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Big Data Analytics for Event Processing

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Courtesy cloud and Internet of Things, big data is gaining prominence and recognition worldwide. Large chunks of data are being stored in robust platforms such as Hadoop. As a result, much-hyped data frameworks are clouted with ML-powered technologies to discover interesting patterns from the given datasets.

Defining Event Processing

In simple terms, event processing is a typical practice of tracking and analyzing a steady stream of data about events to derive relevant insights about the events taking place real time in the real world. However, the process is not as easy as it sounds; transforming the insights and patterns quickly into meaningful actions while hatching operational market data in real time is no mean feat. The whole process is known as ‘fast data approach’ and it works by embedding patterns, which are panned out from previous data analysis into the future transactions that take place real time.

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Employing Analytics and ML Models

In some instances, it is crucial to analyze data that is still in motion. For that, the predictions must be proactive and must be determined in real-time. Random forests, logistic regression, k-means clustering and linear regression are some of the most common machine learning techniques used for prediction needs. Below, we’ve enlisted the analytical purposes for which the organizations are levering the power of predictive analytics:

Developing the Model – The companies ask the data scientists to construct a comprehensive predictive model and in the process can use different types of ML algorithms along with different approaches to fulfill the purpose.

Validating the Model – It is important to validate a model to check if it is working in the desired manner. At times, coordinating with new data inputs can give a tough time to the data scientists. After validation, the model has to further meet the improvement standards to deploy real-time event processing.

Top 4 Frameworks for ML in Event Processing

Apache Spark

Ideal for batch and streaming data, Apache Spark is an open-source parallel processing framework. It is simple, easy to use and is ideal for machine learning as it supports cluster-computing framework.

Hadoop

If you are looking for an open-source batch processing framework then Hadoop is the best you can get. It not only supports distributed processing of large scale data sets across different clusters of computers with a single programming model but also boasts of an incredibly versatile library.

Apache Storm

Apache Storm is a cutting edge open source, big data processing framework that supports real-time as well as distributed stream processing. It makes it fairly easy to steadily process unbounded streams of data working on real-time.

IBM Infosphere Streams

IBM Infosphere Streams is a highly-functional platform that facilitates the development and execution of applications that channels information in data streams. It also boosts the process of data analysis and improves the overall speed of business decision-making and insight drawing.

If you are interested in reading more such blogs, you must follow us at DexLab Analytics. We are the most reputed big data training center in Delhi NCR. In case, if you have any query regarding big data or Machine Learning using Python, feel free to reach us anytime.

 

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Big Data and Its Use in the Supply Chain

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Big Data and Its Use in the Supply Chain

Data is indispensable, especially for modern business houses. Every day, more and more businesses are embracing digital technology and producing massive piles of data within their supply chain networks. But of course, data without the proper tools is useless; the emergence of big data revolution has made it essential for business honchos to invest in robust technologies that facilitate big data analytics, and for good reasons.

Quality Vs Quantity

The overwhelming volumes of data exceed the ability to analyze that data in a majority of organizations. This is why many supply chains find it difficult to gather and make sense of the voluptuous amount of information available across multiple sources, processes and siloed systems. As a result, they struggle with reduced visibility into the processes and enhanced exposure to cost disruptions and risk.

To tackle such a situation, supply chains need to adopt comprehensive advanced analytics, employing cognitive technologies, which ensure improved visibility throughout their enterprises. An initiative like this will win these enterprises a competitive edge over those who don’t.

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Predictive Analytics

 A striking combination of AI, location intelligence and machine learning is wreaking havoc in the data analytics industry. It is helping organizations collect, store and analyze huge volumes of data and run cutting edge analytics programs. One of the finest examples is found in drone imagery across seagrass sites.

Thanks to predictive analytics and spatial analysis, professionals can now realize their expected revenue goals and costs from a retail location that is yet to come up. Subject to their business objectives, consultants can even observe and compare numerous potential retail sites, decrypting their expected sales and ascertain the best possible location. Also, location intelligence helps evaluate data, regarding demographics, proximity to other identical stores, traffic patterns and more, and determine the best location of the new proposed site.

The Future of Supply Chain

Talking from a logistic point of view, AI tools are phenomenal – IoT sensors are being ingested with raw data with their aid and then these sensors are combined with location intelligence to formulate new types of services that actually help meet increasing customer demands and expectations. To prove this, we have a whip-smart AI program, which can easily pinpoint the impassable roads by using hundreds and thousands of GPS points traceable from an organization’s pool of delivery vans. As soon as this data is updated, route planners along with the drivers can definitely avoid the immoderate missteps leading to better efficiency and performance of the company.

Moreover, many logistics companies are today better equipped to develop interesting 3D Models highlighting their assets and operations to run better simulations and carry a 360-degree analysis. These kinds of models are of high importance in the domain of supply chains. After all, it is here that you have to deal with the intricate interplay of processes and assets.

Conclusion

 Since the advent of digital transformation, organizations face the growing urge to derive even more from their big data. As a result, they end up investing more on advanced analytics, local intelligence and AI across several supply chain verticals. They make such strategic investments to deliver efficient service across the supply chains, triggering higher productivity and better customer experience.

With a big data training center in Delhi NCR, DexLab Analytics is a premier institution specializing in in-demand skill training courses. Their industry-relevant big data courses are perfect for data enthusiasts.

 
The blog has been sourced from ―  www.forbes.com/sites/yasamankazemi/2019/01/29/ai-big-data-advanced-analytics-in-the-supply-chain/#73294afd244f
 

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The Soaring Importance of Apache Spark in Machine Learning: Explained Here

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The Soaring Importance of Apache Spark in Machine Learning: Explained Here

Apache Spark has become an essential part of operations of big technology firms, like Yahoo, Facebook, Amazon and eBay. This is mainly owing to the lightning speed offered by Apache Spark – it is the speediest engine for big data activities. The reason behind this speed: Rather than a disk, it operates on memory (RAM). Hence, data processing in Spark is even faster than in Hadoop.

The main purpose of Apache Spark is offering an integrated platform for big data processes. It also offers robust APIs in Python, Java, R and Scala. Additionally, integration with Hadoop ecosystem is very convenient.

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Why Apache Spark for ML applications?

Many machine learning processes involve heavy computation. Distributing such processes through Apache Spark is the fastest, simplest and most efficient approach. For the needs of industrial applications, a powerful engine capable of processing data in real time, performing in batch mode and in-memory processing is vital. With Apache Spark, real-time streaming, graph processing, interactive processing and batch processing are possible through a speedy and simple interface. This is why Spark is so popular in ML applications.

Apache Spark Use Cases:

Below are some noteworthy applications of Apache Spark engine across different fields:

Entertainment: In the gaming industry, Apache Spark is used to discover patterns from the firehose of real-time gaming information and come up with swift responses in no time. Jobs like targeted advertising, player retention and auto-adjustment of complexity levels can be deployed to Spark engine.

E-commerce: In the ecommerce sector, providing recommendations in tandem with fresh trends and demands is crucial. This can be achieved because real-time data is relayed to streaming clustering algorithms such as k-means, the results from which are further merged with various unstructured data sources, like customer feedback. ML algorithms with the aid of Apache Spark process the immeasurable chunk of interactions happening between users and an e-com platform, which are expressed via complex graphs.

Finance: In finance, Apache Spark is very helpful in detecting fraud or intrusion and for authentication. When used with ML, it can study business expenses of individuals and frame suggestions the bank must give to expose customers to new products and avenues. Moreover, financial problems are indentified fast and accurately.  PayPal incorporates ML techniques like neural networks to spot unethical or fraud transactions.

Healthcare: Apache Spark is used to analyze medical history of patients and determine who is prone to which ailment in future. Moreover, to bring down processing time, Spark is applied in genomic data sequencing too.

Media: Several websites use Apache Spark together with MongoDB for better video recommendations to users, which is generated from their historical data.

ML and Apache Spark:

Many enterprises have been working with Apache Spark and ML algorithms for improved results. Yahoo, for example, uses Apache Spark along with ML algorithms to collect innovative topics than can enhance user interest. If only ML is used for this purpose, over 20, 000 lines of code in C or C++ will be needed, but with Apache Spark, the programming code is snipped at 150 lines! Another example is Netflix where Apache Spark is used for real-time streaming, providing better video recommendations to users. Streaming technology is dependent on event data, and Apache Spark ML facilities greatly improve the efficiency of video recommendations.

Spark has a separate library labelled MLib for machine learning, which includes algorithms for classification, collaborative filtering, clustering, dimensionality reduction, etc. Classification is basically sorting things into relevant categories. For example in mails, classification is done on the basis of inbox, draft, sent and so on. Many websites suggest products to users depending on their past purchases – this is collaborative filtering. Other applications offered by Apache Spark Mlib are sentiment analysis and customer segmentation.

Conclusion:

Apache Spark is a highly powerful API for machine learning applications. Its aim is wide-scale popularity of big data processing and making machine learning practical and approachable. Challenging tasks like processing massive volumes of data, both real-time and archived, are simplified through Apache Spark. Any kind of streaming and predictive analytics solution benefits hugely from its use.

If this article has piqued your interest in Apache Spark, take the next step right away and join Apache Spark training in Delhi. DexLab Analytics offers one the best Apache Spark certification in Gurgaon – experienced industry professionals train you dedicatedly, so you master this leading technology and make remarkable progress in your line of work.

 

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Top 3 Reasons to Learn Scala Programming Language RN

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Top 3 Reasons to Learn Scala Programming Language RN

Highly scalable general-purpose programming language, Scala is a wonder tool for newbie developers as well as seasoned professionals. It provides both object-oriented and functional programming assistance. The key to such wide level popularity of Scala lies in the sudden explosive growth of Apache Spark, which is actually written in Scala – thus making later a powerful programming language best suited for machine learning, data processing and streaming analytics.

Below, we have enumerated top three reasons why you should learn Scala and tame the tides of success:

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For Better Coding

The best part is that you would be able to leverage varied functional programming techniques that will stabilize your applications and mitigate challenges that might arise due to unforeseen side effects. Just by shifting from mutable data structures to immutable ones, and from traditional methods to purely functional strategies that have zero effect on their environment, you can stay rest assured that your codes would be more stable, safer and easy to comprehend.

Inarguably, your code would be simple and expressive. If you are already working on languages, such as JavaScript, Python or Ruby, you already know about the power of a simple, short and expressive syntax. Hence, use Scala to shed unnecessary punctuations, explicit types and boilerplate code.

What’s more, your code would support multiple inheritance and myriad capabilities, and would be strongly-typed. Also, in case of any incompatibilities, it would be soon caught even before the code runs. So, developers in both dynamic and statically typed languages should embrace Scala programming language – it ensures safety with performance along with staying as expressive as possible.

To Become a Better Engineer

He who can write short but expressive codes as well as ensure a type-safe and robust-performance application is the man for us! This breed of engineers and developers are considered immensely valuable, they impress us to the core. We suggest take up advanced Scala classes in Delhi NCR and take full advantage of its high-grade functional abilities. Not just learn how to deliver expressive codes but also be productive for your organization and yourself than ever before.

Mastering a new programming language or upgrading skills is always appreciable. And, when it comes to learning a new language, we can’t stop recommending Scala – it will not only shape your viewpoint regarding concepts, like data mutability, higher-order functions and their potential side effects, but also will brush your coding and designing skills.

It Enhances Your Code Proficiency

It’s true, Scala specialization improves your coding abilities by helping you read better, debug better and run codes pretty faster. All this even makes you write codes in no time – thus making you proficient, and happy.

Now, that you are into all-things-coding, it’s imperative to make it interesting and fun. Scala fits the bill perfectly. If you are still wondering whether to imbibe the new-age skill, take a look at our itinerary on advanced Scala Training in Delhi displayed on the website and decide for yourself. The world of data science is evolving at a steadfast rate, and it’s high time you learn this powerful productive language to be on the edge.

 

The blog has been sourced from – www.oreilly.com/ideas/3-simple-reasons-why-you-need-to-learn-scala

 

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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.

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 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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Advancement in Genomics with Artificial Intelligence

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Advancement in Genomics with Artificial Intelligence

Artificial Intelligence is raging hot, and the healthcare industry is not left behind. Reports suggest AI will help the healthcare industry generate $6.7 billion in revenue. In healthcare, genomics is one of the most notable areas that have evolved significantly after the rise of AI. Involving processes like gene editing and sequencing, genomics is largely performed in agriculture, customized medicine industry and animal husbandry.

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Researchers have long been conducting DNA analysis. However, their initiatives used to be stalled midway because of several challenges – such as the massive size of the genome, high cost, regulatory factors, prediction norms and technology limitations. On top of that, a vast amount of data on genes and genomes further added up to the problem of ostentatiously large amount of patient data. 

Fortunately, today, researchers are better off using machine learning for genomics – they can now perform gene synthesis, construct precision and personalized medicines and understand the genetic makeup of each orgasm amongst others.

Major Development Highlights

  • Elevation Project by Microsoft grabbed eyeballs when its researchers collaborated with a set of biologists from UC Berkeley to assist in gene editing using AI. They decided to combine their efforts and increase efficiency and accuracy of CRISPR technology – which is basically a gene-editing tool for resulting in genetic improvements.

Together, they also launched Elevation, which uses Machine Learning technology to forecast effectively the off-target effects that take place during the process, thus increasing the efficiency of the entire process.

  • Nvidia and Scripps Research Translational Institute (SRTI) improvised their operations for developing deep learning tools and methods. They aim to process and analyze genomic and digital medical sensor data that would increase the use of AI and prevent the spread of diseases, promote health and streamline a host of biomedical research measures.
  • Google released DeepVariant – it is a cutting-edge deep learning model designed to analyze genetic succession. Last year, they devised a new version DeepVariant v0.6, which features brand new accuracy developments that helps get a more accurate picture of an entire genome.
  • Deep Genomics, a budding startup in Canada is found leveraging artificial intelligence to decipher genome and ascertain the most suitable drug therapies based on DNA found on the cell. The company specializes in the field of personalized medicines.

Genomics in India

Following the footsteps of its global partners, India too is slowly maneuvering into the space of AI-powered genomics – several startups, like Artivatic Data Labs are building power in this new field with radical innovations. Another Chennai-based startup, Orbuculum is leveraging AI to predict debilitating diseases and optimize disease diagnosis.

End Note

Major breakthroughs are happening in the new world of genomics. But, of course, understanding human genome and developing genomic medicines is beyond the human capabilities. Often, it needs analysis of millions and millions of data and performs several repetitive tasks for which AI seem to be the most feasible solution. Undeniably, advancements in AI and ML technology have resulted in a comprehensive understanding of genomics – they are the best way to interpret and proceed on genomic data.

FYI: DexLab Analytics is a top-notch artificial intelligence training institute in Gurgaon. It offers excellent in-demand skill training for students, professionals and anyone who is interested in data.

 

The blog has been sourced from ― www.analyticsindiamag.com/when-artificial-intelligence-meets-genomics

 

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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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