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the right foundation will ensure citizen data scientist success
The Right Foundation Will Ensure Citizen Data Scientist Success

When you take on the mantle of Citizen Data Scientist, there is a lot to process! Much of the reason business users feel overwhelmed at the idea of the Citizen Data Scientist role is the anticipation of having to learn new tools and techniques to analyze data.

“I am not a data scientist”, “I don’t understand analytics”, “this new role is going to take up all my time”, “my manager doesn’t care about this stuff” – these are just a few of the arguments you will hear when you bring up the concept of Citizen Data Scientists, improved data literacy and data democratization. But, in the real world, the introduction of a Citizen Data Scientist initiative should not be intimidating or overwhelming.

There are many benefits to the Citizen Data Scientist evolution. Here are just a few:

  • The business will base its decisions on facts
  • It is easier to understand and address issues and opportunities
  • Business users can add more value to the business
  • Business users can add skills to their resume and advance their careers
  • Businesses can improve their competitive stance
  • Businesses will be more nimble and agile and ready to respond to the market

All of these benefits depend on the organization being prepared to change its culture and to support Citizen Data Scientists as they take on their new role. One of the most important aspects of this change requires establishing a foundation of augmented analytics solution and providing easy, intuitive methods to access and perform analytics without the need for data scientist or business analyst skills.

With the right self-serve augmented analytics, Citizen Data Scientists can leverage auto-recommendations, suggestions and guidance to choose the right algorithm or analytical technique and visualization techniques to receive clear results suitable for presentation and reporting.

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the nft crypto art bubble has officially burst
The NFT Crypto-Art Bubble has Officially Burst
  • Non-fungible token assets are declining rapidly,
  • The market is down 70% from its peak,
  • NFTs , in a moderated form, are likely here to stay.

Non-fungible tokens (NFTs), tradable digital certificates that verify ownership of digital assets using blockchain technology, have dominated headlines in the last several months. The media mania hit a high with the $69 million sale of Beeple’s Everydays:The First 5000 Days. A few months after Beeple’s historic sale at Christie’s auction house, the crypo-art bubble has officially burst.

NFTs Take a Tumble

Beeple himself predicted the decline of NFTs in a March 2021 interview with CoinDesk, a media outlet for blockchain and cryptocurrency news [1]. “I think it’s a bubble,” he said. “If it’s not a bubble now, I do believe it probably will be a bubble at some point, because there’s just so many people rushing into this space.”  Concerned about Ethereum’s volatility, he cashed out his earnings into hard U.S. dollars [2]. That wise move protected his stash from the effects of this year’s May 19 sell-off, which wiped $1.2 trillion in value from the crypto market.  According to marketplace tracker Nonfungible.com [3], average prices for NFTs dropped nearly 70% from its February peak to around $1,400. As a result of the crash, and possibly because of environmental concerns, user activity has also plummeted.

Source: Nonfungible.com.

History Repeats Itself

It should not come as a surprise that non-fungible tokens are sliding downwards to a trough; They exhibit the highs and lows of any speculative market, which have followed predictable paths for centuries. In February 1637, a speculative frenzy in the Netherlands was fueled by the sale of one tulip bulb for 6,700 guilders, netting the seller enough to purchase a small mansion. A “tulip mania” ensued, followed by a crash later that month which wiped out up to 95 percent of bulb prices. Centuries later, the dotcom bubble followed a similar pattern, as have dozens of other fads, crazes and speculations. It was just a matter of time before NFT investments were dragged down by the gravity of certainty.

Interestingly, there is an even deeper connection between the 16th century tulip mania and the demise of crypto art: both occurred during epidemics, which forced people to spend inordinate amounts of time indoors.  Tulip mania coincided with a bubonic plague outbreak in the Netherlands [4], while the current Covid-19 pandemic likely had much to do with the NFT phenomenon as well as GameStop and dogecoin.

What’s the Future?

While history can tell us a lot about the expected peaks and troughs of speculative markets, it also offers a caution about dismissing new technology. Not so long ago, two obscure bicycle repairmen who had never been to college were ridiculed by notable scientists about the impossibility of their invention, until they flew their “impossible” invention—the airplane– at Kitty Hawk [5]. Internet trading was also dismissed as a passing fad after the dotcom bubble, only for Amazon to emerge from the rubble as a virtual powerhouse. It definitely isn’t all doom-and-gloom in the NFT-verse either; While crypto art is certainly taking a tumble, other virtual artifacts like digital real estate are holding their ground; Currently, NFTs linked to digital real estate are outselling crypto-art linked NFTs.

Chris Wilmer, a University of Pittsburgh academic who co-edits a blockchain research journal states in a Bloomberg article [6] that “There will be manias and irrational exuberance, but cryptocurrency is clearly here to stay with us for the long term and NFTs probably are too”. Only time will tell whether the market has long-term potential. While investors are more likely be to more judicious about their purchases going forward, NFTs, including crypto art, are likely here to stay; As long as artists continue to mint new NFTs and buyers still purchase them, the market will eventually stabilize as much as any speculative market can. [7].

 References

Image: Joate3, CC BY-SA 4.0, via Wikimedia Commons

[1] CoinDesk.com

[2] Beeple Immediately Converted His $53 Million NFT Earnings From ETH …

[3] Nonfungible.com

[4] Art’s NFT Question: Next Frontier in Trading, or a New Form of Tulip?

[5] They Wouldn’t Believe the Wrights Had Flown

[6] NFT Price Crash Stirs Debate on Whether Stimulus-Led Fad Is Over

[7]  NFTs Combat Bubble Burst Claims As Real Life Use Cases Push Forward

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personal cyber security tips
Personal Cyber Security Tips

We hope that these personal cyber security guidelines will assist our readers in becoming more cyber aware. These security suggestions were prepared based on our expertise managing millions of security events for organisations and professionals throughout the world.
The Top Cyber Security Tips for Individuals

1. Keep Your Software Up to Date

Ransomware assaults were a major attack vector for both enterprises and consumers in 2017, as evidenced by the statistics above. Patching obsolete software, both operating systems and applications, is one of the most critical cyber security strategies for preventing ransomware.

This aids in the elimination of significant vulnerabilities that hackers exploit to gain access to your devices. Here are some pointers to help you get started:

Set your device to receive automatic system upgrades.
Make sure your desktop web browser downloads and instals security updates automatically.
Keep your web browser’s plugins, such as Flash and Java, up to date.

Check out our blog for best practises in patch management!

2. Install anti-virus software and a firewall

To combat malicious attacks, anti-virus (AV) protection software has been the most widely used approach. Malware and other harmful viruses are prevented from entering your device and corrupting your data by antivirus software. Use only one anti-virus tool on your device, and be sure it’s from a reputable vendor.

When it comes to protecting your data from hostile attacks, using a firewall is essential. A firewall protects your device by filtering out hackers, malware, and other dangerous behaviour that occurs over the Internet and deciding what traffic is allowed to enter. Windows Firewall and Mac Firewall are the firewalls that come with Windows and Mac OS X, respectively. To protect your network from threats, your router should include a firewall.

3. Create strong passwords and use a password manager

Strong passwords are essential for internet security, as you’ve probably heard. Passwords are crucial in keeping hackers away of your information! According to the new password policy framework published by the National Institute of Standards and Technology (NIST) in 2017, you should think about:

Getting rid of the wacky, convoluted combination of upper case characters, symbols, and numbers. Instead, choose something more user-friendly that is at least eight characters long and no longer than 64 characters.
Do not re-use the same password.
At least one lowercase letter, one uppercase letter, one number, and four symbols are required, but not the characters & percent #@ .
Choose a password that is simple to remember, and never put a password hint out in the open or in a place where hackers can see it.
If you forget your password, you can reset it. However, as a general refresh, alter it once a year.

Try utilising a password management tool or a password account vault to make managing your passwords easier. Individuals will find LastPass FREE to be a useful tool. LastPass provides a free account as well as a $2/month premium with additional password capabilities.

4. Authentication with two-factor or multi-factor

Two-factor authentication, often known as multi-factor authentication, is a service that adds additional layers of security to the traditional password-based method of online identity. You would ordinarily input a username and password without two-factor authentication. However, if you use two-factor authentication, you will be asked to provide an extra authentication method such as a Personal Identification Code, another password, or even your fingerprint. After entering your login and password, you’ll be required to input more than two additional authentication methods with multi-factor authentication.

SMS delivery should not be utilised during two-factor authentication, according to NIST, because malware can be used to attack mobile phone networks and compromise data in the process.

5. Become aware of phishing scams and be wary of emails, phone calls, and pamphlets.

This year’s phishing scams are nastier than ever, according to a new blog post. In a phishing technique, the attacker impersonates someone or something that the sender is not in order to mislead the recipient into disclosing credentials, clicking a malicious link, or opening an attachment that infects the user’s machine with malware, trojans, or zero-day vulnerability exploits. This frequently results in a ransomware attack. In reality, phishing attempts are the source of 90% of ransomware outbreaks.

The following are some crucial cyber security tips to keep in mind when dealing with phishing schemes:

In conclusion, do not open emails from persons you do not know.
Know which links are safe and which are not — hover your mouse over a link to see where it leads.
In general, be wary of emails sent to you; check to see where they came from and if there are any grammatical issues.
Friends who have also been affected can provide malicious links. As a result, take extra precautions!

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seven famous apps using react js
Seven Famous Apps Using React js

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.

Source Prolead brokers usa

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.

Source Prolead brokers usa

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!).

Source Prolead brokers usa

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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