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How to Effectively Pursue your Dream Career in Market Analysis? A Go To Guide!

How to Effectively Pursue your Dream Career in Market Analysis? A Go To Guide!

Being a Market Analyst in these days is synonymous to sealing the deal, where you don’t have to worry about your salary and many other perks which varies from company to company. But, it’s not an easy-peasy way to success in here.

A Market Analyst is heaped with a huge amount of responsibilities day in and day out, involving a colossal amount of data and analysing them to perfection. With the help of these data, they have to come up with innovative ideas to boost the business of the company.

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Some of the other responsibilities include:

  • Exploring and analysing the tactics of the competitors, market conditions and the consumer demographics flawlessly.
  • Studying the customer opinions, buying habits, customers’ wants and needs.
  • Converting the collection of data into interactive presentations, tables and texts to bring them to the effective use for the benefit of the company.
  • Coming up with improved approach to collect data, comprising of surveys, interviews, questionnaires and more.

These are a few of the highlights of the jobs assigned to a Market Analyst. You can catch some more of them at A Market Analyst and His Job: An Overview!

Want to Become a Market Analyst? Here’s What You Should Go For!

Now, if you are a newbie and interested to pursue a career in Market Analysis, you shall always opt for the best customer marketing analysis training in the country, besides having:

  1. A Degree in Statistics, Computer Science, Economics or Business Administration – When it comes to Market Analysis, all they want is to interview the candidates with a Bachelor’s Degree in maths, statistics, computer science, business administration or economics. Some of the companies also shortlist the candidates from the field of communications. Furthermore, specialist degrees in marketing research and consumer psychology prove exceptional.
  2. Manoeuvre your Technical and Business Skills towards Analytic ThinkingAnalytic thinking is the thing that would take you for miles on end, regardless of your skill set. Besides, the skills which you should be needing are:

Deep Learning and AI using Python

Technical Skills:

  • Proficient with Statistical Analysis software, like, R, SAS and SPSS.
  • Well-versed in SQL databases and database querying languages.
  • Good programming skills.
  • Known to business intelligence and reporting software.
  • Customer Market Analysis courses.

At the end, we will provide you some business skills that you can develop to pursue your dreams smoothly:

  • Analytic problem solving skills.
  • Developing a habit of critical thinking.
  • Effective communication skills.
  • An impressive knowledge of the industry.

Grab an insight of this article in order to get a grasp of the stream of Market Analysis and how to become a Market Analyst! For more such informative blogs related to computer science and the evolving technologies of Python, Data Science, Big Data and AI, visit Dexlab Analytics. You can also follow us on Facebook and LinkedIn for any updates and queries about our courses and the teaching staffs.

 

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Take a Deep Look on How Machine Learning Boosts Business Growth!

Take a Deep Look on How Machine Learning Boosts Business Growth!

Machine Learning is the technology of the future and the rise of it is, well, shocking! Numerous businesses have already started adopting Machine Learning into their business strategy which is ultimately culminating towards their growth. You can also get the most of Machine Learning by going for the best Machine Learning course in India without wasting hours on the internet.

This new and improving technology is showing marked results in making a particular business more efficient, enhancing customer relationships and driving more sales than ever. You can get right on to Machine Learning Significantly Aids in Improving the Business Performance: Learn the Hows and learn about Machine Learning and its rising curve.

Here we have decided to discuss in details about the ways how Machine Learning is helping business touch great heights:

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Natural Language

One of the major setbacks in the industry of computer science was the inability of computers to comprehend our natural language or the way we speak in our everyday life. This is slowly changing with the rapid growth and considerable research and development on Machine Learning. 

It looks like we have come a long way from the crude search terms that we used to generate the results that we wanted. The AI-driven programs of now, with the help of Machine Learning, can figure out the essence of our conversations and also capitalizing largely on the nuances of our language. Most importantly, they learn from past experiences, which is highly progressive.

Logistics

The retail industry and that of logistics are largely relying on Python for Data Analysis and this in turn, is making them future-proof.

Retail giants like Amazon are encouraging the use of Machine Learning to sharpen the efficiency of their company with new features and technology like “anticipatory shopping” protocol. Retail analytics using Python is becoming formidable.

Even in the field of logistics, the inclusion of Machine Learning is proving a boon!

Manufacturing Industry

Innumerable manufacturing companies are adopting the budding technology of Machine Learning and utilizing it in almost every stage of production, simply because the AI-driven technology reduces unnecessary expenses. 

Companies like Seebo, are taking up Python seriously to build accurate data analytics software. Moreover, machine learning is estimated to cut down on the delivery times by 30% and surprisingly save fuel by 12%. According to the reports, the programs fed on AI would even reduce the maintenance costs by 20 – 30%.

Deep Learning and AI using Python

Consumer Data

We have already seen a world of data collection which has been on a rise for years. Now, finally, with the rise of machine learning, the companies are looking forward to making some use of all these data that they have accumulated. In the coming years, we will see AI improving powered by Machine Learning to make the world productive and smart all the more.

You can take a look at A DISCUSSION ABOUT ARTIFICIAL INTELLIGENCE: KNOWING AI CLOSELY if you are interested in AI. Stay glued to our website for more updates and information from the world of technology!

 

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How to Start a Successful Data Science Career?

How to Start a Successful Data Science Career?

The most common question we come across in DexLab Analytics HQ is how to take a step into the world of analytics and data science. Of course, grabbing a data science job isn’t easy, especially when there is so much hype going around. This is why we have put together top 5 ways to bag the hottest job in town. Follow these points and swerve towards your dream career.

Deep Learning and AI using Python

Enhance Your Skills

At present, LinkedIn in the US alone have 24,697 vacant data scientist positions. Python, SQL and R are the most common skills in demand followed by Tensorflow, Jupyter Notebooks and AWS. Gaining statistical literacy is the best way to grab these hot positions but for that, you need hands-on training from an expert institute.

If interested, you can check out analytics courses in Delhi NCR delivered by DexLab Analytics. They can help you stay ahead of the curve.

Create an Interesting Portfolio

A portfolio filled with machine learning projects is the best bet. Companies look for candidates who have prior work experience or are involved in data science projects. Your portfolio is the potential proof that you are capable enough to be hired. Thus, make it as attractive as possible.

Include projects that qualify you to be a successful data scientist. We would recommend including a programming language of your choice, your data visualization skill and your ability to employ SQL.

Get Yourself a Website

Want to standout from the rest? Build up your website, create a strong online presence and continuously add and update your Kaggle and GitHub profile to exhibit your skills and command over the language. Profile showcasing is of utmost importance to get recognized by the recruiters. A strong online presence will not only help you fetch the best jobs but also garner the attention of the leads of various freelance projects.

Be Confident and Apply for Jobs You Are Interested In

It doesn’t matter if you possess the skills or meet the job requirements mentioned on the post, don’t stop applying for the jobs that interest you. You might not know every skill given on a job description. Follow a general rule, if you qualify even half of the skills, you should apply.

However, while job hunting, make sure you contact recruiters, well-versed in data science and boost your networking skills. We would recommend you visit career fairs, approach family, friends or colleagues and scroll through company websites. These are the best ways to look for data science jobs. 

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Improve Your Communication Skills

One of the key skills of data scientists is to communicate insights to different users and stakeholders. Since data science projects run across numerous teams and insights are often shared across a large domain, hence superior communication skill is an absolute must-have.

Want more information on how to become a data scientist? Follow DexLab Analytics. We are a leading data analyst training institute in Delhi offering in-demand skill training courses at affordable prices.

 

The blog has been sourced fromwww.forbes.com/sites/louiscolumbus/2019/04/14/how-to-get-your-data-scientist-career-started/#67fdbc0e7e5c

 

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A Regression Line Is the Best Fit for the Given PRF If the Parameters Are OLS Estimations – Elucidate

A Regression Line Is the Best Fit for the Given PRF If the Parameters Are OLS Estimations - Elucidate

Regression analysis is extensively used in business applications. It’s one of the most integral statistical techniques that help in estimating the direction and strength between two or more (financial) variables – thus determining a company’s sales and profits over the past few years.

In this blog, we have explained how a regression line is the best fit for a given PRF if the parameters are all OLS estimations.

The OLS estimators for a given regression line has been obtained as: a = y ̅ – bx ̅ and b = (Cov(x,y))/(v(x)). The regression line on the basis of these OLS estimate has been given as: Y ̂_ i-Y ̅ = b(x_i-x ̅ )….. (1)

The regression line (1) constructed above is a function of the least square i.e. the parameters of the regression equation have been selected so that the residual sum of squares is minimized. Thus, the estimators ‘a’ & ‘b’ explains the population parameters, the best relative to any other parameters. Given, the linearity of the parameters, these estimators share the minimum variations with the population parameters, i.e. they explain the maximum variations in the model, in relation to the population parameters, as compared to any other estimators, in a class of unbiased estimators.

Thus, the regression line would be the ‘best fit’ for a given PRF. If ‘a’ & ‘b’ are best linear unbiased estimators for  respectively. Thus, to show ‘best fit’, we need to prove:

  1. To ‘b’ is Best unbiased estimator for :-

From the OLS estimation; we have ‘b’ as:

i.e.b is a linear combination of w’is & y’is.

Hence; ‘b’ is a linear estimator for β. Therefore, the regression line would be linear in parameters as far as ‘b’ is concerned.

Now,

Let us test for the prevalence of this conditions:

For unbiasedness, we must have :- E(b)=β. To test this, we take expectation on both sides of (3) & get:

From (1) & (4) we can say that ‘b’ is a linear unbiased estimator for β.

To check whether ‘b’ is the best estimator or not we need to check whether it has the minimum variance in a class of linear unbiased estimator.

Thus, we need to calculate the variance for ‘b’ & show that it is the minimum in a class of unbiased estimations. But, first, we need to calculate the variance for ‘b’.

Now; we need to construct another linear unbiased estimator and find its variance.

Let another estimator be: b^*=∑ci yi….(6)  For unbiasedness ∑ci =0,∑cixi =1.

Now; from (6) we get:

∴b* is an unbiased estimator for  Now; the variance for  can be calculated as:-

Now;

Hence; from (9) we can say V(b) is the least among a class of unbiasedness estimators.

Therefore, ‘b’ is the best linear unbiased estimator for  in a class of linear unbiased estimators.

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  1. To prove ‘a’ is the best linear unbiased estimator for α:-

Form the OLS estimation we have ‘a’ as:-

Here; ‘b’ is a function of Y and Y is a linear function of X(orUi).

‘a’ is also a linear function of Y. i.e. has linearity.

There, ‘a’ is a linear estimator for   ……. (11)

Now, for ‘a’ to be an unbiased estimator; we must have From (10) we have:-

Taking expectations on both sides of the equation; we get:

Therefore, ‘a’ is an unbiased estimator for  ……… (12)

From (11) & (12) ‘a’ is a linear unbiased estimator for

Now, if ‘a’ is to be the best estimator for then is most have the minimum variance. Thus; we first need to calculate the variance of ‘a’.

Now, 

Now; let us consider an estimator in the class of linear unbiased estimator.

Further we know,

Now;

Hence;

Now;

Therefore;

Hence; from (16) we can say that is the Min Variance Unbiased estimator in a class of unbiased estimator.

Hence; we can now safely conclude that a regression line composed of OLS estimators is the ‘best fit’ line for a given PRF, compared to any other estimator.

Thus, the best-fit regression line can be depicted as

Thus, a regression line is the best fit for a given PRF if the estimators are OLS.

End Notes

The beauty and efficiency of Regression method of forecasting never fail to amaze us. The way it crunches the data to help make better decisions and improve the current position of the business is incredible. If you are interested in the same, follow us at DexLab Analytics. A continues blog series on regression model and analysis is upcoming. Watch this space for more.

DexLab Analytics offers premium data science courses in Gurgaon crafted by the experts. After thorough research, each course is prepared keeping student’s needs and industry demands in mind. You can check out our course offerings here.

 

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Bayesian Thinking & Its Underlying Principles

Bayesian Thinking & Its Underlying Principles

In the previous blog on Bayes’ Theorem, we left off at an interesting junction where we just touched upon the ideas on prior odds ratio, likelihood ratio and the resulting Posterior Odds Ratio. However, we didn’t go into much detail of what it means in real life scenarios and how should we use them.

In this blog, we will introduce the powerful concept of “Bayesian Thinking” and explain why it is so important. Bayesian Thinking is a practical application of the Bayes’ Theorem which can be used as a powerful decision-making tool too!

We’ll consider an example to understand how Bayesian Thinking is used to make sound decisions.

For the sake of simplicity, let’s imagine a management consultation firm hires only two types of employees. Let’s say, IT professionals and business consultants. You come across an employee of this firm, let’s call him Raj. You notice something about Raj instantly. Raj is shy. Now if you were asked to guess which type of employee Raj is what would be your guess?

If your guess is that Raj is an IT guy based on shyness as an attribute, then you have already fallen for one of the inherent cognitive biases. We’ll talk more about it later. But what if it can be proved Raj is actually twice as likely to be a Business Consultant?!

This is where Bayesian Thinking allows us to keep account of priors and likelihood information to predict a posterior probability.

The inherent cognitive bias you fell for is actually called – Base Rate Neglect. Base Rate Neglect occurs when we do not take into account the underlying proportion of a group in the population. Put it simply, what is the proportion of IT professionals to Business consultants in a business management firm? It would be fair to assume for every 1 IT professional, the firm hires 10 business consultants.

Another assumption could be made about shyness as an attribute. It would be fair to assume shyness is more common in IT professionals as compared to business consultants. Let’s assume, 75% of IT professionals are in fact shy corresponding to about 15% of business consultants.

Think of the proportion of employees in the firm as the prior odds. Now, think of the shyness as an attribute as the Likelihood. The figure below demonstrates when we take a product of the two, we get posterior odds.

Plugging in the values shows us that Raj is actually twice as likely to be a Business consultant. This proves to us that by applying Bayesian Thinking we can eliminate bias and make a sound judgment.

Now, it would be unrealistic for you to try drawing a diagram or quantifying assumptions in most of the cases. So, how do we learn to apply Bayesian Thinking without quantifying our assumptions? Turns out we could, if we understood what are the underlying principles of Bayesian Thinking are.

Principles of Bayesian Thinking

Rule 1 – Remember your priors!

As we saw earlier how easy it is to fall for the base rate neglect trap. The underlying proportion in the population is often times neglected and we as human beings have a tendency to just focus on just the attribute. Think of priors as the underlying or the background knowledge which is essentially an additional bit of information in addition to the likelihood. A product of the priors together with likelihood determines the posterior odds/probability.

Rule 2 – Question your existing belief

This is somewhat tricky and counter-intuitive to grasp but question your priors. Present yourself with a hypothesis what if your priors were irrelevant or even wrong? How will that affect your posterior probability? Would the new posterior probability be any different than the existing one if your priors are irrelevant or even wrong?

Rule 3 – Update incrementally

We live in a dynamic world where evidence and attributes are constantly shifting. While it is okay to believe in well-tested priors and likelihoods in the present moment. However, always question does my priors & likelihood still hold true today? In other words, update your beliefs incrementally as new information or evidence surfaces. A good example of this would be the shifting sentiments of the financial markets. What holds true today, may not tomorrow? Hence, the priors and likelihoods must also be incrementally updated.

Conclusion

In conclusion, Bayesian Thinking is a powerful tool to hone your judgment skills. Developing Bayesian Thinking essentially tells us what to believe in and how much confident you are about that belief. It also allows us to shift our existing beliefs in light of new information or as the evidence unfolds. Hopefully, you now have a better understanding of Bayesian Thinking and why is it so important.

On that note, we would like to say DexLab Analytics is a premium data analytics training institute located in the heart of Delhi NCR. We provide intensive training on a plethora of data-centric subjects, including data science, Python and credit risk analytics. Stay tuned for more such interesting blogs and updates!

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

 

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The Almighty Central Limit Theorem

The Almighty Central Limit Theorem

The Central Limit Theorem (CLT) is perhaps one of the most important results in all of the statistics. In this blog, we will take a glance at why CLT is so special and how it works out in practice. Intuitive examples will be used to explain the underlying concepts of CLT.

First, let us take a look at why CLT is so significant. Firstly, CLT affords us the flexibility of not knowing the underlying distribution of any data set provided if the sample is large enough. Secondly, it enables us to make “Large sample inference” about the population parameters such as its mean and standard deviation.

The obvious question anybody would be asking themselves is why it is useful not to know the underlying distribution of a given data set?

To put it simply in real life, often times than not the population size of anything will be unknown. Population size here refers to the entire collection of something, like the exact number of cars in Gurgaon, NCR at any given day. It would be very cumbersome and expensive to get a true estimate of the population size. If the population size is unknown its underlying distribution will be known too and so will be its standard deviation. Here, CLT is used to approximate the underlying unknown distribution to a normal distribution. In a nutshell, we don’t have to worry about knowing the size of the population or its distribution. If the sample sizes are large enough, i.e. – we have a lot of observed data, it takes the shape of a symmetric bell-shaped curve. 

Now let’s talk about what we mean by “Large sample inference”. Imagine slicing up the data into ‘n’ number of samples as below:

Now, each of these samples will have a mean of their own.

Therefore, effectively the mean of each sample is a random variable which follows the below distribution:

Imagine, plotting each of the sample mean on a line plot, and as “n”, i.e. the number of samples goes to infinity or a large number the distribution takes a perfect bell-shaped curve, i.e – it tends to a normal distribution.

Large sample inferences could be drawn about the population from the above distribution of x̅. Say, if you’d like to know the probability that any given sample mean will not exceed quantity or limit.

The Central Limit Theorem has vast application in statistics which makes analyzing very large quantities easy through a large enough sample. Some of these we will meet in the subsequent blogs.

Try this for yourself: Imagine the average number of cars transiting from Gurgaon in any given week is normally distributed with the following parameter . A study was conducted which observed weekly car transition through Gurgaon for 4 weeks. What is the probability that in the 5th week number of cars transiting through Gurgaon will not exceed 113,000?

If you liked this blog, then do please leave a comment or suggestions below.

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

About the Institute: DexLab Analytics is a premier data analytics training institute headquartered in Gurgaon. The expert consultants working here craft the most industry-relevant courses for interested candidates. Our technology-driven classrooms enhance the learning experience.

 

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Upskill and Upgrade: The Mantra for Budding Data Scientists

Upskill and Upgrade: The Mantra for Budding Data Scientists

Have the right skills? Then the hottest jobs of the millennium might be waiting for you! The job profiles of data analysts, data scientists, data managers and statisticians harbour great potentials.

However, the biggest challenge in today’s age lies in preparing novice graduates for Industry 4.0 jobs. Although no one has yet cleared which roles will cease to exist and which new roles will be created, the consultants have started advising students to imbibe necessary skills and up-skill in domains that are likely to influence and carve the future jobs. Becoming adaptive is the best way to sail high in the looming technology-dominated future.

Data Science and Future

In this context, data science has proved to be one of the most promising fields of technology and science that exhibits a wide gap between demand and supply yet an absolute imperative across disciplines. “Today there is no shortage of data or computing abilities but there is a shortage of workforce equipped with the right skill set that can interpret data and get valuable insights,” revealed James Abdey, assistant professorial lecturer Statistics, London School of Economics and Political Science (LSE).

He further added that data science is a multidisciplinary field – drawing collectives from Economics, Mathematics, Finance, Statistics and more.

As a matter of fact, anyone, who has the right skill and expertise, can become a data scientist. The required skills are analytical thinking, problem-solving and decision-making aptitude. “As everything becomes data-driven, acquiring analytical and statistical skill sets will soon be imperative for all students, including those pursuing Social Sciences or Liberal Arts and also for professionals,” said Jitin Chadha, founder and director, Indian School of Business and Finance (ISBF).

DexLab Analytics is one of the most prominent deep learning training institutes seated in the heart of Delhi. We offer state-of-the-art in-demand skill training courses to all the interested candidates.

The Challenges Ahead

The dearth of expert training faculty and obsolete curriculum acts as major roadblocks to the success of data science training. Such hindrances cause difficulty in preparing graduates for Industry 4.0. In this regard, Chiraag Mehta from ISBF shared that by increasing international collaborations and intensifying industry-academia connect, they can formulate an effective solution and bring forth the best practices to the classrooms. “With international collaborations, higher education institutes can bring in the latest curriculum while a deeper industry-academia connect including, guest lecturers from industry players and internships will help students relate the theory to real-world applications, ” shared Mehta during an interview with Education Times.

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Industry 4.0: A Brief Overview

The concept Industry 4.0 encompasses the potential of a new industrial revolution – where gathering and analyzing data across machines will become the order of the day. The rise of this new digital industrial revolution is expected to facilitate faster, more flexible and efficient processes to manufacture high-quality products at reduced costs – thus, increasing productivity, switch economies, stimulate industrial growth and reform workforce profile.

Want to know more about data science courses in Gurgaon? Feel free to reach us at DexLab Analytics.

 

The blog has been sourced fromtimesofindia.indiatimes.com/home/education/news/learn-to-upskill-and-be-adaptive/articleshow/68989949.cms

 

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Bayes’ Theorem: A Brief Explanation

Bayes’ Theorem: A Brief Explanation

(This is in continuation of the previous blog, which was published on 22nd April, 2019 – www.dexlabanalytics.com/blog/a-beginners-guide-to-learning-data-science-fundamentals )

In this blog, we’ll try to get a hands-on understanding of the Bayes’ Theorem. While doing so, hopefully we’ll be able to grasp a basic understanding of concepts such as Prior odds ratio, Likelihood ratio and Posterior odds ratio.

Arguably, a lot of classification problems have their root in Bayes’ Theorem. Reverend T. Bayes came up with this superior logical function, which mathematically deducts the probability of an event occurring from a larger set by “flipping” the conditional probabilities.

 


 

Consider,  E1, E2, E3,……..En to be a partition a larger set “S” and now define an Event – A, such that A is a subset of S.

Let the square be the larger set “S” containing mutually exclusive events Ei’s. Now, let the yellow ring passing through all Ei’s be an event – A.

Using conditional probabilities, we know,

Also, the relationship:

Law of total probability states:

Rearranging the values of  &  gives us the Bayes Theorem:

The values of  are also known as prior probabilities, the event A is some event, which is known to have occurred and the conditional probability   is known as the posterior probability.

Now that, you’ve got the maths behind it, it’s time to visualise its practical application. Bayesian thinking is a method of applying Bayes’ Theorem into a practical scenario to make sound judgements.

The next blog will be dedicated to Bayesian Thinking and its principles.

For now, imagine, there have been news headlines about builders snooping around houses they work in. You’ve got a builder in to work on something in your house. There is room for all sorts of bias to influence you into believing that the builder in your house is also an opportunistic thief.

However, if you were to apply Bayesian thinking, you can deduce that only a small fraction of the population are builders and of that population, a very tiny proportion is opportunistic thieves. Therefore, the probability of the builder in your house being an opportunistic thief is actually a product of the two proportions, which is indeed very-very small.

Technically speaking, we call the resulting posterior odds ratio as a product of prior odds ratio and likelihood ratio. More on applying Bayesian Thinking coming up in the next blog.

In the meantime try this exercise and leave your comments below in the comments section.

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In the above example on “snooping builders”, what are your:

  • Ei’s
  • Event – A
  • “S”

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

About the Institute: DexLab Analytics is a premier data analyst training institute in Gurgaon specializing in an enriching array of in-demand skill training courses for interested candidates. Skilled industry consultants craft state-of-the-art big data courses and excellent placement assistance ensures job guarantee.

For more from the tech series, stay tuned!

 

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Study: The Demand for Data Scientists is Likely to Rise Sharply

Study: The Demand for Data Scientists is Likely to Rise Sharply

Data is like the new oil. A large number of companies are leveraging artificial intelligence and big data to mine these vast volumes of data in today’s time. Data science is a promising landmine of job opportunities – and it’s high time to consider it as a successful career avenue.

The prospect of data science is skyrocketing. Today, it is estimated that more than 50000 data science and machine learning jobs are lying vacant. Plus, nearly 40000 new jobs are to be generated in India alone by 2020. If you follow the global trends, the role of data scientist has expanded over 650% since 2012 yet only 35000 people in the US are skilled enough.

Data scientists are like the platform that connects the dots between programming and implementation of data to solve challenging business intricacies – says Pankaj Muthe, Academic Program Manager (APAC), Company Spokesperson, QlikTech. The company delivers intuitive platform solutions for embedded analytics, self-service data visualizations and guided analytics and reporting across the globe.

According to a pool of experts, data science is the hottest job trend of this century and is the second most popular degree to have at the master level next to MBA. No wonder, this new breed of science and technology is believed to be driving a new wave of innovation! Data scientists and front-end developers attracted the highest remuneration across Indian startups throughout 2017.

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Eligibility Criteria

To become a professional data scientist, a degree in computer science/engineering or mathematics is a must. Most of the data scientists have a knack for intricate tasks and aptitude to learn challenging programming languages. Any good organization seeks interested and intelligent candidates with the zeal to learn more. The subjects in which they need to be proficient are mathematics, statistics and programming. Moreover, data science jobs need a very sound base in machine learning algorithms, statistical modeling and neural networks as well as incredible communication skills.

Today, a lot of institutes offer state-of-the-art data science online courses that prove extremely beneficial for career growth and expansion. Combining theoretical knowledge and technical aspects of data science training, these institutes provide skill and assistance to develop real-world applications. DexLab Analytics is one such institute that is located in the heart of Delhi NCR. For more, feel free to reach us at <www.dexlabanalytics.com>

Future Prospects

After land, labour and capital, data ranks as the fourth factor of production. According to the US Department of Statistics, the demand for data engineers is likely to grow by 40% by 2020. If you are looking for a flourishing career option, this is the place to be: an entry-level engineer begins their career as a business analyst and then proceeds towards becoming a project manager. Later, after years of experience, these virgin business analysts further get promoted to become chief data officers.

 

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