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With technologies seeing growth in businesses, more companies are embracing them. The impact of disruptive technologies such as Data Science, Artificial Intelligence (AI), Augmented and Virtual reality (AR and VR), Internet of Things (IoT), Blockchain, Cloud, big data analytics, and other technologies are more often discussed.  

According to a recent IDC survey, 47% of the US organizations surveyed thought that AI and robotics will have a positive impact on their organization’s job in the next three years. The companies and business leaders are striving to build capabilities in their organizations to ride the tide of digital transformation.

Let us explore how organizations are planning to leverage technology in their sectors. Though 61% of organizations indicated that they may hire new skills, 57% said that they would invest in reskilling the existing workforce. The Future of Work survey conducted by the IDC survey says that online training, internal knowledge sharing, and engagements with 3rd party training providers (to earn certifications for data analysts, engineers, cloud analysts, or other technologists) to reskill their employees.  

However, training and reskilling employees are not the responsibility of the organization alone. The professionals must take initiative to prepare for the new normal. It is necessary to make a shift in thinking to learn. They must evolve their skillset as new tools and techniques surface the organization. They need to reposition themselves by staying abreast of the technology front. It helps them to add value to the business.  

The Next Wave of Upskills for Data Scientists

As companies are becoming data-driven, data scientists must embrace the latest tools and technologies to automate tasks and conduct big data analytics. The next wave of upskills includes learning of AutoML, explainable machine learning models, Custom Neural Networks, reusable code, and Natural Language Processing.

  • Using AutoML, data scientists can evaluate models, train and deploy them. As the tasks get automated, they can focus on solving problems than selecting the ML models  
  • Custom in-house ML models can assist employees to achieve the company’s unique objectives
  • Data scientists must upskill themselves to explain the factors behind the code. It avoids biased insights
  • Data scientists must reuse codes to expedite their analytics. It helps them to complete projects at a faster rate
  • Professionals with Natural Language Processing (NLP) skills have an edge over the others to find relevant information from voice searches and text analysis

Business Benefits of Upskilling in Data Science 

It is necessary to integrate data science into the organization. And, reskill employees to derive actionable insights for growth. Some of the business benefits include:

  • Big data analytics help businesses to detect fraudulent activities
  • Help businesses to prevent fraud and data breaches
  • Outpace shareholders’ assumptions and competitors in revenue growth
  • Integrate data points with the customers’ info and derive insights
  • Target the potential market
  • Create personalized products for customers

Going forward, let us know how to upskill oneself to thrive well in the data world.

How to Upskill in Data Science 

It is necessary to focus on the skills that employees will be using. Some of them are:

  • Develop a job-specific upskilling plan or reskill yourselves to succeed
  • Correlate your tasks with the company’s bigger picture
  • Focus on business acumen to identify problems, approach, and create solutions
  • Pursue big data professional certifications. Learn the specific skill set in data analytics

Key Takeaways 

Many tech companies today are investing time and money to upskill their employees. For e.g., Wipro, Tech Mahindra, CSS Corp. Moreover, they are looking for a mix of internal and external talent for emerging new roles.

Make a move that best fits in the interest of your company by upskilling yourselves.

Blockchain and AI have been revolutionizing the industry in their own ways. Transforming various industries, financial services, and supply chain, for instance, by blockchain, and IT by artificial intelligence, have proven the extent to which they can bring out change and innovation.

Blockchain’s underlying encryption techniques which help to store, distribute, and verify data, coupled with AI’s decision making can offer immense value for development and progress.

One useful way in which Blockchain technology can help AI is by making it easily understandable. We can understand how decisions are made by machine learning by leveraging Blockchain’s capability to store all data and variables that go through decision making.

Further, AI can improve the efficiency of Blockchain technology far greater than humans. Blockchain requires an immense amount of computing power. Hashing algorithm – the prominent approach in most blockchain offerings is a hammer and tongs approach. With AI, this could be reduced to smaller and less power consuming tasks.

Here are some other ways in which Blockchain and AI can complement each other

1.Less Computing Power


As mentioned above, blockchain requires a large amount of computing power. The reason is -- it uses a ‘brute force’ approach for its operations. For mining Bitcoin’s blocks and verify, blockchain runs all possibilities until the right one fits in. It’s like code-breaking, trying all possible code combinations until the right one fits in. AI can reduce these possibilities; given that it is fed right training data.

2.Creating diverse data sets

Blockchain is known for its humongous decentralized and transparent network which can be accessed from anywhere on the network. Companies are now using blockchain to build a distribution network. Singularity Net is a network over which data and algorithms can be shared, leading to better access to information and future development of artificial intelligence—what is being called “decentralized A.I”.

Singularity AI uses blockchain and AI to hosts diverse data sets.

3.Data Protection

AI works on personal data. AI learns about the world and about us with the help of data. The more data it uses, the better it gets at its work. Blockchain technology, on the other hand, secures data with its encryption, allowing access to people who only have been approved to see. Combining both—blockchain and AI – will build more secure databases for sensitive personal data.

For instance, medical and financial data are sensitive information to share with a company. Alternately, this data can be stored on the blockchain and accessed by an AI algorithm, upon permission, to provide a recommendation.

4.Data monetization

Companies like Google, Facebook, Snap Chat, etc. are already using personal data to drive their revenue. However, this data is accessible to companies and individuals have more or less access to control.

As blockchain stores data in encrypted form. This can be easily utilized to monetize it. As blockchain requires permission, accessibility of data can be controlled. We can easily choose the amount of information we want to share. Similarly, as AI requires data, it opens up the opportunity to buy data from creators and data marketplaces. This will make the process fairer.

Further, it will open opportunities for smaller AI companies that do not generate their own data, instead buy from other places at far more cost. Through the decentralized marketplace, they will be able to access private valuable data, which otherwise would be expensive to buy.

Final words

A combination of artificial intelligence and blockchain is yet to be explored. Largely, this space has remained undiscovered. Even though scholars have explored the combination, commercial research is yet to be explored.

A simplistic view is – AI requires data to improve and enhance, but this data is secured by blockchain. AI is revolutionary and blockchain assists in this. However, how the combination of these technologies will turn out is a complete guess. One thing is sure –blockchain and artificial intelligence have potential for disruption and it is evolving.


Slow internet connectivity can be a trying task for internet users. From simple hacks like rebooting the router to using high-end tech support like Mywifiext, there are numerous ways to boost the speed.

Speed test and the choice of your internet plan: The first and foremost thing to do is to test the speed of your internet. There are many speed testing websites that offer to test your internet speed. Once you have installed a reputed and trusted speed testing tool, you can cross-check the actual speed with the given speed of your internet plan. If necessary you will have to change your plan according to your browsing needs.

Check unnecessary or illegal devices that are connected to your internet: Sometimes when many devices are connected to the Wi-Fi, the data connectivity seems to have slowed down, because of bandwidth usage. Sometimes, when you have connected a regular laptop and an iPad to your home Wi-Fi you observe a slower connection to the laptop. Also, media and gaming consoles consume higher bandwidth causing the Wi-Fi to slow down drastically. Check for connected devices that are no longer in use and remove them accordingly.

If your Wi-Fi connection is not secured with a password, a neighbor or anyone who has observed your unsecured Wi-Fi connection might be hopping on to your network and using up your bandwidth. You can check for such unwanted users in the log of connected users available under the Network or Wireless tab.

Check router for upgrades: If your router is outdated, you may want to update it with a newer model or check for updates. Modern routers come with auto-update firmware features, which make you worry less about an incompatible router. If you are using the same router for a decade it is best to search for modern routers to boost your internet speed.

Restart router: If your router is brand new and is up-to-date and yet you are facing internet slow down, try rebooting your router. Simply remove all the cords and power of the router. Switch it on after connecting the modem and wait for it to broadcast the internet signal. You may observe significant improvement in the router speed after this as it clears the router cache.

Change router placement for better signal: If you have hidden your router in a cabinet or a shelf, it is recommended that you change the place. The ideal place for routers is a high open shelf with the antenna placed at an angle between the vertical and horizontal. If it has two antennae you may place an antenna vertically and another horizontally to cover both smart devices and laptops.

Change router channel: Sometimes, the channel interference plays a significant role in slowing down the Wi-Fi speed. Try changing the channel to avoid neighbor channel interference.

Wi-Fi range and how to increase it: Sometimes weaker Wi-Fi signal strength and range may be causing a slow-down of the internet. Try using a modern network to overcome this problem.

Use of Mesh network: Mesh network consists of the main unit which is connected to the modem, there are other units which cover the entire house and work in tandem to give you a strong Wi-Fi signal.

Tech support Mywifiext.net new extender setup for Wi-Fi extenders like Netgear is also available to provide you with extended support for your Wi-Fi network and boost your internet speed.

“If our era is the next Industrial Revolution, as many claims, Artificial Intelligence is surely one of its driving forces,” says Fei-Fei-Li, a Chinese Computer Science expert.  

Artificial Intelligence (AI) has the power to transform the way businesses operate and humans interact with each other. Today, we often hear that “AI will take our jobs” or “AI will create more jobs soon”. Though the statements are paradoxical, both the statements appear to be true in one or the other condition.  

While the argument continues on the long-term impact of AI, the majority of the engineers are excited to grab the AI career and take their job to a new level altogether. Several activities are seeing automation already and no job will remain untouched by AI, though the degree of impact may vary from field to field.  

According to a report by Economic Times, 50k jobs are vacant that require Data Science and AI expertise. The talents are untrained to meet the new-age demands. This makes upskilling a norm for the professionals. 

It is estimated that AI and Machine Learning alone have at least 1.4 million job openings at the moment. A LinkedIn report suggests that 11.5 million jobs will be created by 2026.  

To grab an opportunity in the AI field, you need to upgrade your skills and redesign your existing career. It is necessary to earn a certification in AI apart from graduation in Computer Science or related fields to make a smooth switch in your career. The curve is steep and requires you to have a set of basic skill sets. Know how to embark on your journey into the AI industry.   

Assess your work profile

It is recommended to enlist your daily tasks and evaluate each activity for automation possibilities. If any of your activity involves the performance of the repetitive task(s) that needs little or no human discretion, root cause analysis, or identifying triggers, anomalies, and alerts, then AI can perform them effectively.  

Some of the activities such as IT infrastructure scaling, maintenance, monitoring, service desk, and database administration tasks already use AI algorithms. Moreover, quality analysis, quality control, and audits can be performed by AI.  

Embrace the change

It is recommended to accept AI automation possibilities and learn about AI capabilities. There is nothing to get scared off or to fight back. Whether it is computer vision, pattern recognition, or natural language processing, build awareness of robotic process automation in your industry. Augment AI by leveraging the benefits of AI in your career and utilize the time for other higher-value activities such as technology or business.  

Learn AI capabilities

It is crucial to learn technologies that are alternatives for your area of specialization and upskill yourselves. Understand the broader opportunities AI can bring in and be the technologist who can navigate the changing technology landscape. Identify the opportunities for innovations using AI in your industry. Be the problem-solver.  

You can learn AI authentically by earning AI certifications. Some of the popular AI certifications include:

  • MIT Professional Certificate Program in Machine Learning and Artificial Intelligence
  • Artificial Intelligence Engineer TM from ARTiBA
  • Machine Learning Certification by Experfy
  • Education-Google AI, a free course on AI from Google
  • Artificial Intelligence (AI) Certification by Columbia University
  • Artificial Intelligence (AI) Certification by Microsoft
  • Machine Learning at Udacity

And so forth.  

A dedicated AI certification course will help you to master the subject. Artificial Intelligence is the next thing in the IT sector and as the organizations are adopting this technology at a faster rate, the demand for certified professionals is increasing. Give a boost to your career by enrolling in AI program(s). 

 

Reposted sharmaniti437's post.

Data science is a vastly lucrative and growth-oriented field. Newer opportunities are coming up across industries. Some high-paying job roles make up roles across data science. Demand for talent has been steadily increasing in the industry. You can venture in this field by equipping yourself with data science –related skills. So how do you do that?  

Answer certifications! 

Several data science certifications are available across the globe. You can pursue certifications based on your level of experience and knowledge. As the demand for Big Data talent is expanding, pursuing these certifications can help you become more employable. These certifications are offered by vendor, industrial bodies, and educational institutions. 

Here are some of the prominent ones:   

  1. edX – This certification is provided by Microsoft, as part of Professional Program Certificate in Data Science.  Knowledge of R and Python is a pre-requisite for this certification. Students further learn data exploration, visualization, probability and statistics, and machine learning.
     
  2. IBM- Data Science Fundamentals 

This program is rebranded as Cognitive Class and covers data science 101, methodology, programming in R and open source tools. 

3. Data Quest

 This is one of the few online course providers in data science. It offers three paths – Data Analyst, Data Engineer, and Data Scientist. This platform offers free access to its courses. To get a hang of the course, you can try these courses. Further, you can opt for premium courses as well.  

4. Coursera – Data Science Specialization 

Coursera offers a data science course in association with John Hopkins University. As part of the course, you learn natural language processing, cluster analysis, programming in R and applications of Machine Learning. There are total of 10 courses as part of the course. 

5. Data Science Council of America 

This credentialing body offers multiple credentials for people with different experience levels. Starting from ABDE, which stands for Associate Big Data Engineer) meant for fresh graduates who are looking to get into Big Data to PDS (Principal Data Scientist) professionals with at least 10 years’ experience in technology space. 

So these are five prominent globally recognized data science certifications. Harvard University and MIT also offer data science courses which cover essential areas such as probability, visualization, inference and modelling, linear regression and machine. As part of the MIT course, learners have opportunity to learn big data analysis and prediction modelling using statistical inference and probabilistic modelling. 

So if you’re looking to get into data science, go for the above courses. Most of them free can help you get started on the path to becoming an in-demand professional in data science. There are paid courses as well. However, if you are just getting started these will give you taste of what’s it like to work in data science. So don’t wait and start learning! Opportunities are on your way. The more you wait, the more you lose.
 
 

In the age that we are living in, do you which is the technology causing maximum disruption? It is Artificial Intelligence, as per Gartner’s 2019 CIO Survey. So, it is clear that AI is being adopted and implemented across a plethora of industries. But, there is one thing that is blocking organizations from embracing AI in an optimum fashion which is the lack of suitable talent. There is an insufficient supply of AI engineers and data scientists in the market which is becoming a bottleneck for industries on their way to success.

According to the Chief Executive Officer of Hired, Mehul Patel, there is a 38% increase in the demand for competent data engineers along with a 27% enhancement in the demand for smart machine learning engineers, according to the new State of Software Engineer report. 

As a matter of fact, employers are projected to face major challenges while seeking tech geniuses who won a flair for AI and machine learning, hence, there is going to be an intense competition amongst employers when hiring laborious tech experts. Which clearly indicates that this is a golden era for professionals well-versed with contemporary technologies. These developments are propelling companies to offer exorbitant salaries to their Machine learning engineers who are easily earning 20k more than other tech personnel around the globe.

Believe it or not, top-notch firms are going bonkers over AI and are willing to pay big bucks to acquire strong teams of people possessing AI certifications who can help them get a competitive advantage in their industry. A good example of this is McDonald's that ended up paying 300 Million Dollars for Dynamic Yield which is known to be an Artificial Intelligence company that enables other firms to strengthen their customer experience. But, what McDonald's achieved might not be a feasible move to make for other companies.

So what are the other routes to AI implementation? Here are some pragmatic ideas:

Automation

Artificial intelligence is taking a leap into multiple industries. Therefore, a myriad of automation tools has come into the picture which is used by startups as well as tech biggies. For instance, Microsoft has conceived a group of systems in order to streamline processes.

Implementing an effective automation platform for data science is the best thing that companies can do to make their data science teams more productive. Automation is the way to simplify tasks that were previously carried out by data scientists which enables important resources of a firm like data engineers, business analysts along with AI engineers to focus on a large-scale project that can simply be rum through GUI operation.

Automation has solved the issue of seeking new talent since most of the data science processes are performed by machines which helps the organizations maintain proficient data science teams at a low cost. But, this is definitely not a universal solution indicating that companies would not need qualified data scientists and AI engineers. To tell the truth, employers will still be in deep need for tech geeks holding AI certification, but at least, automation can ensure that the highly adroit employees do not waste their time mundane and monotonous activities.

Reskilling

 In case, your workforce consists of people who are working as business analysts or have a functional knowledge of big data, they can prove to be eligible candidates for AI certifications that can infuse relevant skills into these employees. Certifications form the most solid avenue to learning Python as well as TensorFlow.

By good fortune, there is a whole line of certifications in that can help your employees delve into the world of AI and machine learning. Moreover, employers can make the smart move of hiring certified AI engineers who can lead big-scale technical projects along with mentoring freshman of the AI field.

Tech giants with sufficient resources can prepare for in-house training which can be conducted in large groups. There must company-wide training sessions and it is only possible when the organizations foster a data-driven culture. It is then that AI will be fully embraced and business leaders shall work hand in hand with data science teams to fulfill the overall objectives of the firm.

Data science is a vastly lucrative and growth-oriented field. Newer opportunities are coming up across industries. Some high-paying job roles make up roles across data science. Demand for talent has been steadily increasing in the industry. You can venture in this field by equipping yourself with data science –related skills. So how do you do that?  

Answer certifications! 

Several data science certifications are available across the globe. You can pursue certifications based on your level of experience and knowledge. As the demand for Big Data talent is expanding, pursuing these certifications can help you become more employable. These certifications are offered by vendor, industrial bodies, and educational institutions. 

Here are some of the prominent ones:   

  1. edX – This certification is provided by Microsoft, as part of Professional Program Certificate in Data Science.  Knowledge of R and Python is a pre-requisite for this certification. Students further learn data exploration, visualization, probability and statistics, and machine learning.
     
  2. IBM- Data Science Fundamentals 

This program is rebranded as Cognitive Class and covers data science 101, methodology, programming in R and open source tools. 

3. Data Quest

 This is one of the few online course providers in data science. It offers three paths – Data Analyst, Data Engineer, and Data Scientist. This platform offers free access to its courses. To get a hang of the course, you can try these courses. Further, you can opt for premium courses as well.  

4. Coursera – Data Science Specialization 

Coursera offers a data science course in association with John Hopkins University. As part of the course, you learn natural language processing, cluster analysis, programming in R and applications of Machine Learning. There are total of 10 courses as part of the course. 

5. Data Science Council of America 

This credentialing body offers multiple credentials for people with different experience levels. Starting from ABDE, which stands for Associate Big Data Engineer) meant for fresh graduates who are looking to get into Big Data to PDS (Principal Data Scientist) professionals with at least 10 years’ experience in technology space. 

So these are five prominent globally recognized data science certifications. Harvard University and MIT also offer data science courses which cover essential areas such as probability, visualization, inference and modelling, linear regression and machine. As part of the MIT course, learners have opportunity to learn big data analysis and prediction modelling using statistical inference and probabilistic modelling. 

So if you’re looking to get into data science, go for the above courses. Most of them free can help you get started on the path to becoming an in-demand professional in data science. There are paid courses as well. However, if you are just getting started these will give you taste of what’s it like to work in data science. So don’t wait and start learning! Opportunities are on your way. The more you wait, the more you lose.
 
 

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