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7 famous apps using reactjs nowadays
7 Famous Apps Using Reactjs Nowadays

Front-end development is continuously increasing by adding new tools released daily. There are several libraries and frameworks available online and choosing one from them is quite difficult. Talking about front-end development, Angular was the default choice for any business owners but the time has changed, React JS is breaking records in the web development market.

Facebook 

Facebook uses React Native as it is a cross-platform mobile app development platform that develops its own mobile app. There are around 990 million Facebook users daily as this social channel helps us stay connected with friends and family. Facebook is built on React Native version and responsible for displaying the iOS and Android native components. ReactJS library was firstly placed on Facebook when the beta version was created. It was completely rewritten in React Native and called React Fiber.
   

Instagram

ReactJs has played a vast role in delivering a digital experience to the user connected with Instagram. The app gives an amazing look and feels in terms of UI and UX. Moving an existing app to new technology is a big challenge for Instagram, but React Native has comparatively played well. The major change was made in the effect of the app and was easy to maintain for both Android and iOS platforms.
  

Netflix

Today, when you’re enjoying great UI and Ux, it is due to React Native. React Native was also added with Netflix when it was facing low performance on various devices. Netflix has initially published the blog by explaining how the ReactJS library has helped overcome the difficulties and head to speed, starting from improving runtime performance, modularity, and various other advantages.
    

New York Times

Coming with a new design and a great project, the New York Times has given a great move with React Native. The New project adds a great look and feels to the content implemented to it. Looking at the interface we can say that it is built by React Native as there are impressive features added to it
   

WhatsApp

Talking about daily using social platform Whatsapp has officially released ReactJS for building user interface from Facebook. It uses some of the most efficient engines such as Velocity.Js and Underscore.js to give better results. Currently, Whatapp is using React to give a better experience to the users.

Myntra

Myntra is one of the leading Indian fashion e-Commerce Companies from where one can shop for clothing, home furnishing, footwear, and other accessories for men, women, and kids. The perfect look and feel to the finest user experience you get are with the help of the ReactJS mobile app. React Native has given a beautiful presentation of the profiles, catalogs and order placement convenience to the user in a better way. ReactJs Development Services has offered an amazing UI and UX to all Android and iOS users.

Discord

You all must have a heart for free voice and chat app Discord for gamers. The game enables chatting between the team, allows checking the availability, and catch up on text conversations. Using React JS, 98% of the code on iOS and Android were shared which is the best example of using Cross-platform app development.

Conclusion:

Anticipate in this competitive market and craft goals by demonstrating how your business will use this technology and accelerate growth. Raise conversions, cut costs, and brand your business with ReactJS development. We are one of the best ReactJS Development companies that use React JS to ensure better performance than other frameworks. You can hire ReactJS developers to understand the technology and lever the business for a competitive advantage.

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what movies can teach us about prospering in an ai world part 1
What Movies Can Teach Us About Prospering in an AI World – Part 1

In his book “Outliers”, Malcom Gladwell unveils the “10,000-Hour Rule” which postulates that the key to achieving world-class mastery of a skill is a matter of 10,000 hours of practice or learning. And while there may be disagreement on the actual number of hours (though I did hear my basketball coaches yell that at me about 10,000 times), let’s say that we can accept that it requires roughly 10,000 hours of practice and learning – exploring, trying, failing, learning, exploring again, trying again, failing again, learning again – for one to master a skill.

If that is truly the case, then dang, us humans are doomed.

10,000 hours of learning is a rounding error for some of today’s AI models. Think about 1,000,000 Tesla cars with its Fully Self Driving (FSD) autonomous driving module “practicing and learning” every hour that it is driving.  In a single hour of the day, Tesla’s FSD driving module is learning 100x more than what Malcom Gladwell postulates is necessary to master a task.  And over a year, the Tesla FSD module is going to have amassed 8.69 billion hours of learning – 869,000 times more hours than Gladwell postulated was needed to master a skill!

AI models are the masters of learning. Or as Matthew Broderick yells at the WOPR AI war simulation module in the movie “Wargames”: Learn, goddamn it!  (See Figure 1).

Figure 1:Learn, goddamn it!

AI models have numerous ways in which it can learn.  Here are just a few of them:

Machine Learning learns by using algorithms to analyze and draw inferences from patterns in data, correlating patterns between inputs and outcomes. There are two categories of Machine Learning:

  • Supervised Machine Learning uses labeled datasets (known outcomes) to train algorithms that can predict expected outcomes. As labeled input data is fed into the model, the model adjusts its weights across the model variables until the model has been fitted appropriately using an optimization routine to minimize the loss or error function. Regression modeling is a common Supervised Machine Learning algorithm.
  • Unsupervised Machine Learning learns trends, patterns, and relationships from unlabeled data (unknown outcomes). Unsupervised Machine Learning algorithms discover trends, patterns, and relationships buried in the data. Clustering is a common Unsupervised Machine Learning algorithm.

Automated Machine Learning, or AutoML, eliminates the need for skilled data scientists to analyze and test the multitude of different machine learning algorithms by automagically applying all of them to a data set to see which ones are most effective. AutoML can also optimize the machine learning hyperparameters of the best models to train an even better model (see Figure 2).

Figure 2: Source: Microsoft: “What is automated machine learning (AutoML)?

Deep Learning uses neural networks to imitate the workings of the human brain in processing data and identifying patterns in unstructured data sets (audio, images, text, speech, video, waves).  Deep learning learns to classify patterns and relationships using extremely large training data sets (Big Data) and a deep hierarchy of layers, with each layer solving different pieces of a complex problem.

Reinforcement Learning uses intelligent agents to take actions in a known environment to maximize cumulative reward. Reinforcement Learning learns by replaying a certain situation (a specific game, vacuuming the house, driving a car) millions or billions (using simulators) of times.  The program is rewarded when it makes a good decision and given no reward (or punished) when it loses or makes a bad decision.  This system of rewards and punishments strengthens the connections to eventually make the “right” moves without programmers explicitly programming the rules into the game.

Active Learning is a special type of machine learning algorithm that leverages human subject matter experts to assist in labeling the input data. Since the key to an effective machine learning’s model is access to labeled data, Active Learning prioritizes the inputs that it cannot decipher so that human experts can help (see Figure 3).

Figure 3: Human Subject Matter Expert to distinguish a “4” from a “9”

Transfer Learning is a technique whereby a neural network is first trained on one type of problem and then the neural network model is reapplied to another, similar problem with only minimal training. Transfer learning re-applies the Neural Network knowledge (weights and biases) gained while solving one problem to a different but related problem with minimal re-training. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks or tanks or trains.

Federated Learning trains an algorithm across multiple decentralized remote or edge devices using local data samples. All the training data remains on your remote device.  Federated Learning works like this: the remote device downloads the current analytic model, improves it by learning from data on the remote or edge device, and then summarizes the changes as a small, focused update that is sent to the cloud where it is aggregated with other updates to improve the analytic model.

Meta-learning is teaching machines to “learn how to learn” by designing algorithmic models that can learn new skills or adapt to new environments without requiring massive test data sets. There are three common Meta-learning approaches: 1) learn an efficient distance metric (metric-based); 2) use a (recurrent) neural network with external or internal memory (model-based); 3) optimize the model parameters explicitly for fast learning (optimization-based)

Generative Adversarial Networks (GANs) are deep neural net architectures comprised of two neural nets – a Generator and a Discriminator – that are pitted one against the other to accelerate the training of the deep learning models.  The Generator neural network manufactures new data based upon an understanding of the current data set, and the Discriminator neural network tries to discriminate real data from manufactured data. This accelerates the training of deep learning models by providing even more data against which to train the deep learning models.

Given how rapidly AI / ML models can learn (think accelerated learning that quickly builds upon itself with minimal human oversight that can quickly spiral out-of-control), the real AI challenge to humanity is this:

You give AI a goal and the way that AI achieves that goal turns out to be at odds with what you really intended

It can be dangerous when goals don’t align, and while every organization knows that’s a given, that misalignment of goals could become catastrophic when you engage an engine that is continuously learning and adapting a billion times faster speed than us humans.

So, are us humans really doomed?  To learn more, you’ll have to wait for Part 2 of this series (and hint: Tom Hanks to the rescue!).

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ethical ai responsible ai best practices
Ethical AI – Responsible AI best practices

 

Ethical AI and Responsible AI are becoming increasingly important for two main reasons

Firstly, it is good customer best practice and also governments(especially in the EU and USA) are regulating in this space and compliance is critical

However, it is not easy to get a best practice/ independent view on ethical AI

Hence, the free and open source best practice guide under creative commons from the foundation for best practices in machine learning could be useful as an overall checklist. The report also includes a terminology / definitions for ethical and responsible AI which I find useful

The foundation probably makes its income from consulting (but is a non profit). Interestingly, it does not sell certification which is good ie they do not believe in commodifying ethical and responsible machine learning.

They also emphasise context

“Context is probably one of the most important aspects of ethical and responsible machine learning. This is because, despite it being talked about as an independent phenomena, machine learning is – arguably – an augmenting technology. It augments the process and/or operations it is applied in. This means it is a tool (means), as opposed to an end-product (ends). Given this, the context of any machine learning operation is very important in understanding how best and responsibly this technology can be used and what its particular risks might be.”

Themes covered

 Managerial Oversight & Management

 Internal Organisation 

Management & Oversight

Data Governance

Product and Model Oversight & Management

Product Validation

Human Resources Management

Asset Management

Software Management

Incident Management

Third Party Contracts Management

Ethics & Transparency Management

Compliance, Auditing & Legal Management and Oversight

Definitions and terminology for ethical AI and responsible AI (from the best practice wiki)

  1. Absolute Reproducibility means a guarantee that any and all results, outputs, outcomes, artifacts, etc can be exactly reproduced under any circumstances.
  2. Adversarial Action means actions characterised by mala fide (malicious) intent and/or bad faith.
  3. Assessment means the action or process of making a series of determinations and judgments after taking deliberate steps to test, measure and collectively deliberate the objects of concern and their outcomes.
  4. Assets means information technology hardware that concerns Products Machine Learning.
  5. Best Practice Guideline means this document.
  6. Business Stakeholders means the departments and/or teams within the Organisation who do not conduct data science and/or technical Machine Learning, but have a material interest in Products Machine Learning.
  7. Confidence Value means a measure of a Model’s self-reported certainty that the given Output is correct.
  8. Corporate Governance Principles mean the structure of rules, practices and processes used to direct and manage a company in terms of industry recognised and published legal guidelines.
  9. Data Generating Process means the process, through physical and digital means, by which Records of data are created (usually representing events, objects or persons).
  10. Data Governance means the systems of governance and/or management over data assets and/or processes within an Organisation.
  11. Data Quality means the calibre of qualitative or quantitative data.
  12. Data Science means an interdisciplinary field that uses scientific methods, processes, algorithms and computational systems to extract knowledge and insights from structured and/or unstructured data.
  13. Domain means the societal and/or commercial environment within which the Product will be and/or is operationalised.
  14. Edge Case means an outlier in the space of both input Features and Model Outputs.
  15. Error Rate means the frequency of occurrence of errors in the (Sub)population relative to the size of the (Sub)population
  16. Ethical Practices means the ethical principles, values and/or practices that are encapsulated and promoted in an ‘artificial intelligence’ ethics guideline and/or framework, such as (a) The Asilomar AI Principles (Asilomar AI Principles, 2017), (b) The Montreal Declaration for Responsible AI (Montreal Declaration, 2017), (c) The Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems (IEEE, 2017), and/or (d) any other analogous guideline and/or framework.
  17. Ethics Committee means the committee within the Organisation charged with managing and/or directing organisation Ethical Practices.
  18. Evaluation Error means the difference between the ground truth and a Model’s prediction or output.
  19. Executive Management means the managerial team at the highest level of management within the Organisation.
  20. Explainability means the property of Models and Model outcomes to be interpreted and/or explained by humans in a comprehensible manner.
  21. Fairness & Non-Discrimination means the property of Models and Model outcomes to be free from bias against protected classes.
  22. Features mean the different attributes of datapoints as recorded in the data.
  23. Guide means an established and clearly documented series of actions or process(es) conducted in a certain order or manner to achieve particular outcomes.
  24. Hidden Variable means an attribute of a datapoint or an attribute of a system that has a causal relation to other attributes, but is itself not measured or unmeasurable.
  25. Human-Centric Design & Redress means orienting Products and/or Models to focus on humans and their environments through promoting human and/or environment centric values and resources for redress.
  26. Implementation means every aspect of the Product and Model(s) insertion of and/or application to Organisation systems, infrastructure, processes and culture and Domains and Society.
  27. Incident means the occurrence of a technical event that affects the integrity of a Product and/or Model
  28. Label means the Feature that represents the (supposed) ground-truth values corresponding to the Target Variable.
  29. Machine Learning means the use and development of computer systems and Models that are able to learn and adapt with minimal explicit human instructions by using algorithms and statistical modelling to analyse, draw inferences, and derive outputs from data.
  30. Model means Machine Learning algorithms and data processing designed, developed, trained and implemented to achieve set outputs, inclusive of datasets used for said purposes unless otherwise stated.
  31. Organisation means the concerned juristic entity designing, developing and/or implementing Machine Learning.
  32. Outcome means the resultant effect of applying Models and/or Products.
  33. Output means that which Models produce, typically (but not exclusively) predictions or decisions.
  34. Performance Robustness means the propensity of Products and/or Models to retain their desired performance over diverse and wide operational conditions.
  35. Policy means a documented course of normative actions or set of principles adopted to achieve a particular outcome.
  36. Procedure means an established and defined series of actions or process(es) conducted in a certain order or manner to achieve a particular outcome.
  37. Product means the collective and broad process of design, development, implementation and operationalisation of Models, and associated processes, to execute and achieve Product Definitions, inclusive of, inter alia, the integration of such operations and/or Models into organisation products, software and/or systems.
  38. Product Lifecycle means the collective phases of Products from initiation to termination – such as design, exploration, experimentation, development, implementation, operationalisation, and decommissioning – and their mutual iterations.
  39. Product Manager means either a Design Owner and/or Run Owner as identified in the Organisation Best Practice Guideline in Sections 3.1.4. & 3.1.7. respectively.
  40. Product Owner means the employee charged with (a) managing and maximising the value of the Product and its Product Team; and (b) engaging with various Business Stakeholders concerning the Product and its Product Definitions.
  41. Product Subjects means the entities and/or objects that are represented as data points in datasets and/or Models, and who may be the subject of Product and/or Model outcomes.
  42. Product Team means the collective group of Organisation employees directly charged with designing, developing and/or implementing the Product.
  43. Project Lifecycle means the collective phases of Products from initiation to termination – such as design, exploration, experimentation, development, implementation, operationalisation, and decommissioning – and their mutual iterations.
  44. Protected Classes mean (Sub)populations of Product Subjects, typically persons, that are protected by law, regulation, policy or based on Product Definition(s)
  45. Public means society at large.
  46. Public Interest means the welfare or well-being of the Public.
  47. Representativeness means the degree to which datasets and Models reflect the true distribution and conditions of Subjects, Subject populations, and/or Domains.
  48. Root Cause Analysis means the activity and/or report of the investigation into the primary causal reasons for the existence of some behaviour (usually an error or deviation).
  49. Safety means real Product Domain based physical harms that result through Products and/or Models applications.
  50. Security means the resilience of Products and/or Models against malicious and/or negligent activities that result in Organisational loss of control over concerned Products and/or Models.
  51. Selection Function means a (where possible mathematical) description of the probability or proportion of all real Subjects that might potentially be recorded in the dataset that are actually recorded in a dataset.
  52. Social Corporate Responsibilities means the structure of rules, practices and processes used to direct and manage a company in terms of industry recognised and published legal guidelines to positively contribute to economic, environmental and social progress.
  53. Software means information technology software that concerns Products Machine Learning.
  54. Special Interest Groups means a specific body politic, or a particular collective of citizens, who can reasonably be determined to have a material interest in the Product.
  55. Specification means the accuracy, completeness and exactness of Products, Models and/or datasets in reflecting Product Definitions, Product Domains and/or Product Subjects, either in their design and development and/or operationalisation.
  56. Stakeholders mean the department(s) and/or team(s) within the Organisation who do not conduct data science and/or technical Machine Learning, but have a material interest in Product Machine Learning.
  57. Subjects means the entities and/or objects that are represented as data points in datasets and/or Models, and who may be the subject of Product and/or Model outcomes.
  58. (Sub)population means any group of persons, animals, or any other entities represented by a piece of data , that is part of a larger (potential) dataset and characterized by any (combination of) attributes. The importance of (Sub)populations is particularly high when some (Sub)populations are vulnerable or protected (Protected Classes).
  59. Systemic Stability means the stability of Organisation, Domain, society and environments as a collective ecosystem.
  60. Target Variable means the Variable which a Model is made to predict and/or output.
  61. Target of Interest means the fundamental concept that the Product is truly interested in when all is said and done, even if it is something that is not (objectively) measureable.
  62. Traceability means the ability to trace, recount, and reproduce Product outcomes, reports, intermediate products, and other artifacts, inclusive of Models, datasets and codebases.
  63. Transparency means the provision of an informed target audiences understanding of Organisation and/or Products Machine Learning, and their workings, based on documented Organisation information.
  64. Variables mean the different attributes of subjects or systems which may or may not be measured.
  65. Workflows means the coordinated and standardised sequences of employee work activities, processes, and tasks.

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an overview of quadrat sampling
An overview of Quadrat Sampling

                                                     Image source: Statistical Aid

Quadrat sampling is a classic tool for the study of ecology, especially biodiversity. It is an important method by which organisms in a certain proportion (sample) of the habitat are counted directly.  It is used to estimate population abundance (number), density, frequency and distributions. The quadrat method has been widely used in plant studies. A quadrat is a four-sided figure which delimits the boundaries of a sample plot. The term quadrat is used more widely to include circular plots and other shapes.

Quadrat sampling methods are time-tested sampling techniques that are best suited for coastal areas where access to a habitat is relatively easy.

Assumptions of quadratic sampling

The quadrat sampling method has the following assumptions,

  • The number of individuals in each quadrat is counted.
  • The size of the quadrats is known.
  • The quadrat samples are representative of the study area as a whole.

Advantages

Some advantages are given below-

  • It sampling is easy to use, inexpensive.
  • It is suitable for studying plants, slow-moving animals and faster-moving animals with a small range.
  • It requires the researcher to perform the work in the field and, without care.
  • It measures abundance and needed cheap equipment.

Disadvantages

Some disadvantages are given below-

Quadrat sampling is not useful for studying very fast-moving animals which are not stay within the quadrat boundaries.

  • There exists biasness in favor of slow moving taxa.
  • Collect only taxa that are present in the sampling time and not buried too deeper in sediment.
  • It is a low estimate of taxonomic richness and assemblage composition.
  • It is also a low detectability of among-site differences in assemblage composition.
  • Some animals may experience harm if the scientist collects the population within the quadrat rather than studying it in the field.

Source

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how data science is beneficial for your digital marketing strategy
How Data Science is Beneficial for your Digital Marketing Strategy?

Data Science refers to a technique that deals with vast volumes of data to extract knowledge and valuable inputs using various scientific systems and algorithms. With the dawn of this interdisciplinary field in this modern world, data can now be sophistically structured and utilized on various application domains.

The authority of Data science has emerged significantly in the last few years, allowing oneself to manage customer interaction and better understand the target audience.

The presence of Data science is not less than a boon for digital marketers. The vast amount of information Data science offers is critical for identifying your audience behavior and interests, which in turn help you modify your marketing campaigns. Hence, thinking about Digital marketing without Data science would be a grave mistake in the present and future scenario.

Top benefits of Data Science in Digital marketing  

Here is a list of a few reasons why merging Data science with your Digital marketing strategies can reap fruitful results.

#1 Efficient campaign planning

The data you possess on your social media channels and website can be analyzed precisely using Data science. This data can give you detailed insights about your audience such as when, where, and how they interact with your brand.

This, in turn, enables you to plan and implement your marketing campaign, as per your business requirements, customer behavior, and the data extracted, paying you with higher sales number in return.

#2. Plan optimized budget

With Data science, you can effectively compare your present campaigns’ performance with the previous ones, and analyze the users’ behavior on various channels. While your performance will vary with different platforms, you will be able to test which campaign performed the best and can drive better results at a certain point in time.

This data analysis will help you allocate your budget effectively to various channels and boost your customer acquisition rates, exceeding the expectations of your target audience.

#3 Enhance customer experience

As already mentioned, Data science helps to identify customer behavior, which can help you better tailor your marketing campaigns and implement them accordingly. This results in generating a high-quality consumer experience and satisfying their needs.

Moreover, collect the information to strategize a better-personalized relation with your customers, making them feel exceptional when they are about to make a purchase.

Having a pleasing customer base is the need of the hour for any business. With Data Science, you can gather information about your audience and develop effective marketing guidelines, which you can implement keeping tomorrow in mind.

#4 Improve campaign’s performance

Better optimizing the channels, dealing with your users’ reviews, and tailoring marketing content forms an integral part of any campaign’s journey.

As proper data analysis helps to dissect the vast online audience based on their demographics, buying history, and interests, managing various marketing platforms has become a piece of cake. Also, you can modify and better optimize your social media and website content as per the latest search result algorithms.

Thus, with Data Science, you can give a big boost to your campaign’s performance and help yourself gain new customers or retain existing ones.

 #5 Real-time data

Usually, a marketer tends to collect information about customers after a campaign has been executed to measure its progress. However, with Data science, this strategy has been reversed.

Data science helps to collect real-time data, which is based upon the current market trends and consumer’s purchasing pattern, rather than analyzing the performance of previous marketing campaigns. The real-time data collected can further be optimized to plan your current and future marketing strategies.

Moreover, this can enable you to foresee future opportunities and give your propel business to stay ahead of your competitors. 

#Better product development

With Data science, one can align the right product with the right audience at the right time. You can gather valuable insights from your customer data and perform cluster analysis to check your audience is willing to buy from your current stock and at what price.

Moreover, you can learn more about your competitor’s target audience and look at what interests them the most. This allows you to develop new products and widen your customer base dramatically.


Final words

As digital marketing continues to prosper with an increase in digitization, the relevance of Data science will also continue to evolve. To always have an edge ahead of your competitors and avail of the benefits mentioned above, integration of Data science with Digital marketing will remain a critical factor in the years to come.

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why is there a shortage of data scientists
Why Is There a Shortage of Data Scientists?

Introduction

Data science is driving the industry crazy. It is trending everywhere. Everyone is talking about data science. Whether it’s data science in the industry or data science as a career. Over time, it has become like a superhero! Along with this, we all have frequently heard that data science is one of the most lucrative career options. Do you ever wonder why the companies are offering such a high amount of salaries to the data scientists? 

The answer to this question is very simple. We value those things more which are less available. The case of data scientists is also the same. The salaries of data scientists are skyrocketing because there is a shortage of data scientists in the industry. As per the McKinsey report, the United States is facing a shortage of approximately 140,000 data scientists.  

Let’s understand why there is a shortage of data scientists and what do companies look for in them.

WHY IS THERE A SHORTAGE? 

The major reason why there is a shortage of data scientists in the industry is lack of skills. A person is not valued by its percentages and degrees, but by his skills. Data scientists are highly skilled persons who are supposed to possess technical skills as well as non-technical skills. 

But the companies are not able to find the required data science skills in the data science aspirants. That’s why there is a huge shortage of data scientists in the industry. 

The other major problem that beginners are facing is that companies are demanding a master’s degree with some years of experience.  This is a major issue for them. Being a beginner, they have no experience in the domain of data science and the companies are demanding experience because it’s required for the job. So, that forms a deadlock. 

Let’s have a look at the skills that companies are looking for in a data science aspirant. The skills are broadly divided into two categories, i.e. technical skills and non-technical skills. 

Technical skills:

In technical skills, a data scientist must have good command over mathematics, statistics, probability, programming, tableau, and big data technologies. Here is the list of technical skills that a data scientist must have:

  • Descriptive statistics
  • Inferential statistics 
  • Linear algebra 
  • Calculus
  • Discrete math 
  • Optimization theory
  • Python 
  • R
  • Database query language 
  • Tableau 
  • Big data technologies 

Non-technical skills:

Along with technical skills, non-technical skills are also important for a data scientist. Here are the non-technical skills:

  • Data intuition
  • Data inquisitiveness
  • Business expertise
  • Communication skills
  • Teamwork 

CONCLUSION 

These are the skills which a data scientist must possess and skills are the foremost reason why there is a shortage of data scientists in the industry. Work on the above-mentioned skills to drive your career to data science!

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devops trends 2021 devops enters a new decade with the hottest trends
DevOps Trends 2021: DevOps Enters a New Decade with the Hottest Trends

Have you heard IDC’s latest predictions on DevOps? According to this study, the DevOps market expects to grow from $2.9 billion to $8 billion by 2022! According to the experts, it is set to dominate the new decade by offering more excellent benefits to developers and users. Organizations are more likely to adopt DevOps at all levels now, as it is efficient and quick to implement. It is already starting to reshape the software world, and 2021 will undoubtedly be bigger and better than in previous years.

So what are the critical DevOps trends for 2021? We think these eight DevOps trends will steal the show now and shortly.

| The rise of AI

The time when manual testing will no longer be the order of the day is not far off. With AI combining with DevOps automation, the change in the way processes are performed is already underway.

AI uses logs and activity reports to predict the performance of a code. When you harness the power of AI, automating acceptance testing, implementation testing, and functional testing becomes easier for organizations. As a result, the software release process becomes flawless, more efficient, and faster, as continuous delivery is assured.

According to the latest expert predictions,  there will be increasingly DevOps ideas in workflows due to the growing number of AI-powered applications. DevOps will emerge as the preferred option for managing the testing and maintenance of several models in the production chain.

| Using serverless computing in DevOps

DevOps is ready to reach a new level of excellence with serverless computing. These applications rely on third-party services, called BaaS (Backend as a Service), or on custom code running in temporary containers, called FaaS (Function as a Service).

Serverless means that the company or individual running the system does not need to rent or buy virtual machines to run the backend code.

The main advantage of serverless computing is that it gives the developer the freedom to focus solely on the development aspects of the application without having to worry about anything else. It does not require upgrading existing servers, and deployment is quick and easy. It also takes less time and doesn’t cost a cent.

| Controlling security breaches with DevSecOps

A majority of DevOps companies are turning to DevSecOps because the number of incidents related to security breaches has increased recently. IT companies consider DevSecOps as one of the many DevOps best practices.

Think of DevSecOps as an approach to application security that builds security into every aspect of the code from the beginning.

Security measures in the development process will lead to greater collaboration in the process. It will make the process much more efficient, error-free, and effective. Expect more adoption of DevSecOps in the years to come.

Save time through greater automation.

Detecting errors quickly, enhancing security, and saving time: automation offers all this and more. It eliminates the need for manual work throughout the software development cycle. It is no wonder that industrialization played an essential role in 2021.

So there are six Cs of DevOps, which are:

  • Continuous optimization and feedback
  • Continuous monitoring
  • Continuous deployment and release
  • Continuous testing
  • Collaborative development
  • Ongoing business plans

must integrate with each of these components in the coming years to be more efficient.

| The container of choice is Kubernetes.

Kubernetes has been a massive success in 2019, and this reign will continue in 2021 and beyond. Several valuable features and improved experiences have led developers to rely on Kubernetes more than others.

Kubernetes will help businesses in terms of scalability, portability, and automation, which is why it is considered one of the best container technologies. There will be new and better features of Kubernetes in the coming years that will support reliable and efficient distributed workloads in different environments.

| The growing popularity of Golang

             

Like Kubernetes, Docker, Helm, and Etcd, Google’s Golang is a programming tool that lends itself well to DevOps tools. It’s a new language compared to the other options, but it fits well with DevOps goals such as software and application portability, modularity, performance efficiency, and scalability.

Leading brands such as YouTube, Dropbox, Apple, Twitter, and Uber have adopted this cloud-based programming language.

With support from Google and ideal for DevOps, the future of Golang looks bright. DevOps teams have either already started using it or are planning to deploy it shortly. In the end, it will help organizations develop highly competitive concurrent applications with accurate results for software development companies.

| The growing importance of cloud-based native DevOps

It is possible to ensure better user experiences, better transformation, and innovation management when cloud-native DevOps is adopted. It is the proper use of technology to automatically manage the configuration, monitoring, and deployment of cloud services.

With cloud automation, the software can release faster. Thus, a bright future awaits cloud-based technologies. Oracle believes that 80 percent of enterprise workloads will eventually move to the cloud by 2025.

Moreover, the US Air Force has embraced cloud principles and has agile approaches to developing applications that run on more than one cloud format.

| Growth in the use of mesh services

Organizations can gain several benefits from adopting microservices. Developers use microservices to increase portability, even if it doesn’t make the DevOps team’s job any easier. Operators need to manage large multi-cloud and hybrid deployments.

The emergence of microservices has led to increased use of service networks, which promise to reduce deployment complexity. Service networks provide the ability to observe and manage a network of microservices and their interactions. This composition offers a complete view of services. It helps SRE and DevOps developers with their complex operational needs such as end-to-end authentication, access control, canary deployment, and A/B testing.

You’ll see an increase in adoption and offerings as these are critical components to running successful microservices. Service mesh is the crossroads that the enterprise must cross when moving from monoliths to microservices.

Conclusion

The DevOps field is constantly growing, and the future bodes well for it. Organizations all over the world are using it because of the many benefits it brings to their businesses. Keep a center on the most modern trends in such a situation because when DevOps reaches new heights, it will drag your business down with it. If you are looking for a software development team there are many software development companies that may help you in growing the buisness.

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recent advances in the cognitive cloud computing market
Recent Advances in the Cognitive Cloud Computing Market

Cognitive cloud computing are self-learning systems that imitates the human brain with the help of machine learning. It is often mentioned as the third age of computing that works by utilizing big data analytics and deep learning algorithms.

Factors Propelling Market Growth

Cognitive cloud computing system can combine information or data from different sources such as natural language processing, data mining, and pattern recognition and suggest the most suitable strategic approaches for businesses.

These are the benefits of the advanced technology that has enhanced the adoption of cognitive cloud computing infrastructure by industry verticals including healthcare, BFSI, and retail. This is one of the major factor behind the growth of the market. Another factor behind the market growth is the growing application of artificial intelligence in cognitive cloud computing. Especially in healthcare sector, cognitive computing with AI technologies are being used to in oncology sector to develop suitable medicine. Furthermore, rising implementation of cognitive cloud computing model in OTT sector for high quality video streaming is expected to enhance the market growth.

Recent Advances in the Market

According to a recent report by Research Dive, the most significant players of the global cognitive cloud computing market players include SparkCognition, CognitiveScale, Microsoft, Nuance Communications, Numenta, Cisco, SAP, EXPERT.AI, Hewlett Packard Enterprise Development LP, and IBM. These companies are working towards the further growth of the market by developing various strategies such as merger & acquisition, partnership and collaboration, and product launches etc.

Some of the most recent developments of the market has been mentioned below:

  • According to a most recent news, Nuance Communications, Inc. has been ranked the No.1 Solutions Provider by Black Book Research in five categories. Medical speech recognition and AI technologies are two main categories. The rank was entitled to the company based on 3,250 survey responses from 203 hospitals and 2,263 physician practices. Undoubtedly, the rankings validates Nuance’s unparalleled understanding of clients’ needs, farsighted strategy.
  • As per a latest news, SparkCognition has announced a collaboration with Cendana Digital, a data science solutions company. SparkCognition is considered as the world’s leading industrial AI Company. The partnership is aimed at the expansion of SparkCognition’s global presence to bring advanced AI solutions to the oil and gas market of Malasiya. The partnership was announced on July 9, 2020.
  • In June, 2020, SparkCognition and Siemens has initiated a new partnership on a cybersecurity system, DeepArmor Industrial, stimulated by Siemens. This system is designed to protect operational technology (OT), endpoint, or remote assets across the energy value chain by leveraging artificial intelligence (AI) to observe and identify cyberattacks.

This is an innovative AI-enabled system which will provide the benefits of next-generation antivirus, application control, threat detection, and immediate attack prevention to endpoint power generation, oil and gas, and transmission and distribution assets, which will fleet level cybersecurity monitoring and protection capabilities to the energy industry for the first time.

  • recent news reveals that Hewlett Packard Enterprise (HPE) is going to initiate a partnership with Wipro Limited. The business giants aims to cooperatively convey their portfolio of hybrid cloud and infrastructure solutions as a service through this partnership. This partnership will enable Wipro to leverage HPE GreenLake across its managed services portfolio and offer a pay-per-use model. This model is agile, elastic, subscription-based, and offers an unfailing cloud experience to the consumers. This will give the customers the benefit of fast tracking their digital transformation efforts by eradicating the necessity for upfront capital investment and overprovisioning costs, while appreciating the added benefits of security, compliance, and on-premises control.

Impact of COVID-19 on the Industry

The coronavirus outbreak has impacted all industries in some way. However, for cognitive cloud computing market is has been proved to be very beneficial. The main attributor of this growth is the rising demand of natural language processing technique in the healthcare and pharmaceutical organizations. This technology has been used to support the healthcare professionals and scientists during the pandemic. NLP technique has been proved to be the most advanced approach to better patient monitoring and patient care. Moreover, being an automated process, the NLP technique allows the healthcare staff to manage and monitor patient more effectively. These are the main factors behind the growth of the cognitive cloud computing market during the pandemic.

About Us:
Research Dive is a market research firm based in Pune, India. Maintaining the integrity and authenticity of the services, the firm provides the services that are solely based on its exclusive data model, compelled by the 360-degree research methodology, which guarantees comprehensive and accurate analysis. With unprecedented access to several paid data resources, team of expert researchers, and strict work ethic, the firm offers insights that are extremely precise and reliable. Scrutinizing relevant news releases, government publications, decades of trade data, and technical & white papers, Research dive deliver the required services to its clients well within the required timeframe. Its expertise is focused on examining niche markets, targeting its major driving factors, and spotting threatening hindrances. Complementarily, it also has a seamless collaboration with the major industry aficionado that further offers its research an edge.

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complete guide to be an artificial intelligence professional
Complete guide to be an artificial intelligence professional

Artificial Intelligence is one of the biggest technological waves that have hit the world of technology. According to research from Gartner, artificial intelligence will create a business value worth US$3.9 trillion by 2022. Globally the artificial Intelligence market will grow at a rate of 154 percent. This resulted in the high demand for AI engineers today.

With the growing demand for AI, many individuals are considering it as a career option. In this article, let’s understand the step-by-step process of becoming an artificial intelligence professional.

Step-1:  One of the crucial requirements for an individual, who is seeking a career in the field of AI must good at numbers, i.e. they should polish their basic math skills. This will help in writing better code.

Step-2: In this step, one must strengthen their roots, on those concepts that play a vital part in this field. These are the following concepts:

  • Linear algebra, probability, and statistics – As mentioned before mathematics is an integral part of AI. And if an individual wants to make a growing career in it, then they must have good knowledge of the concepts of advanced math. They are vectors, matrices, statistics, and dimensionality, and must also have knowledge of probability concepts like the Bayes Theorem.
  • Programming languages – The most crucial aspect is that an individual should be learning programming languages, as they play a prominent role in AI. One can enroll in an AI engineer certification course to learn the programming languages. There are several programming languages, an individual should choose at least one among the following to learn and perfect:
  • Python
  • Java
  • C
  • R
  • Data structures – Enhance the way to solve problems involving data, create an analysis of data more accurately so one can develop their own systems with minimum errors. Learn the different parts of programming languages, which will be useful in getting an understanding of data structures like stacks, linked lists, dictionaries, etc.
  • Regression – Regression is very helpful for making predictions in real-time applications. It is very important to have good knowledge of the concepts of regression.
  • Machine learning models – Gain knowledge on the various machine learning concepts, which include Decision trees, Random Forests, KNN, SVM, etc. Learn the ways to implement these by understanding the algorithms as they are quite helpful in solving the problems

Step-3: In this step, the artificial intelligence professionals must learn more in-depth concepts, which are a complex part of AI. If one master these concepts then they can excel in their career in the field of AI.

  • Neural networks It is a computer system modeled on the human brain and nervous system, which works by incorporating data through an algorithm it is developed on. The concepts of neural networks are the foundations for building AI machines, it is better to have a deep understanding of its functionalities.

There are different kinds of neural networks, which are used in various ways. Some of the common neural networks are:

  • Perceptron
  • Multilayer perceptrons
  • Recurrent neural network
  • Sequence to sequence model
  • Convolutional neural network
  • Feedforward neural network
  • Modular neural network
  • Radial basis function neural network
  • Long Short-Term Memory (LSTM)
  • Domains of AI – After gaining knowledge about the concepts and different kinds of neural networks, learn about the various applications of the neural networks, it will be helpful to build one’s own applications. Every application in the AI field demands a different approach. The artificial intelligence professionals must start with a specific domain, and then can proceed further, to master all the fields of AI.
  • Big data – Though it is not considered a crucial part of gaining expertise in AI, understanding the basic concepts of big data will be fruitful.

Step-4: This is the last step in the process of becoming an expert AI professional. Following things are required to be a master in the field of AI:

  • Optimization techniques – By learning optimization of algorithms helps to maximize or minimize the error function. These functions are based on the models’ internal learnable parameters that play a key role in the accuracy and efficiency of results. Learning this will be helpful to apply optimization techniques and algorithms to model parameters, which are useful to attain optimum values and accuracy of such parameters.
  • Publish research papers – One of the best ways to establish one’s own credibility in the field of AI is by going a step forward by reading research papers in this field and publish research papers. Start your own research and understand the cases that are in the process of developing.
  • Develop algorithms – After completing the process of learning and research, start working on developing algorithms. You might bring a new revolution with the knowledge you have.

Conclusion

The aforementioned steps will ensure an individual sail through the learning path of AI. Undoubtedly, mastering all the skills can be a difficult task. But one can achieve it with hard work, continuous practice, and consistency.  

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the pros and cons of working for a startup
The Pros and Cons of Working for a Startup

As a machine learning professional, I have worked for several startups ranging from zero to 600 employees, as well as companies such as eBay, Wells Fargo, Visa and Microsoft. Here I share my experience. A brief summary can be found in my conclusions, at the bottom of this article.

It is not easy to define what a startup is. The first one I worked for was NBCi, a spinoff of CNET, and had 600 employees almost on day one, and nearly half a billion dollars in funding, from GE. The pay was not great especially for San Francisco, I had stock options but the company went the way many startups go: it was shut down after two years when the Internet bubble popped, so I was able to only cash one year worth of salary from my stock options. Still not bad, but a far cry from what most people imagine. I was essentially the only statistician in the company, though they had a big IT team, with many data engineers, and were collecting a lot of data. I quickly learned that my best allies were in the IT department, and I was the bridge between the IT and the marketing department. I was probably the only “neutral” employee who could talk to both departments, as they were at war against each other (my boss was the lead of the marketing department). I also interacted a lot with the sales, product, and finance teams, and executives. I really liked that situation though, and the fact that there was a large turnover, allowing me to work with many new people (thus new connections and friends) on a regular basis, and on many original projects. The drawback: I was the only statistician. It was not an issue for me.

When people think about startups, many think about a company starting from scratch, with 20 employees, and funded with VC money. I also experienced that situation, and again, I was the only statistician (actually chief scientist and co-founder) though we also had a strong IT team. It lasted a few years until the 2008 crash, I had a great salary, and great stock options that never materialized. But they eventually bought one of my patents. I was hired as co-founder because I was (back then) the top expert in my field: click fraud detection, and scoring Internet traffic for advertisers and publishers. Again, I was the only machine learning guy, and not involved with live databases other than to set the rules and analyze the data. And to conceptually design the dashboard platform for our customers. I was interacting with various people from various small teams, occasionally even with clients, and prototyping solutions and working on proofs of concept – some helped us win a few large customers. I was in all the big meetings involving large, new clients, sometimes flying to the client’s location. This is one of the benefits of working as a sole data scientist. Another one, especially if you have specialized, hard-to-find skills (earned by running small businesses on the side), is that I worked remotely, from home. 

Yet another startup, the last one I co-founded, structured as a S-corp, had zero employee, no payroll, no funding, no CEO, and no office or headquarter (the official address, needed for tax purposes, was listed as my home address). It had no home-made Internet platform or database: this was inexpensively outsourced. We were working with people in different countries, our IT team (a one-man operation) was in Eastern Europe. This is the one that was acquired recently by a tech publisher, and my most successful exit. It still continues to grow very nicely today, despite (or thanks to) Covid. It started bare-bone unlike the other ones, making its survival more likely to happen, with 50% profit margins. However, people working with us were well paid, offered a lot of flexibility, and of course everyone was always working from home. We only met face-to-face when visiting a client. No stock options were ever issued; I made money in a different way. I was interacting mostly with sales, and also contributing content and automatically growing our membership using proprietary techniques of my own, that outsmarted all the competitors.

As for the big companies I worked for, I will say this. At Wells Fargo, I was part of a small group (about 100 people) with open office, relatively low hierarchy, and all the feelings of working for a startup. I was told that this was a special Wells Fargo experiment that the company reluctantly tried, in order to hire a different type of talent. It is unusual to be in such a working environment at Wells Fargo. To the contrary, Visa looked more like a big corporation, with many machine learning people each working on very specialized tasks, and a heavier hierarchy. Still I loved the place, and it really helped grow my career. The data sets were quite big, which pleased me. One of the benefits of working for such a company is the career opportunities that it provides. Finally, it is possible to work for a startup within a big company, in what is called a corporate startup. My first example, NBCi, illustrates this concept; in the end I was indirectly working for GE or NBC and even met with the GE auditing team and their six-sigma philosophy. Many of the folks they brought to the company were actually GE and NBC internal employees. 

Conclusion

Finding a job at a startup may be easier than applying for positions at big companies. If you have solid expertise, the salary might even be better. Stocks options could prove to be elusive. The job is usually more flexible and requires creativity; you might be the only machine learning employee in the company, interacting with various teams and even with clients. Projects can potentially be more varied and interesting, and the environment is usually fast-paced. Working from home is usually an option. You may report directly to the CEO; the hierarchy is typically less heavy. It requires adaptation and may not be a good fit for everyone. You can also work for a startup within a big corporation: it is called a corporate startup.  Working for a big company may be a better move for your career, especially if your plan is to work for big companies in the future. Of course, startups also try to attract talent from big companies. 

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About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). You can access Vincent’s articles and books, here.

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