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How Company Leaders and Data Scientists Work Together

How Company Leaders and Data Scientists Work Together

Business leaders across platforms are hungrily eyeing data-driven decision making for its ability to transform businesses. But what needs to be taken into account is the opinion of data scientists in the core company teams for they are the experts in the field and whatever they have to say regarding data driven decisions should be the final word in these matters.

“The ideal scenario is all parties in complete alignment. This can be envisioned as a perfect rectangle, with business leaders’ expectations at the top, fully supported by a foundation of data science capabilities — for example, when data science and AI can achieve management’s goal of reducing customer retention costs by automating identification and outreach to at-risk customers,”says a report.

The much sought after rectangle, however, is rarely achieved. “A more workable shape is the rhombus, depicting the push-and-pull of expectations and deliverables.”

Using the power of your company’s data.

Business leaders must have patience with developments on the part of data scientists for what they expect is usually not in sync with the deliverables on the ground.

“Over the last few years, an automaker, for example, dove into data science on leadership’s blind faith that analytics could revolutionize the driver experience. After much trial and error, the results fell far short of adding anything meaningful to what drivers found valuable behind the wheel of a car.”

Appreciate Small Improvements

Also, what must be appreciated are small improvements made impactful. For instance, “slight increases in profitability per customer or conversion rates” are things that should be taken into account despite the fact that they might be modest gains in comparison to what business leaders had invested in analytics. “Applied over a large population of customers, however, those small improvements can yield big results. Moreover, these improvements can lead to gains elsewhere, such as eliminating ineffective business initiatives.”

Healthy Competition

However, it is advisable for business leaders to constantly push their data scientists to strive for more deliverables and improve their tally with a framework of healthy competition in place. In fact, big companies form data science centers of excellence, “while also creating a healthy competitive atmosphere that encourages data scientists to push each other to find the best tools, strategies, and techniques for solving problems and implementing solutions.”

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Here are three ways to inspire data scientists

  1. Both sides must work togetherTake the example of a data science team with expertise in building models to improve customers’ shopping experiences. “Business leaders might assume that a natural next step is to use AI to enhance all customer service needs.”However, AI and machine learning cannot answer the ‘why’ or ‘how’ of the data insights. Human beings have to delve into those aspects by studying the AI output. And on the other hand, data scientists also must understand why business leaders expect so much from them and how to achieve a middle path with regard to expectations and deliverables.
  2. Gain from past successes and achievements – “There is value in small data projects to build capabilities and understanding and to help foster a data-driven culture.”The best policy for firms to follow is to initially keep modest expectations. After executing and implementing the analytics projects, they should conduct a brutally honest anatomy of the successes and failures, and then build business expectations at the same time as analytics investment.
  3. Let data scientists spell out the delivery of analytics results “Communication around what is reasonable and deliverable given current capabilities must come from the data scientists — not the frontline marketing person in an agency or the business unit leader.” Before signing any contract or deal with a client, it is advisable to allow the client to have a discussion with the data scientists so that there is no conflict of ideas between what the data science team spells out and what the marketing team has in mind. For this, data scientists will have to work on their soft skills and improve their ability to “speak business” regarding specific projects.


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Statistical Application in R & Python: EXPONENTIAL DISTRIBUTION

Statistical Application in R & Python: EXPONENTIAL DISTRIBUTIONStatistical Application in R & Python: EXPONENTIAL DISTRIBUTION

In this blog, we will explore the Exponential distribution. We will begin by questioning the “why” behind the exponential distribution instead of just looking at its PDF formula to calculate probabilities. If we can understand the “why” behind every distribution, we will have a head start in figuring out its practical uses in our everyday business situations.

Much could be said about the Exponential distribution. It is an important distribution used quite frequently in data science and analytics. Besides, it is also a continuous distribution with one parameter “λ” (Lambda). Lambda as a parameter in the case of the exponential distribution represents the “rate of something”. Essentially, the exponential distribution is used to model the decay rate of something or “waiting times”.

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For instance, you might be interested in predicting answers to the below-mentioned situations:

  • The amount of time until the customer finishes browsing and actually purchases something in your store (success).
  • The amount of time until the hardware on AWS EC2 fails (failure).
  • The amount of time you need to wait until the bus arrives (arrival).

In all of the above cases if we can estimate a robust value for the parameter lambda, then we can make the predictions using the probability density function for the distribution given below:

Application:-

Assume that a telemarketer spends on “average” roughly 5 minutes on a call. Imagine they are on a call right now. You are asked to find out the probability that this particular call will last for 3 minutes or less.

 

 

Below we have illustrated how to calculate this probability using Python and R.

Calculate Exponential Distribution in R:

In R we calculate exponential distribution and get the probability of mean call time of the tele-caller will be less than 3 minutes instead of 5 minutes for one call is 45.11%.This is to say that there is a fairly good chance for the call to end before it hits the 3 minute mark.

Calculate Exponential Distribution in Python:

We get the same result using Python.

Conclusion:

We use exponential distribution to predict the amount of waiting time until the next event (i.e., success, failure, arrival, etc).

Here we try to predict that the probability of the mean call time of the telemarketer will be less than 3 minutes instead of 5 minutes for one call, with the help of Exponential Distribution. Similarly, the exponential distribution is of particular relevance when faced with business problems that involve the continuous rate of decay of something. For instance, when attempting to model the rate with which the batteries will run out. 

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Hopefully, this blog has enabled you to gather a better understanding of the exponential distribution. For more such interesting blogs and useful insights into the technologies of the age, check out the best Analytics Training institute Gurgaon, with extensive Data Science Courses in Gurgaon and Data analyst course in Delhi NCR.

Lastly, let us know your opinions about this blog through your comments below and we will meet you with another blog in our series on data science blogs soon.

 

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Statistical Application in R & Python: Poisson Distribution

Statistical Application in R & Python: Poisson Distribution

Continuing with the series of blogs, the first of which was Statistical Application In R & Python: Normal Probability Distribution, here we bring you a post on how you can calculate Poisson distribution effortless using R & Python. So, stay tuned!

Poisson distribution is a counting process which is a discrete probabilistic model. It has only one parameter, (lambda or “m”) which is essentially the average rate of change. Poisson distribution is used to model “number of anything”. The probability distribution function of a Poisson distribution is given by the below expression.

If m is the mean occurrence per interval, then the probability of having x occurrence with in a given interval is:

Application:

A business firm receives on an average 6.5 telephone calls per day during the time period 11:00 – 11:15 A.M., Find the probability that on a certain day, the firm receives exactly9 calls during the same period.

The random variable x is the ‘number of telephone calls received during the period 11:00 – 11:15 A.M, since x is assumed to Poisson distribution. The parameter m is equal to the mean of the distribution; i.e.  m = 6.5 and x = 9, then the equation is:

Calculate Poisson Distribution in R:

So, while calculating Poisson distribution in R, we notice that the probability of occurring exactly 9 calls instead of average 6.5 calls in a given particular time (11:00 A.M – 11:15 A.M ) = 85.81%

Calculate Poisson Distribution in Python:

So, while we calculate Poisson distribution in Python, we notice that the probability of occurring exactly 9 calls instead of average 6.5 calls in a given particular time (11:00 A.M – 11:15 A.M) = 85.81%

Conclusion:

Companies can use the Poisson distribution to contrive effective steps to improve their operational efficiency. For instance, an analysis done with the Poisson distribution might reveal how a company can arrange staffing in order to be able to handle the peak periods efficiently, when the customer service calls keep on pouring.

In this problem we see that the business firm receives on an average 6.5 telephone calls per day during the time period 11:00A.M – 11:15A.M, then the probability of the firm receives exactly 9 calls in a same is 85.81%.

Dexlab Analytics is the best Python training institute in Delhi, bringing you the all-inclusive courses of Python for Data Analysis and R Predictive Modelling Certification, among others to start your career in Data Science and Analytics.

 

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Python vs. Scala: Which is Better for Data Analytics?

Python vs. Scala: Which is Better for Data Analytics?

Data Science and Analytics seem to be synonymous to progress as far as the field of computer science is concerned. Now, with the rise of these technologies, everything goes down to the programming languages, which single-handedly help in the growth of them. 

This gave rise to Python, now known as the most significant language in the world of technology. Scala is another versatile language which is not unknown to the researchers and tech geeks. These two languages are the most talked about in the industry today. Nevertheless, both of them are extensively used in data analytics and data science. However, the debate regarding which one to opt for among the two has always been constant. But worry no longer because here we will discuss both of them, in brief, to help you with your choice!

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Python

Python is really one of the most popular languages in the industry. The open-source nature of the language makes it a popular choice for scripting and automation works. 

Besides, Python is powerful, effective, and easy to learn. Moreover, Neural Network Machine learning Python boasts of its efficient high-level data structures and for object-oriented programming.

Advantages

  • Easy to learn and effective too.
  • Exhaustive support from active communities.
  • Python enjoys built-in support for the datatypes.

Disadvantages

  • Your computer might slow down a little when you are running Python. This is in contrast to when you are running other languages like C or Java.

Scala

If you want an object-oriented, functional programming language, then Scala would certainly be your first choice. It was basically built for the Java Virtual Machine (JVM) and remains the most compatible programming language with Java code till date.

Advantages

  • Scala can utilise the majority of the JVM libraries, thus helping them to be embedded in the enterprise code.
  • It shares an array of readable syntax features of the popular languages, like Ruby.
  • Scala brags about numerous incredible features like string comparison advancements, pattern matching and its likes.

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Disadvantages

  • Scala has a limited number of users in the communities, which encourages lesser interactions and stunted growth.
  • At times the type-information in Scala is really complex to comprehend. This difficulty can be attributed to the functional and object-oriented nature of the language.

We hope that this article helps you to have a brief insight into two of the most demanding programming languages: Python and Scala.

Now, if you want to enrol yourself in Computer vision course Python, you can reach us right at Dexlab Analytics, the most reputable institute for Big Data Analytics. Also, if you are looking for all-inclusive Deep learning for computer vision Course, turn no further than our premium institute to shoot your career up!

 

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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.

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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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The Rising Popularity of Python in Data Science

The Rising Popularity of Python in Data Science

Python is the preferred programming language for data scientists. They need an easy-to-use language that has decent library availability and great community participation. Projects that have inactive communities are usually less likely to maintain or update their platforms, which is not the case with Python.

What exactly makes Python so ideal for data science? We have examined why Python is so prevalent in the booming data science industry — and how you can use it for in your big data and machine learning projects.

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Why Python is Dominating?

Python has long been known as a simple programming language to pick up, from a syntax point of view, anyway. Python also has an active community with a vast selection of libraries and resources. The result? You have a programming platform that makes sense of how to use emerging technologies like machine learning and data science.

Professionals working with data science applications don’t want to be bogged down with complicated programming requirements. They want to use programming languages like Python and Ruby to perform tasks in a hassle-free way.

Ruby is excellent for performing tasks such as data cleaning and data wrangling, along with other data pre-processing tasks. However, it doesn’t feature as many machine learning libraries as Python. This gives Python the edge when it comes to data science and machine learning.

Python also enables developers to roll out programs and get prototypes running, making the development process much faster. Once a project is on its way to becoming an analytical tool or application, it can be ported to more sophisticated languages such as Java or C, if necessary.

Newer data scientists gravitate toward Python because of its ease of use, which makes it accessible.

Why Python is Ideal for Data Science?

Data science involves extrapolating useful information from massive stores of statistics, registers, and data. These data are usually unsorted and difficult to correlate with any meaningful accuracy. Machine learning can make connections between disparate datasets but requires serious computational sophistry and power.

Python fills this need by being a general-purpose programming language. It allows you to create CSV output for easy data reading in a spreadsheet. Alternatively, more complicated file outputs that can be ingested by machine learning clusters for computation.

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Consider the Following Example:

Weather forecasts rely on past readings from a century’s worth of weather records. Machine learning can help make more accurate predictive models based on past weather events. Python can do this because it is lightweight and efficient at executing code, but it is also multi-functional. Also, Python can support object-orientated and functional styles, meaning it can find an application anywhere.

There are now over 70,000 libraries in the Python Package Index, and that number continues to grow. As previously mentioned, Python offers many libraries geared toward data science. A simple Google search reveals plenty of Top 10 Python libraries for data science lists. Arguably, the most popular data analysis library is an open-source library called pandas. It is a high-performance set of applications that make data analysis in Python a much simpler task.

No matter what data scientists are looking to do with Python, be it predictive causal analytics or prescriptive analytics, Python has the toolset to perform a variety of powerful functions. It’s no wonder why data scientists embrace Python.

If you are interested in Python Certification Training in Delhi, drop by DexLab Analytics. With a team of expert consultants, we provide state-of-the-art Machine Learning Using Python training courses for aspiring candidates. Check out our course itinerary for more information.

 

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ThoughtSpot-Alteryx Partnership to Revolutionize Analytics with AI

ThoughtSpot-Alteryx Partnership to Revolutionize Analytics with AI

ThoughtSpot has recently partnered with Alteryx to boost analytics endeavors with AI.

The aim is to transform businesses through systematic data analytics simplifying the entire process for business users. Right from data preparation to insight generation, Alteryx in association with ThoughtSpot is expected to perform advanced analytics, evaluate meaningful insights and answer relevant questions – all using an all-natural, simple language search.

Constructing Business-driven Data Analytics Pipeline

Data is everywhere. Small and big business houses leverage the power of data science and invest large amounts in technologies that transform industries. Nevertheless, there are still many organizations that fail to put this data into the right hands. Data matters the most for business people. They are the ones who drive the change and data needs to reach them.

The latest collaboration between Alteryx and ThoughtSpot has re-conceptualized the whole idea of data pipeline by empowering every single business person, irrespective of their technical superiority, to connect, evaluate and model intricate data using Alteryx and then immediately start discovering insights using search and AI-driven analytical frameworks by ThoughtSpot. The amalgamation comes with a set of new tools that let Alteryx users add original ThoughtSpot Bulk Loader connections and ThoughtSpot TQL statements directly into a workflow of Alteryx.

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This system automatically offers several advantages to the businesses, including:

  • Connect, modify and model intricate data in a fraction of a second – The users can now collect data from their organization with the help of a high throughput fast parallel loader, transform it and then model it for further analysis more efficiently than before.
  • Faster results – Business users enjoy the liberty to work with data that matters the most, apply sophisticated analytical tools and unravel crucial automated insights while releasing valuable analytical resources that can be employed in other high-value and strategic projects.
  • Advance with AI – The users get to develop machine learning and AI models in an absolutely code-friendly and code-less environment. Later, they can use search to absorb and share insights derived from these models.

The Leaders’ Opinions

On the above context, Toni Adams, Vice President, Global Alliances & Partnerships at ThoughtSpot shared that today’s enterprises cannot afford to wait for days in the hope of putting insights in the hands of the business users. The business users need to be empowered with every mean to churn out insights directly from the most complex data. Fortunately, the recent partnership is likely to change the complete dynamo of data pipeline by transforming every employee of an organization into a data-driven powerhouse capable enough to derive insights instantly. This will eventually make operations simpler and productive.

Furthermore, Steve Walden, Senior Vice President of Business Development, Alteryx emphasized that they believe in strengthening business capabilities by empowering every data worker of the organization. These workers, in turn, will transmute data into actionable insights faster and tweak business outcomes. The partnership with ThoughtSpot is expected to enhance ease-of-use throughout the entire analytics pipeline and unleash unprecedented value for every joint customer.

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About Alteryx

Transforming businesses through data analytics, Alteryx is a reputable end-to-end analytics platform that entitles data analysts to connect, deliver powerful insights and witness the thrill of deriving answers pretty faster. For more information, click www.alteryx.com.

A premier data analytics training institute, DexLab Analytics offers comprehensive Alteryx certification training in Delhi. Through hands-on training and real-life examples, the expert consultants help you master the intricacies of Alteryx for complex data analytics. Enroll Now.

 

The blog has been sourced from: www.marketwatch.com/press-release/thoughtspot-partners-with-alteryx-to-advance-analyticswith-ai-2019-03-14

 

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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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Demand for Data Analysts is Skyrocketing – Explained

Demand for Data Analysts is Skyrocketing - Explained

The salary of analytics professionals outnumbers that of software engineers by more than 26%. The wave of big data analytics is taking the world by storm. If you follow the latest studies, you will discover that there has been a prominent growth in median salary over several experience levels in the past three years (2016 to 2018). In 2019, the average analytics salary has been capped at 12.6 lakh per annum.

The key takeaway is that the salary structure of analytics professionals continues to beat other tech-related job roles. In fact, data analysts are found out-earning their Java correspondents by nearly 50% in India alone. A latest survey provides an encompassing view of base and compensation salaries in data science along with median salaries followed across diverse job categories, regions, education profiles, experience, tools and skills.

In this regard, a spokesperson of a prominent data analytics learning institute was found saying, “The demand for AI skills is expected to increase rapidly, which is also reflected by the fact that AI engineers command a higher salary than peers.” She further added, “Many of our clients have realized that investing in data-driven skills at the leadership level is a determining factor for the success of digital and AI initiatives in the organization. With the increasing adoption of digital technologies, we expect an enduring growth of Data Science and AI initiatives to offer exciting and lucrative career options to new age professionals,”

Over time, we are witnessing how markets are evolving while the demand for skilled data scientists is following an upward trend. It is not only the technology firms that are posting job offers, but the change is also evident across industries, like retail, medical, retail and CPG amongst others. These sectors are enhancing their analytical capabilities implying an automatic increase in the number of data-centric jobs and recruitment of data scientists.

Points to Consider:

  • In the beginning, nearly 76% of data analysts earn 6-lakh figure per annum.
  • The average analytics salary observed in 2018-19 is 12.6 lakh.
  • In terms of analytics career, Mumbai offers the highest compensation of 13.7 lakh yearly, followed by Bangalore at 13 lakh.
  • Mid-level professionals proficient in data analytics are more in demand.
  • Knowing Python is an added advantage; Python Programming training will help you earn more. Expect a package of 15.1 lakh.
  • Nevertheless, we often see a pay disparity for female data scientists against their male counterparts. While women’s take-home salary is 9.2 lakh, male from the same designation and profession earns 13.7 lakh per annum.

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As endnotes, the demand for data science skills is skyrocketing. If you want to enter into this flourishing job market, this is the best time! Enroll in a good data analyst course in Delhi and mould your career in the shape of success! DexLab Analytics is a top-notch data analyst training institute that offers a plethora of in-demand skill training courses. Reach us for more.

 

This article has been sourced fromwww.tribuneindia.com/news/jobs-careers/data-analytics-professionals-ride-the-big-data-wave/759602.html

 

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