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Discover Top 5 Data Scientist Archetypes

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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.

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

 

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What Does a Business Analyst Do: Job Responsibilities and More!

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What Does a Business Analyst Do: Job Responsibilities and More!

A flamboyant, sophisticated technology lashed with a heavy stroke of sci-fi, AI and machine learning – is today’s data science. To manage, control and understand such an elusive concept, we need highly skilled data specialists – they must have mastered thoroughly the art and science of machine learning, analytics and statistics.

As the world is becoming more dynamic, the roles of data analysts and professionals are found to be increasingly inclined towards precision, versatility and eccentricity. More and more, they are expected to do things differently, posing as catalysts for change. They play an incredible role in inspiring others and bringing accuracy and accountability within an organization.

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Data Analysts Facilitate Solutions for Stakeholders

“Business analysis involves understanding how organizations function to accomplish their purposes and defining the capabilities an organization requires to provide products and services to external stakeholders,” shares International Institute of Business Analysis in its BABOK Guide.

The main job of a business analyst is to understand the current situation of a company and facilitate a respective solution to the problem. Mostly, a team of analysts work with the stakeholders to define their business goals and extract what they expect to be delivered. They gather a long range of business-fulfilled conditions and capabilities, document them in a collection and then eventually frame and strategize a plausible solution.

Analysts Have a Multifaceted Job Role

Mostly, they wear many hats as the tasks of analysts are widely versatile and always changing. Below, we have mentioned a few most common job responsibilities they have to perform every day:

  • Understand and analyze business needs
  • Address a business problem
  • Construe information from stakeholders
  • Fulfill model requirements
  • Facilitate solutions
  • Project management
  • Project development
  • Ensure quality testing

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The Title ‘Business Analyst’ Hardly Matters

As a matter of fact, the title ‘business analyst’ doesn’t matter much. To fulfill the role of a ‘business analyst’, you don’t have to an analyst at the first place. Many execute the tasks as part of their existing role – data analysts, user experience specialists, change managers and process analysts – each one of them can feature business analyst behaviour.

Put simply, you don’t have to be a business analyst to do the job of a business analyst.

Business Analysts Act As Interpreters

As always, different stakeholders have different goals, needs and knowledge regarding their businesses. Stakeholders can be anyone – managers to end users, vendors to customers, developers to testers, subject matter experts, architects and more. So, it depends on the analysts to bring together all this knowledge and analyze the information gathered. This, in turn, offers a clear understanding of company goals and vision. It bridges the gap between the business and IT.

For this and more, business analysts are often compared with interpreters. Just the way the latter translates French into English – analysts too translate their stakeholders’ query and needs into a language that IT professionals can easily grasp.

Hope this comprehensive list of thoughts has helped you understand what analysts do in general!

If you want to become a data analyst or interested in the study of analytics, drop by DexLab Analytics. They are a one-stop-destination to grab data analyst certification. For more, reach us at dexlabanalytics.com

 

 The blog has been sourced from ― elabor8.com.au/what-does-a-business-analyst-actually-do

 

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Private Banks, Followed by E-commerce and Telecom Industry Shows High Adoption Rates for Data Analytics

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Private Banks, Followed by E-commerce and Telecom Industry Shows High Adoption Rates for Data Analytics

Are you looking for a data analyst job? The chances of bagging a job at a private bank are more than that a public bank. The former is more likely to hire you than the latter.

As a matter of fact, data analytics is widely being used in the private banking and e-commerce sectors – according to a report on the state of data analytics in Indian business. The veritable report was released last month by Analytics India Magazine in association with the data science institute INSOFE. Next to banking and ecommerce, telecom and financial service sectors have started to adopt the tools of data analytics on a larger scale, the report mentioned.

The report was prepared focusing on 50 large firms across myriad sectors, namely Maruti Suzuki and Tata Motors in automobiles, ONGC and Reliance Industries under oil-drilling and refineries, Zomato and Paytm under e-commerce tab, and HDFC and the State Bank of India in banking.

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If you follow the study closely, you will discover that in a nutshell, data analytics and data science boasts of a healthy adoption rate all throughout – 64% large Indian firms has started implementing this wonder tool at their workplaces. As a fact, if a firm is found to have an analytics penetration rate of minimum 0.75% (which means, at least one analytics professional is found out of 133 employees in a company), we can say the company has adopted analytics.

Nevertheless, the rate of adoption was not universal overall. We can see that infrastructure firms have zero adoption rates – this might be due to a lack of resources to power up a robust analytics facility or whatever. Also, steel, power and oil exhibited low adoption rates as well with not even 40% of the surveyed firms crossing the 0.75% bar. On contrary, private banks and telecom industry showed a total 100% adoption rates.

Astonishingly, public sector banks showed a 50% adoption rate- almost half of the rate in the private sector.

The study revealed more and more companies in India are looking forward to data analytics to boost sales and marketing initiatives. The tools of analytics are largely employed in the sales domain, followed by finance and operations.

Apparently, not much of the results were directly comparable with that of the last year’s study. Interestingly, one metric – analytics penetration rate – was measured last year as well, which is nothing but the ratio of analytics-oriented employees to the total. Also, last year, you would have found one out of 59 employees in an average organization, which has now reached one data analyst for every 36 employees.

For detailed information, read the full blog here: qz.com/india/1482919/banks-telcos-e-commerce-firms-hire-most-data-analysts-in-india

If you are interested in following more such interesting blogs and technology-related updates, follow DexLab Analytics, a premium analytics training institute headquartered in Gurgaon, Delhi. Grab a data analyst certification today and join the bandwagon of success.

 

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How to Build and Maintain Successful Data Science Teams?

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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 from ― www.analyticsindiamag.com/the-art-of-running-successful-data-science-teams

 

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7 Leading Sectors in India That Need an AI & Analytics Makeover

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7 Leading Sectors in India That Need an AI & Analytics Makeover

Advancements in the field of data analytics and artificial intelligence are fuelling innovation in every nation around the world. India too is showing keen interest in AI. This year, the government has doubled the amount invested in the innovation program Digital India which drives advances in machine learning, AI and 3-D printing.

The signs of increased activity in AI research and development are showing in different areas. Here are the topmost sectors of India that are in dire need of AI and data science revolution:

FINANCE

According to reports by PricewaterhouseCoopers, financial bodies and payment regulators deal with billions of dollars in transactions through ATMs, credit cards, e-commerce transactions, etc. When human expertise is combined with advanced analytical methods and machine learning algorithms, fraudulent transactions can be flagged the moment they occur. This leaves less room for human errors. Considering the recent discoveries about major frauds in reputed banks in India, this approach seems more like a necessity.

Image source: American Banker

 

AGRICULTURE

Although 40% of the Indian population works in the agricultural sector, revenues from this sector make up only 16% of the total GDP. The agricultural industry needs advanced data analytics techniques for the prediction of annual, quarterly or monthly yields; analyzing weather reports are observing the best time to sow; estimating the market price of different products so that the most profitable crop can be cultivated, etc. AI powered sensors can measure the temperature and moisture level of soil. With the help of such data farmers can identify the best time to plant and harvest crops and make efficient use of fertilizers.

Image source: Inventiva

HEALTHCARE

According to the Indian constitution, each and every citizen is supposed to get free healthcare. And government hospitals do provide that to people below poverty line. Nonetheless, 81% of the doctors work for private hospitals and nearly 60% hospitals in India are private (According to Wikipedia). The root cause for this is that government hospitals are overpopulated. People who can afford healthcare services from a private hospital prefer to be treated there. Data science can play a pivotal role in managing the growing demand for healthcare services by strengthening the current infrastructure. It can help by predicting how many days a patient is likely to be admitted and find out the proper allotment of beds. AI fine tunes medical predictions and helps selecting a proper line of treatment.

Image source: wxpress

CRIME PREDICTION

Considering the number of security threats and extremist attacks India has faced in the past, there’s urgent need to develop efficient methods that can neutralize such threats and maintain proper law and order. AI and ML can step in to ease the burden of security personnel. A welcome development is the collaboration between Israeli company Cortica and Best Group. Massive amounts of data from CCTV cameras across the nation are being analyzed to anticipate crime and take action before it happens. Streaming data is scrutinized for behavioral anomalies, which are considered as warning signs for a person who commits a violent crime. The aim of the Indian authorities is improving safely in roads, stations, bus stops and other public places.

Image source: Digital Trends

From the paragraphs above it is evident that AI and data analytics has immense scope to improve these major sectors in India. While you look forward to these developments also follow DexLab Analytics, which is a leading data analyst training institute in Delhi. For data analyst certification, get in touch with DexLab’s industry experts.

Reference:

www.brookings.edu/blog/techtank/2018/05/17/artificial-intelligence-and-data-analytics-in-india

www.analyticsvidhya.com/blog/2018/08/top-7-sectors-where-data-science-can-transform-india-with-free-datasets

 

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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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5 Trends Shaping the Future of Data Analytics

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5 Trends Shaping the Future of Data Analytics

Data Analytics is popular. The future of data science and analytics is bright and happening. Terms like ‘artificial intelligence’ and ‘machine learning’ are taking the world by storm.

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020, says Forbes, a leading business magazine.

 

Last year, at the DataHack Summit Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton shared some slivers of knowledge in the illuminating field of data science. He believes that the following trends will shape up the world of data analytics, and we can’t agree more.

Dive down to pore over a definitive list – thank us later!

Internet of Things (IoT)

Does IoT ring any bell? Yes, it does, because it’s nothing but evolved wireless networks. The market of this fascinating new breed of tech is expected to grow from $170.57 billion in 2017 to $561.04 billion by 2022 – reasons being advanced analytics and superior data processing techniques.

Artificial Intelligence

An improved version of AI is Augmented Intelligence – instead of replacing human intelligence, this new sophisticated AI program largely focuses on AI’s assistive characteristic, enhancing human intelligence. The word ‘Augmented’ stands for ‘to improve’ and together it reinforces the idea of amalgamating machine intelligence with human conscience to tackle challenges and form relationships.

Augmented Reality

Look forward to better performances and successful models? Data is the weapon of all battles. Augmented Reality is indeed a reality now. The recent launch of Apple ARkit is a pivotal development in bulk manufacturing of AR apps. The power of AR is now in the fingertips of all iPhone users, and the development of Google’s Tango is an added thrust.

Hyper Personalization

#KnowYourCustomer, it has become an indispensable part of today’s retail marketing; the better you know your customers, the higher are the chances of selling a product. Yes, you heard that right. And Google Home and Amazon Echo is boosting the ongoing operations.

Graph Analytics

Mapping relationships across wide volumes of well connected critical data is the essence of graph analytics. It’s an intricate set of analytics tools used for unlocking insightful questions and delivering more accurate results. A few use cases of graph analytics is as follows:

  • Optimizing airline and logistic routes
  • Extensive life science researches
  • Influencer analysis for social network communities
  • Crime detection, including money laundering

 
Advice: Be at the edge of data accumulation – because data is power, and data analytics is the power-device.

Calling all data enthusiasts… DexLab Analytics offers state of the art data analytics training in Gurgaon within affordable budget. Apply now and grab amazing discounts and offers on data analyst course.

 

The article has been sourced from – yourstory.com/2017/12/data-analytics-future-trends

 

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How To Incorporate Embedded Analytics In Your Products or Applications?

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How To Incorporate Embedded Analytics In Your Products or Applications?

The adept R&D team shares a common responsibility – devising incredible products and solutions.  Right from VPs and directors of development to DevOps and system engineers – every professional is well aware about their customer expectations.

In today’s data-driven industry, customers desire to collect information regarding product use, numerous status updates, number of times they engaged with the product and so on. In short, they need access to data that not only help unravel crucial insights but also makes the product fetching.

Definitely, you can create your very own analytics solution, but what if it takes so much time that your competitors outgo you? So, what next?

Embedding analytics into your product or application can be your thing for the day, but how to do it effortlessly?

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Collect Data from Various Sources

We are surrounded by IoT (Internet of Things) and IIoT (Industrial Internet of Things) revolution, where each connected device and sensor accumulates data and this collection and analysis of data is significant to customers. Teams need to sit together and discuss out options for creating an analytics overlay for the product, which will trigger a million questions – how can we get through it? Will our solution scale the growth of number of users? How can we go on improving our products? How do we keep up with all the developments happening in analytics?

Things to Consider While Embedding Analytics

“Start by looking for specific analytic applications that complement your ERP and BI platform investments. In the long term, review vendor capability to support reusable analytics artifacts (i.e., services) in a service-oriented architecture environment,” – says Gartner.

To this, we’ve listed a few functionalities waiting for your attention:

Data Access – How simple do you want your platform to be so as to integrate your data well across all sources and types?

Visualization – Does the platform you chose comprise widgets you need? If not, can you develop them using customization options?

Modeling – How much easier will it be to code for data preparation for user consumption?

Embeddability (iFrame, JS libraries, JavaScript) – Dashboards should be built in a way to suit your customer’s requirements either in mobiles or in web-based applications.

Extensibility (APIs, SDK, JavaScirpt) – No hard fact, for incorporating analytics workflow, solutions supporting API is the key. Otherwise, not getting extensibility will leave you tied to the same analytics platform and can cost you consulting fees and vendor-developed modifications.

Process integration – Generally, integration takes months – so find a vendor who is capable enough to integrate with your products in a week or two so that you remain focused on the benefits alone.

Security – Judge a vendor based on his security credentials – it’s one of the most crucial considerations to tick off your checklist.

As last thoughts, the consideration of these 7 functionalities is just the beginning of embedding analytics into your products or applications. To sail through, the most important thing to do is to choose a suitable vendor who will grow and start thinking of you as a partner and not just any customer. Let him offer you quick, easy and seamless integration, and you solely focus on your customer needs and preferences, and for this, they will LOVE YOU for sure!

If you are still confused about embedded analytics or related concept, let career-building business analyst training courses in Noida help you! For more information on business analyst training delhi, drop by DexLab Analytics.

 

This blog has been sourced from – https://www.sisense.com/blog/going-embedded-pillar-analytics-success

 

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5 Expected Changes You Are Going to Witness Once You Move to SaaS

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Moving to the cloud takes time. One of our friends started with Salesforce in 2009, after 5 years they introduced G Suite (widely known as Google Apps during that time) and it’s now in 2017 that they have adopted a fully cloud-based electronic health record facility. It took 10 years for an organization to resort to a handful number of installed applications for smooth handling of specialized tasks.  

5 Expected Changes You Are Going to Witness Once You Move to SaaS

Nevertheless, their shift to Software-as-a-Service (SaaS) has had an impact on IT spending. Though the expenditure varies from company to company, every organization must have experienced these 5 changes highlighted below:

 

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Better networks

Unsurprisingly, people need and expect faster internet speed these days. Even small businesses have connections that deliver 250Mbps down and 75Mbps (or more) up. An interesting switch is being observed in infrastructure. Today, more or less any medium-organization boasts of 802.11n or 802.11ac WIFI networks, which was unimaginable even few years ago. Deploying wireless mesh devices has become the order of the day now.

Lesser computer upgrades

There was a time, when we used to think that we have to replace our computers every three or five years. In several cases, we had even planned to make a few upgrades to the hardware to keep them running (RAM and hard-drive replacement was a common thought).

But in reality, organizations seldom have to replace parts. In most offices, five year old desktops perform perfectly in delivering the right results. This means definitely days of upgrades are over, all that matters is a faster internet speed and robust app development.

 

More usage of “plug-in and use” systems

More and more companies are seeking so-called “sealed” systems. Though some big companies still go on deploying standardized drive images, but increasingly organizations are found picking off-the-shelf sealed devices, like all-in-one desktops and non-use-configurable laptops.

 

As organizations are moving towards SaaS, Chromebooks are becoming increasingly famous. In fact, more than 20% of the team mentioned in the beginning of the blog uses a Chromebook as their primary work device.

Longer life for devices

Devices, like desktops and laptops that have embraced SaaS seem to have a longer lifecycle. As SaaS mostly depends on browser and network performance, the need for replacing devices has decreased to a great extent. Systems work totally until the device fails to perform or no longer in a position to receive any updates. Also, with SaaS, crucial data don’t remain solely on the device, hence if a system fails, little seems to be lost.

Considerable attention to the peripherals

Peripherals are intimidating. A large number of conventional desktop units have a scanner, printer and copier devices that are supported by a locally installed Windows software or server. Organizations can easily find alternatives of these devices, but it will take some time and effort as well. Few applications and sectors still suffer from minor or significant glitches, but over time, we hope peripherals and accessories will start showing signs of improvements.

 

What changes have you noticed in cloud computing and storage? How do you think the landscape of IT has changed over the past decade?

 

 

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The article has been sourced from – https://www.techrepublic.com/article/5-changes-companies-will-see-after-moving-to-saas

 

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