now, data-lovers, when are you taking the next step to turn your avocation into your vocation? Pretty soon, right!

Quick Note: DexLab Analytics is offering state of the art Data Science Courses at affordable prices. For more details on Data Science Certification, visit the official page today.

The blog has been sourced from – dataconomy.com/2018/06/five-careers-to-consider-for-data-enthusiasts

How Data Science Is Getting Better, Day by Day?

Posted on July 7, 2018May 23, 2020 by Dexlab

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.

Get excellent data science certification from DexLab Analytics.

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.

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

Estimator Procedure under Simple Random Sampling: EXPLAINED

Posted on June 26, 2018May 23, 2020 by Dexlab

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:

Unbiasedness
Consistency
Sufficiency
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 :

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.

Enjoy 10% Discount, As DexLab Analytics Launches #BigDataIngestion

Posted on June 12, 2018June 12, 2018 by Dexlab

This summer, DexLab Analytics, a pioneering analytics training institute in Delhi is back in action with a whole new admission drive for prospective students: #BigDataIngestion with exclusive discount deals on offer. With an aim to promote an intensive data culture, we have launched Summer Industrial Training on Big Data Hadoop/Data Science. An exclusive 10% discount is on offer for all interested candidates. And, the main focus of the admission drive is on Hadoop, Data Science, Machine Learning and Business Analytics certification.

Data analytics is deemed to be the sexiest job of the 21^st century; it’s comes as no surprise that young aspirants are more than eager to grasp the in-demand skills. Especially for them and others, DexLab Analytics emerges as a saving grace. Our state of the art certification training is completely in sync with the vision of providing top-of-the-line quality analytics coaching through fine approaches and student-friendly curriculum.

That being said, #BigDataIngestion is one of its kinds; while Hadoop and Data Science modules are targeted towards B. Tech and B.E students, Data Science and Business Analytics modules are exclusively oriented for Eco, Statistics and Mathematics students. The comprehensive certification courses help students embark on a wishful journey across various big data domains and architectures, triggering high-end IT jobs, but to avail the high-flying discount offer, the students need to present a valid ID card, while enrolling for the courses.

We are glad to announce that already the institute has gathered a good reputation through its cutting edge, open-to-all demo sessions. The demo sessions has helped countless prospective students in understanding the quality of courses and the way they are being imparted. Now, the new offer announced by the team is like an icing on the cake – 10% discount on in-demand big data courses sounds too alluring! And the admission procedure is also as easy as pie; you can either drop by the institute in person, or else can opt for online registration.

In this context, the spokesperson of DexLab Analytics stated, “We are glad to play an active role in the process of development and condoning of data analytics skills amongst the data-friendly students’ community of the country. We go beyond traditional classroom training and provide hands-on industrial training that will enable you to approach your career with confidence”. He further added, “We’ve always been more than overwhelmed to contribute towards the betterment of skilled human resources of the nation, and #BigDataIngestion is no different. It’s a summer industrial training program to equip students with formidable data skills for a brighter future ahead.”

For more information or to register online, click here: DexLab Analytics Presents #BigDataIngestion

#BigDataIngestion: DexLab Analytics Offers Exclusive 10% Discount for Students This Summer

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An ABC Guide to Sampling Theory

Posted on June 7, 2018June 7, 2018 by Dexlab

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.

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.

How Blockchain Technology is Transforming these Four Popular Industries

Posted on May 14, 2018 by Dexlab

Blockchain technology is the next big thing. It is defying industry norms and altering the manner in which industries implement new projects. The decentralized nature of blockchain technology is the key to its success. Blockchain is transforming every organization through its secure and decentralized protocols, protected peer-to peer applications, and a new approach towards distributed management.

Here are some everyday industries that blockchain technology is revamping.

Finance:

There are all kinds of opinions regarding how cryptocurrency is impacting macroeconomics pertaining to the financial sector. The rapidly increasing demand for Bitcoin signals a flourishing future for cryptocurrency. In 2017, ICOs (Initial Coin Offerings), which are means of crowd funding centered on cryptocurrency, raised more money than venture capital investments. Cryptocurrencies, like Bitcoin, Ethereum and Ripple are improving their speed for processing transaction fees, and will be able to contend with speed of transaction for credit card companies in the near future. Bitcoin permits people to transfer money across borders instantaneously and at low costs. Many banks, such as Barclays, are set to use blockchain technology to facilitate speedier business procedures.

Cloud Computing:

The evolution of cloud has outmoded hard drives, which was the popular choice for transferring files from one computer to another, even a few years ago. Blockchain-based companies, like Akash, want to seize this opportunity and create an open market place where cloud computing costs are determined by demand and supply, instead of centralized, fixed prices. Most large-scale data centers depend on idle computing power. Akash Network makes idle server capacity available for cloud deployments. This system enables users to ‘’rent’’ idle computing power and providers to generate revenue from their idle power. Developers specify their deployment conditions in a file that is posted on the Akash blockchain. Providers capable of fulfilling these conditions bid on it. Low bid wins; after this parties go off chain to allocate workload in Docker containers. Akash tokens are then transferred from tenant‘s wallet to provider’s wallet.

Online Gaming:

The online sports industry is embracing the blockchain technology. An increasing number of developers in the world of e-Sports are employing blockchain technology and cryptocurrencies. Leading fantasy sport companies, like MyDFS, permit their users to create virtual arrays of real players and obtain winnings through tokens. In-app purchase is the newest monetization model for Smartphone app games. Blockchain technology is also advantageous for e-Sports betting platforms. The tech constructs a secure environment for low fee betting that is free from the control of a central party.

Decentralized Governance:

One of the most famed features of blockchain is decentralization. The thought of decentralized, autonomous organizations is no doubt very fascinating, but they are very difficult to establish. A hierarchical structure, where one person or group tends to dominate, is very natural. However, new and advanced frameworks are facilitating decentralized platforms to function effectively. An example of such a framework is DAOstack, which is striving to build a platform that enables collectives to self-organize around similar goals and interests. It is a platform that authorizes emerging organizations to select suitable governance model that will work for them and execute the same through DAOstack’s technological protocol. DAOstack’s founding principle is collaboration- it aims to provide a setting where goals of individuals can work in harmony with goals of a group.

The ‘’blockchain boom’’ is driving breakthroughs for a range of industries. This is just the beginning, though. As this tech evolves, it will enable rapid progress across every industry.

To read more blogs on emerging technologies, follow DexLab Analytics; it is a premier institute providing data science certification courses in Delhi. Do take a look their data analytics certification courses.

Evolving Logistics Scenario: The Tech-driven Future of Logistics Industry

Posted on April 27, 2018April 5, 2019 by Dexlab

Customer expectations are growing by the day; they are demanding faster and more flexible deliveries at minimum delivery costs. Businesses are being pressurized to customize their manufacturing processes as per customer demands. This is a hard slog for the logistics industry, which has to keep delivering better services but for lower prices.

The logistics industry can only achieve this through ‘digital fitness’. It has to make intelligent use of the global wave of digitization, including data analytics, automation and ‘Physical Internet’. The Physical Internet is an open global logistics system that is transforming the way physical objects are handled, moved, stored and supplied. It aims towards the replacement of current logistical models and making global logistics more efficient and sustainable. The Physical Internet promises better standardization in logistics operations, including shipment sizes, labeling and systems.

The central theme in logistics sector is collaborative working, which enables market leaders to retain dominance.

Now, let us take a look at a few tech-driven domains that will shape the future of logistics.

The future of Logistics Lies in IoT

Internet of Things has been the most innovative technology of the present era. It has the potential to revolutionize the logistics sector. The key benefits of IoT with regard to logistics are:

Real-time alerts and notifications
Automate processes that gather data from various machines
Automate vital operations like inventory management and asset tracking: With the help of IoT, companies can improve tasks like tracking orders, determining what items need to be stocked up and how certain products are performing.
Able to function without any human interventions.
Logistic companies can provide safer deliveries
Enable the regulation of temperature and other environmental factors.

IoT will be advantageous for the entire logistics sector, including fleet and warehouse management, and shipment and delivery of products. IoT can help companies dealing with cargo shipments by improving visibility in the delivery and tracking of cargo.

Warehouse Automation

Warehouse automation is set for a major overhaul. Online shopping is thriving and logistics, especially warehouse operations, need to be more refined and speedy. Warehouse operations of many e-commerce giants are undergoing a robotics makeover. According to reports, the market for logistics robotics, which had generated revenues worth 1.9 billion USD in 2016, is likely to generate sky-high revenues worth 22.4 billion USD this year.

The advancements in robotics include programming robots to pick and pack goods, load and unload cargo and at times deliver goods too. Employing robots speed up the processes of data collection, maintaining records and managing inventories. Most importantly, robots leave no room for human errors in the processes.

Blockchain Technology in Logistics

The growth of crypto-currencies like Bitcoin has popularized blockchain technology. Blockchain being a type of distributed ledger technology provides secure, traceable and transparent transactions. Blockchain technology employed by logistics firms will improve customer visibility into shipments and help prevent data breaches.

In the present times, logistics is considered the backbone of a stable economy. Thus, for India to emerge as a superpower, the logistics market needs to be developed and integrated with state-of-the-art technologies. Conducive policies and a healthy partnership between private and public sector is crucial to steer India into an era of competent and cost-effective business operations.

In times to come, automation will transform every industry. Don’t be left behind. Get an edge by enrolling for the data science and machine learning certification course at the premier data analyst training institute in Delhi– Dexlab Analytics.

AI is enhancing careers: How can you gain advantage in this AI-era?

Posted on April 21, 2018April 21, 2018 by Dexlab

Artificial intelligence has a significant impact on our lives. Several AI powered automation tools are already in use such as customer service applications and voice-powered assistants, like Apple’s Siri and Amazon’s Alexa. Adoption of AI will benefit the business by improving the quality and consistency of work. Based on a discussion between Forbes Agency council members, we have listed the ways in which artificial intelligence can help workers improve their career.

More valuable insights

AI will bring positive changes in the job of PR professionals. AI technology will take over manual jobs such as news monitoring, researching, reporting and making media lists. AI based predictive analytics will help PR professionals make better market predictions. They will reduce manual workload and help in strategic and creative thinking.

Replace mundane tasks

AI, automation and machine learning will replace daily low-quality cognitive tasks such as scheduling calendar invites, daily food ordering, determining whether to answer/review/delete emails based on facts. They will eventually aid in quality tasks such as identifying connections, analyzing correlation and drawing inferences.

Act as concierge

Popularity of Alexa, Watson, and Einstein suggest that consumers will expect tech to provide concierge services in the future. As AI techs evolve post their purchase, it will anticipate an individual’s daily tasks and provide highly personal recommendations.

Make marketing smarter

AI will enable companies develop stronger relationships with their customers. IBM’s Watson and other cognitive technology will help analyze unstructured text, audio, images and video. AI’s ability to perceive and process personality, tone and feelings will help deliver better personal recommendations. It will help companies carry out conversations using chatbots.

Automate customer support

The availability of chatbots round the clock will save a lot of time. They answer customer questions, give recommendations and guide customers to the next step. They will reduce the workload of customer support systems. Bots can draw insights on the needs, engagements and emotions of customers.

Unleash the full potential of your mind

Workers will be spared from carrying out mundane tasks. They will have the time to focus on productive tasks, which require problem-solving skills and creativity.

On-the–fly video editing

AI will eventually edit videos in real time. Real- time user engagement will perform multiple instantaneous tasks such as changing sound effects on the fly.

Create jobs and assimilate workflow

AI will interfere with regular workflow but in return it will create new jobs. It will help integrate the workforce. Humans will be instrumental in helping the AI work in harmony with the employees.

Improve future strategies

Humans will always be a part of the PR industry, as they are crucial in maintaining a healthy customer relationship. The data that is collected through AI will enable making more informed decisions for the future. AI will help companies stay abreast of information related to their competitors through better media monitoring.

Shrink 40 hours of analysis to 4 minutes

Manual analysis is very time consuming. The future of marketing efficiency lies in automation tools that will drastically reduce the time taken to analyze data and form strategies.

Productivity even during commute

AI has made automated driving a reality. Driving in autopilot mode greatly reduces driver fatigue and can affect productivity during commute, especially to and from work.

Improve brand engagement

AI can help devise customized experiences in real time. It interprets customer interactions and instantly creates customized content.

Make routine processes easier

Entrepreneurs describe AI as the ultimate efficiency driver. The day to day tasks can be entrusted to digital hands, which enable human hands to be more productive. AI driven technology is benefitting manufacturing processes as well as advertising platforms.

Give edge in competition

Businesses using AI will have a competitive edge over their clients. This is because AI implementation replaces manual processes of sorting complex data, drawing key insights and chalking out an action plan. AI improves decision-making, ROI, operational competence and cost savings.

AI related employment opportunities are on the rise. Compared to the demand, there is a lack in the number of professionals proficient in AI. It is predicted that by 2020, 20 percent of companies will need their workers to monitor and direct neural networks. About 2 million jobs in the cyber security sector are about to go vacant in the coming years.

So it is absolutely imperative to future-proof your career for the imminent AI era. Broaden your skill set and increase your proficiency by taking professional training in Machine Learning, Business Analytics and Data Science. Get an edge in your career by joining the Data science and machine learning certification course offered by Dexlab Analytics- a premier institute offering multiple courses on data science.

Generality

Specificity

Performance

Parting Thoughts

The blog has been sourced from –

Interested in a career in Data Analyst?

Data Scientist

Statistician

Computer Scientist

Database Administrator

Interested in a career in Data Analyst?

Interested in a career in Data Analyst?

The Estimator Procedure under Simple Random Sampling

Unbiasedness of an estimator

Interested in a career in Data Analyst?

Interested in a career in Data Analyst?

Simple Random Sampling with Replacement

Simple Random Sampling Without Replacement

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Finance:

Cloud Computing:

Online Gaming:

Decentralized Governance:

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The future of Logistics Lies in IoT

Warehouse Automation

Blockchain Technology in Logistics

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