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World’s Biggest Tech Companies 2018: A Comprehensive List

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World’s Biggest Tech Companies 2018: A Comprehensive List

Talking of world’s biggest and most-valued companies, people instinctively turn their gaze to technology sector. Ever since the phenomenal dotcom boom and onset of WorldWideWeb, the tech firms have been garnering accolades owing to their huge market caps and power to disrupt conventional industries.

FYI: A public company’s market cap refers to market capitalization, which is a measurement of the value of its current outstanding shares. To calculate the market cap, you need to just multiply the current stock price with the outstanding number of shares. Talking about today’s market condition that would mean a lot of numbers.

To evaluate the top notch tech companies across the globe, Howmuch.net took into consideration the market cap ranking given by Forbes and split it in an unique way. Obviously, the US and China houses some of the wealthiest companies, worth hundreds of billions of dollars.

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Below we’ve 10 most high-valued tech companies on the planet, according to their market caps as of October 2018:

  • Apple: $1.1T
  • com: $962B
  • Microsoft: $883B
  • Alphabet: $839B
  • Facebook: $460B
  • Alibaba: $412B
  • Tencent Holdings: $383B
  • Samsung Electronics: $297B
  • Cisco Systems: $224B
  • Intel: $222B

(Give credits)

“At first glance, retailing and media appear to be much more evenly distributed than they actually are,” the report indicated. “Consider how Amazon has so dominated the market that its North American competitors are so small, they don’t even make it onto the list of top 50 companies. Amazon is so big, there is literally no other company in sight.”

Key Takeaways:

  • As always, Apple tops the list of tech companies, not only as the biggest tech company but it’s also the eighth largest company in the world according to Forbes’ Global 2000 list. The company saw $247.5 billion in sales, $53 billion in profit, $367.5 billion in assets and a market cap of $927 billion for the past year.
  • The AntiTrust Regulations and growth of 5G wireless can bring forth major changes in the modern tech market, and we are eagerly waiting for such shift in focus.

As parting thoughts, we would like to say that though the current market setup has been quite steady for a while, a surge of change may soon be here. Interestingly, Chinese tech bigwig, Alibaba is mostly likely to expand its scopes and capabilities, while 5G connectivity may appear fetching. Moreover, the speculation says antitrust regulation could disrupt functionalities of some of these companies.

To stay updated about technology-related news and innovations, follow DexLab Analytics. It’s a premier institution famous for state of the art data science courses in Delhi. For more, check out their homepage: an army of data science related courses are on offer.

 
The blog has been sourced from — www.techrepublic.com/article/the-10-most-valuable-tech-companies-in-the-world
 

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Best Data Science Interview Questions to Get Hired Right Away

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Best Data Science Interview Questions to Get Hired Right Away

Data scientists are big data ninjas. They tackle colossal amounts of messy data, and utilize their imposing skills in statistics, mathematics and programming to collect, manage and analyze data. Next, they combine all their analytic abilities – including, industry expertise, encompassing knowledge and skepticism to unravel integral business solutions of meaningful challenges.

But how do you think they become such competent data wranglers? Years of experience or substantial pool of knowledge, or both? In this blog, we have penned down the most important interview data questions on data science – it will only aid you crack tough job interviews but also will test your knowledge about this promising field of study.

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What do you mean by data science?

Data is a fine blend of statistics, technical expertise and business acumen. Together they are used to analyze datasets and predict the future trend.

Which is more appropriate for text analytics – R or Python?

Python includes a very versatile library, known as Pandas, which helps analysts use advanced level of data analysis tools and data structures. R doesn’t have such a feature. Therefore, Python is the one that’s highly suitable for text analytics.

Explain a Recommender System.

Today, a recommender system is extensively deployed across multiple fields – be it music recommendations, movie preferences, search queries, social tags, research and analysis – the recommender system works on a person’s past to build a model to predict future buying or movie-viewing or reading pattern in the individual.

What are the advantages of R?

  • A wide assortment of tools available for data analysis
  • Perform robust calculations on matrix and array
  • A well-developed yet simple programming language is R
  • It supports an encompassing set of machine learning applications
  • It poses as a middleman between numerous tools, software and datasets
  • Helps in developing ace reproducible analysis
  • Offers a powerful package ecosystem for versatile needs
  • Ideal for solving complex data-oriented challenges

What are the two big components of Big Data Hadoop framework?

HDFS – It is the abbreviated form of Hadoop Distributed File System. It’s the distributed database that functions over Hadoop. It stores and retrieves vast amounts of data in no time.

YARN – Stands for Yet Another Resource Negotiator. It aims to allocate resources dynamically and manage workloads.

How do you define logistic regression?

Logistic regression is nothing but a statistical technique that analyzes a dataset and forecasts significant binary outcomes. The outcome has to be in either zero or one or a yes or no.

How machine learning is used in real-life?

Following are the real-life scenarios where machine learning is used extensively:

  • Robotics
  • Finance
  • Healthcare
  • Social media
  • Ecommerce
  • Search engine
  • Information sharing
  • Medicine

What do you mean by Power Analysis?

Power analysis is best defined as the process of determining sample size required for determining an impact of a given size from a cause coupled with a certain level of assurance. It helps you understand the sample size estimate and in the process aids you in making good statistical judgments.

To get an in-depth understanding on data science, enroll for our intensive Data Science Certification – the course curriculum is industry-standard, backed by guaranteed placement assistance.

The blog has been sourced from — intellipaat.com/interview-question/data-science-interview-questions

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5 Big Challenges That Data Scientists Face Each Day

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5 Big Challenges That Data Scientists Face Each Day

Data is lucrative; the world is revolving around how we churn out data. As a result, there’s been a high demand for data scientists. But of course, as rightfully said there’s no gain without pain – the promising field of data science is laden with many challenges, which needs to be overcome by expert consultants under needful guidance and with deft expertise.

Below, we’ve mentioned top 5 data science challenges, and how to handle them well…

Address the Specifics

Successful data scientists don’t try to do everything on their own. Instead, they individually focus on a single specific area. “I would encourage new professionals to understand that data science is a bit like medicine—it’s a vast and vague term that encapsulates wildly different practices under one roof,” said Tal Kedar, CTO at Optimove. “Data scientists [can have] very different engineering skill sets [and be] experienced with very different platforms and tools.”

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Be Guided By Your Intuition

Being a data scientist not only exposes you to the question of ‘how’, but also ‘why’. No longer do you just sift through data to make connections, instead you have to use your comprehensive knowledge to develop ‘mental model’, which can be accepted or rejected by your data.

Cross-Department Expertise is Appreciable

“The best data scientists are not just statisticians or machine learning experts; they are also an authority in the field or business where they are applying those skills,” said Kedar. It’s no hard fact, data scientists are arguably the best bridge between technical and non-technical teams. Quite naturally, whichever career they chose next, their skills will be treated as an asset to the next company in question.

Seamless Flow of Communication

Communication amongst the data teams is crucial – data scientists need to explain technical concepts to audiences from other departments, including executives and stakeholders, who might not belong from technical backgrounds. “It can be exciting to share all of the technical complexities that got you to your conclusions,” said Andrew Seitz, senior data analyst at Snowflake. “But what your stakeholders need are the key findings and action items. Save the details for the appendix (or Q&A).”

Raw Data Play

The biggest challenge for data scientists is to find ways of using the data – how the process of data extraction, data cleaning, data analysis and data modeling are carried out. Data scientists need to possess broad domain expertise in all programming languages, such as Python, R and SQL.

The work life of a data scientist revolves around creating clean data sets loaded with useful information on which machine learning algorithms can be applied. This kind of job is mostly treated as an art instead of science, because a majority of hard work and effort goes unnoticed when observing the final product, just like an artist’s craft.

The scope and capability of data science is encompassing, so are the challenges. But, of course, most of the challenges can be mitigated with considerable preparation and communication. How? With an intensive Python data science course – from the expert consultants of DexLab Analytics.

 

The blog has been sourced from — www.forbes.com/sites/laurencebradford/2018/09/06/8-real-challenges-data-scientists-face/#8adbc206d999

 

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3 Potent IoT Challenges That Keeps Data Scientists Always on Toes

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3 Potent IoT Challenges That Keeps Data Scientists Always on Toes

The job responsibility of data scientists is no mean feat. They stay under a lot of pressure. A wide number of stumbling blocks are laid in front of them, which makes it really difficult for them to secure the long-shot business goals and objectives.

As prevention is better than cure – being aware of the challenges always help data scientists plot the shortest and smartest route to success, and we can’t agree more. Brace yourselves! Below, we’ve enumerated some of the challenges data scientists face while getting started with an IoT project:

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Inferior Data Quality

Messy data is life and soul of data scientists. Irrespective of business scale, the job of every data scientist is to organize data in the correct manner. But, however organizing them may require adequate time as well as hard work.

A fundamental rule – avoid manual data, wherever possible. Intelligent data compilation is the final key to high quality data, which is a prerequisite for favorable company operation. It includes crisp communication, regular anomaly detection, logic determination and well-defined industry standards. Another way to tame your data can be through application integration tools – they are a fabulous way to automate data entry and lessen escalation of typographical errors, individual eccentricities, staggering spellings and more from the data.

Once data is in the right format and quality, data scientists can start slicing off the data they don’t need any more, which takes us to the next step.

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Shedding Out Excessive Data

Though big data is found in abundance, too much of data can also pose a substantial challenge. This is why employing superior data selection techniques and minimizing features are supported, they help eliminate unwanted chaos cutting through what matters the most.

What happens is that when data becomes excessively large, we often end up developing high-end predictive models that fails to deliver productive results. But, on the other hand, if you track the events, giving importance to validation and testing routines, the outcomes will spell perfection. And that’s what we are looking forward to.

Predictive Analytics is the Key

IoT has made predictive analytics a daunting reality. Owing to its critical business significance, predictive analytics is quickly accelerating along the priority ladder of IoT stakeholders. However, take a note, this breed of analytics may not be fruitful in every instance. It’s imperative to begin your analytics endeavor by clearly defining your module’s objective, followed by needed research and valuation.

Next, you need to sync in with subject matter pundits to ascertain which predictions will lead you closer to fulfilling the business objectives. Following to this, you have to be sure that you have all the data required to make prediction. In other cases, you can re-set goals, anytime.

Find the best Data Science Courses in Noida… At DexLab Analytics. Get detailed information on the website.

 

The blog has been sourced from — www.networkworld.com/article/3305329/internet-of-things/3-iot-challenges-that-keep-data-scientists-up-at-night.html

 

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Explaining the Job Nitty Gritty of a Data Scientist

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Explaining the Job Nitty Gritty of a Data Scientist

What do data scientists do? Since the inception of the term data science, we’ve heard about how it transforms all major sectors, including retail, agriculture, health, legal, telecommunications and automobile industry, but little do we know what exactly the job entails.

Following a recent DataCamp podcast DataFramed, we found out a set of key things about data scientists, and they are as follows:

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Not only tech, but other industries are being explored

A prominent data scientist from Convoy shared insights about how their company is leveraging data science to revolutionize North American trucking industry. Then again, data science is also deemed to make a significant impact on cancer research. So, from this we can understand that data science is not only limited within the walls of technology but has started to seep through different industry verticals.

via GIPHY

It’s beyond AI and self-driving cars

Sure, deep learning and machine learning are powerful applications, but not all data scientists are lost waddling around these top notch techniques. Instead, most of the regular data scientists earn their daily bread and butter through data accumulation and cleaning, creating reports and dashboards, data viz, statistical inference, communicating and convincing decision-makers about key outcomes.

Skill evolution

“Which skill is more important for a data scientist: the ability to use the most sophisticated deep learning models, or the ability to make good PowerPoint slides?” – The latter is crucial, so is communicating results.

However, these skills are likely to change very quickly. In a very short span of time. Rapid development across diverse open-source ecosystem is evident; as a result any kind of skill or expertise is unlikely to last long.

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Specialization is the key

It’s better to break down data science into three main components: Business Intelligence, which talks about pulling out data and presenting it to the right people in the form of reports, dashboards and mails; Decision Science, which is all about gathering company data and analyzing it for decision-making; and Machine Learning, which deals with the ways in which we can use data science models and put them into production.

Choosing a distinct career path is an emerging trend and it’s gaining a lot of popularity for all the right reasons.

Ethics is a driving factor

No wonder, this profession is full of uncertainty; at a time, when most of our daily interactions are influenced by algorithms designed by data scientists, what role do you think ethics play? On this context, this is what Omuji Miller, the senior machine learning data scientist at GitHub has to say:

‘We need to have that ethical understanding, we need to have that training, and we need to have something akin to a Hippocratic oath. And we need to actually have proper licenses so that if you actually do something unethical, perhaps you have some kind of penalty, or disbarment, or some kind of recourse, something to say this is not what we want to do as an industry, and then figure out ways to remediate people who go off the rails and do things because people just aren’t trained and they don’t know.’

Soon, we’re approaching a state where the need to maintain ethical standards would come from within data science itself and advocates, legislators and other stakeholders. Hope this consensus comes soon.

The data science revolution is quite the order of the day, and it’s going to stay for a while. So, if you want to ace up your data skills, we’ve superior Data Science Courses in Delhi. Just, visit our website and pore over our course offerings.

 

The blog has been sourced from — hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists

 

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Fundamental Concepts of Statistics for Data Science Beginners- Part One

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Fundamental Concepts of Statistics for Data Science Beginners- Part One

Do you aspire to be a data scientist? Then is it essential that you have a solid understanding of the core concepts of statistics. Everyone doesn’t have a Ph.D. in Statistics. And that isn’t the only way to excel in the field of data science. But yes, knowing stats well is a prerequisite for data science.

Nowadays, popularly used libraries, like Tesorflow, liberate the user from the intricacies of complex mathematics. Still, it is advisable to be familiar with the fundamental principles on which they work, because that will enable you to use the libraries better.

In this blog, we attempt to shed light on some basic concepts, theorems and equations of statistics for data science.

Statistical Distributions:

Statistical distributions are important tools that you must arm yourself with to be a skilled data scientist. Here, we shall talk about two important distributions, namely Poisson distribution and Binomial distribution.

Poisson distribution:
This distribution is used to find out the number of events that are expected to occur during an interval of time. For example, the number of page views in one second, the number of phone calls in a particular period of time, number of sales per hour, etc.

The symbols used in the equation are:

x: exact number of successes

e: constant equal to 2.71828 approximately

λ: average number of successes per time interval

Poisson distribution is used for calculating losses in manufacturing. Let us consider that a machine generates metal sheets that have ‘x’ flaws per yard. Suppose the error rate is 2 per yard of sheet (λ). Applying this information to Poisson distribution, we can calculate the probability of having exactly two errors in a yard.

Source: Brilliant.org

Poisson distribution is used for faster detection of anomalies.

Binomial distribution:

This is a very common distribution in Statistics. Suppose you have flipped a coin thrice. Using basic combinatorics for flipping a coin thrice, we see that there are eight combinations possible. We find out the probabilities of getting 0, 1, 2 or 3 heads and plot this on a graph. This gives us the binomial distribution for this particular problem. It must be remembered that Binomial distribution curve is similar to a Normal distribution Curve. Normal distribution is used when values are continuous and Binomial distribution is used for discrete values.

Source: mathnstuff.com

Binomial distribution is a discrete probability distribution where number of trials is predetermined and there are two possible outcomes– success and failure, win or lose, gain or loss. Depending on a few conditions, like the total number of trails is large, the probability of success is near 1 and the probability of failure is near 0, the trails are independent and identical, etc., the binomial distribution is approximated to a normal distribution.

Source: MathBitsNotebook

Binomial distribution has many applications in business. For example, it is estimated that 5% of tax returns for individuals with high net worth in USA is fraudulent. These frauds might be uncovered through audits. Binomial distribution is used to find out for ‘n’ number of tax returns that are audited, what is the probability for say 5 fraudulent returns to be uncovered.

There are some more probability distributions, like Bernoulli and Geometric distributions. We shall cover that and more in the following blogs. So, stay tuned and follow DexLab Analytics. The experts here offer top-quality data science courses in Delhi. Go through the data science certification details right now!

 

References:

upgrad.com/blog/basics-of-statistics-for-data-science

anomaly.io/anomaly-detection-poisson-distribution

analyticsvidhya.com/blog/2017/09/6-probability-distributions-data-science

 

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Business Intelligence: How to Enhance User Adoption?

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Business Intelligence: How to Enhance User Adoption?

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

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

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Reasons behind Lower User Adoption of Business Intelligence:

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

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

How Companies Can Improve Data-Driven Mindsets?

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

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

Introduce BI as a necessity, not luxury

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

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

Promote Favorable Impacts of BI

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

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

Continuous Training is a Must

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

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

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

Want to know more about Data Science Courses in Noida? Drop by DexLab Analytics; for a fulfilling learning experience, opt for their Data Science Courses. They are simply excellent and student-friendly. 

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

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

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

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

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

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

Generality

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

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Specificity

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

Performance

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

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

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

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

Parting Thoughts

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

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

 

The blog has been sourced from – 

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

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

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

 

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

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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 21st 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?!

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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

 

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