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Discover Top 5 Data Scientist Archetypes

Discover Top 5 Data Scientist Archetypes

Data science jobs are labelled as the hottest job of the 21st century. For the last few years, this job profile is indeed gaining accolades. And yes, that’s a good thing! Although much has been said about how to progress towards a successful career as a data scientist, little do we know about the types of data scientists you may come across in the industry! In this blog, we are going to explore the various kinds of data scientists or simply put – the data scientist archetypes found in every organization.

Generalist

This is the most common type of data scientists you find in every industry. The Generalist contains an exemplary mixture of skill and expertise in data modelling, technical engineering, data analysis and mechanics. These data scientists interact with researchers and experts in the team. They are the ones who climb up to the Tier-1 leadership teams, and we aren’t complaining!

Detective

He is the one who is prudent and puts enough emphasis on data analysis. This breed of data scientists knows how to play with the right data, incur insights and derive conclusions. The researchers say, with an absolute focus on analysis, a detective is familiar with numerous engineering and modelling techniques and methods.

Maker

The crop of data scientists who are obsessed with data engineering and architecture are known as the Makers. They know how to transform a petty idea into concrete machinery. The core attribute of a Maker is his knowledge in modelling and data mechanisms, and that’s what makes the project reach heights of success in relatively lesser time.

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Oracle

Having mastered the art and science of machine learning, the Oracle data scientist is rich in experience and full of expertise. Tackling the meat of the problem cracks the deal. Also called as data ninjas, these data scientists possess the right know how of how to deal with specific tools and techniques of analysis and solve crucial challenges. Elaborate experience in data modelling and engineering helps!

Unicorn

The one who runs the entire data science team and is the leader of the team is the Unicorn. A Unicorn data scientist is reckoned to be a data ninja or an expert in all aspects of data science domain and stays a toe ahead to nurture all the data science nuances and concepts. The term is basically a fusion version of all the archetypes mentioned above weaved together – the job responsibility of a data unicorn is impossible to suffice, but it’s a long road, peppered with various archetypes as a waypoint.

Organizations across the globe, including media, telecom, banking and financial institutions, market research companies, etc. are generating data of various types. These large volumes of data call for impeccable data analysis. For that, we have these data science experts – they are well-equipped with desirable data science skills and are in high demand throughout industry verticals.

Thinking of becoming a data ninja? Try data science courses in Delhi NCR: they are encompassing, on-point and industry-relevant.

 

The blog has been sourced fromwww.analyticsindiamag.com/see-the-6-data-scientist-archetypes-you-will-find-in-every-organisation

 

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How to Build and Maintain Successful Data Science Teams?

How to Build and Maintain Successful Data Science Teams?

Businesses are becoming smarter. They are unleashing a bigger impact. Driven by innovation and humongous volumes of data, organizations observe market trends and predict customer behavioral patterns – no wonder, this industry is the right place to incubate newer technologies and explore higher horizons.

Data science is the bull’s eye of this new-age industry. It is unabashedly predictive rather than being conclusive. As a result, garnering cross-team collaborations in this particular field of science may turn a bit challenging. A good data science team is a combination of talented professionals, high intellect, powerful body of knowledge and advanced data-tackling skills.

To give you a hand, we’ve rounded up top trends or tips to follow to get to the bottom of the art of running successful data science teams:

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Diversity is the Key

Diverse backgrounds, on-point technical expertise and voluminous domain knowledge is what makes a data science team high on diversity. A healthy concoction of machine learning skills, knowledge in mathematics and statistics and conversational skills is critical for a productive team. Just having one or two skills is simply not enough, anymore!

Structure and Prioritize

Once you have a team by your side, you need to start structuring an operating model. The data needs to be deconstructed into sizeable prioritized slices. After that, every data-related measure should be backed by needful communication – it helps in determining the bottlenecks and devise effective solutions.

Experimentation Helps

Experimentation is crucial as well as important. Unless you experiment, you can never scale new heights and this is equally applicable in data science. In the sprawling field of data science, every project starts with a challenge and a set of hypothesis that addresses it. However, you won’t find any particular roadmap to success. Hence, it opens a lot of room for innovation and experimentation.

Collective Responsibility

Yielding data science initiatives demand absolute cooperation, collaborative responsibilities and fine reporting structures. A healthy coordination between analytics and business teams, specifically IT, is extremely important for overall business success. Data science experts need to collaborate with each other and strike a tone of success.

Data Accuracy

Gain access to data bank and fine-tune the accuracy of your analysis. Business users leverage improved functional tools of analytics for overall business success. Data is the key, and data availability and quality are the pillars on which organizations stand. Therefore, we suggest practice data accuracy for improved data analytics and boost future business goals.

Today, online resources and libraries can help you almost everything. What they cannot do is feed you is the underlying intricacies of data science and how to devise an effective solution utilizing the base knowledge of mathematics, statistics and machine learning technology. For these, you need an expert Data Science Certification – it will help you discover the grey unknown territories of data and educate you on how to tame them.

Reach us at DexLab Analytics – we offer in-demand data science courses for students and professional, both.

 

The blog has been sourced fromwww.analyticsindiamag.com/the-art-of-running-successful-data-science-teams

 

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World’s Biggest Tech Companies 2018: A Comprehensive List

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

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 fromintellipaat.com/interview-question/data-science-interview-questions

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5 Big Challenges That Data Scientists Face Each Day

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 fromwww.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

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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DexLab Analytics’ AUGUST OFFER: Everything You Need to Know Of

DexLab Analytics’ AUGUST OFFER: Everything You Need to Know Of

We are happy to announce that we’re rolling some good news your way – DexLab Analytics is all set to launch exhaustive modules in Deep Learning with AI starting with Artificial Neural Networks using Python, MS Excel, Dashboards, VBA Macros, Tableau BI, Visualization and Python Spark for Big Data from September 1, 2018. The course modules are on in-demand skills and they are taking the world quite by a storm.

DexLab Analytics’ AUGUST OFFER

Big data, data science and artificial intelligence are buzz words these days. More and more people are coming forward and showing keen interest on these nuanced notions that solves real-world problems. This is why we didn’t want to fall behind. We understand the importance of data in this digitized world, and accordingly have chalked out our intensive industry-ready courses.

Deep Learning and AI starting with Artificial Neural Networks using Python course module is a 30-hour long training program that gives exposure to MLP, CNN, RNN, LSTM, Theano, TensorFlow and Keras. It includes more than 8 projects out of which a couple of focuses on development of models in to Image and Text recognition. MS Excel, Dashboards and VBA Macros certification is curated by the expert consultants after combining industry expertise with academician’s knowledge. The course duration is in total of 24 hours and is conducted by seasoned professionals with more than 8 years of industry experience specific to this budding field of science.

DexLab Analytics’ August Offer is On Machine Learning & AI

DexLab Analytics’ August Offer is On Machine Learning & AI

Next, we have30-hour hands-on classroom training on Tableau BI & Visualization certification, which teaches young minds how graphical representation of data unlocks company future trends and take quicker decisions. Tableau is one of the fastest evolving BI and data visualization tool. With that in mind, we offer a learning path to all you students by framing a structured approach coupled with easy learning methodology and course curriculum.

DexLab Analytics Offers MS Excel, Dashboards and VBA Macros Certification!

DexLab Analytics Offers MS Excel, Dashboards and VBA Macros Certification!

Lastly, our Big Data with PySpark certification is another gem in the learner’s cap: the Spark Python API (PySpark) exposes users to the Spark Programming model with Python. Apache Spark is an open source and is touted as a significant big data framework for pivoting your tasks in a cluster. The main objective of this course is to teach budding programmers how to write python code using map-reduce programming model. The 40-hours hands-on classroom training will talk about Big Data, overview of Hadoop, Python, Apache Spark, Kafka, PySpark and Machine Learning.

Now, first 12 students who happen to register for each course on or before 30th August, 2018 will get alluring discount offer on the total course fee. Interesting, isn’t it? So, what are you waiting for? Go, grab all the details about AUGUST OFFER: to register, call us at +91 9315 725 902 / +91 124 450 2444 or hit the link below – www.dexlabanalytics.com/contact

 

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Explaining the Job Nitty Gritty of a Data Scientist

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

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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