As enterprises, it is very difficult to stay a step ahead of attackers and that’s exactly why learning becomes crucial here. We learn from what other enterprises have experienced and tweak the learnings to our needs and implement revisited strategies. Today’s post sheds light exactly on this, where we revisit the most insane enterprise security breaches in the past few years and take inferences from them to optimize the cybersecurity measures in our enterprise.  

Let’s look at each in no particular order.  

Photo by FLY:D on Unsplash

The Yahoo Incident Of 2017 

Remember Yahoo? There was an infamous incident back in 2017, where the company admitted a security breach that happened in 2013. Yes, after being silent for four years, the company came out and shared that over 3 billion accounts had been breached. The highlight of the attack was that even the security questions that are used to add a protective layer to passwords had been breached. This remains, till date, one of the largest network security breaches of all time.  

Capital One Hack Of 2019 

In March 2019, a configuration vulnerability of a web app firewall was exploited by a hacker, leading to a server access gain. The hacker reportedly got access to data of over 100 million Capital One customers, including personal information. Further investigations revealed that the data was intended to be sold to other vendors and people online. The inference from this is to have firewalls frequently scrutinized. Not just firewalls, apps and modules that are taken to the cloud need to be airtight in terms of configurations as well.  

Credential Stuffed Donuts From Dunkin Donuts 

Dunkin Donuts caught itself in a security breach stemming from a credential stuffing attack. In simple terms, credential stuffing is when attackers use credentials leaked on other websites or portals to gain access to another specific website. With Dunkin Donuts, a first-of-its-kind incident happened, where attackers gained access to Dunkin Donuts’ DD Perks accounts as a result of credential stuffing.  

Investigation revealed that more than the credentials of users, attackers were after accounts themselves for sale on the dark web. Inference from the incident reemphasizes the importance of maintaining good password hygiene and the significance of a 2-step verification system. Besides, it is also recommended to monitor networks for sudden increased traffic and anomalies.  

LinkedIn’s Data Scraping Or Data Breach Incident 

This incident happened while humankind was at the crux of a global pandemic. Attackers exploited LinkedIn’s API to gain access to information of over 750mn users. This is almost 92% of the entire database of LinkedIn.  

A range of details was exploited including email addresses, contact details, geolocation records, profile URLs, genders, other social media accounts, work experiences and more. The inference here is to make websites and APIs secure right from their development stages. When cybersecurity is labeled as a phase of a process, it becomes weaker. When cybersecurity is approached as a mindset or a culture, it becomes part of behavior.  

Sociallark’s Incident Of 2021 

One of China’s fastest growing social media agencies witnessed a colossal blow when the accounts of over 200 million users were compromised on an attack. To start with, the agency’s server wasn’t encrypted, making the entire asset accessible to the public. This translates to the fact that any user with a basic understanding of IP functions can access leaked datasets. Over 200 million user accounts from LinkedIn, Instagram and Facebook were accessed. The inference here is too basic and makes more noise of lethargy than strategy.  

Final Thoughts 

Not just these, security breaches of Facebook, Marriot Hotels, Adobe, Sony Pictures, Canva and more are lessons in cybersecurity. While intentions behind every attack vary, target consistently seem to be access to sensitive user information.  

With the rise in AI tools and systems, tackling attacks and exploitations is more manageable now. All we need is an able team of cybersecurity professionals who understand the intricacies of digital security and are at par with the thought processes of attackers if not a step ahead. The right time to invest in transforming your current IT workforce to cybersecurity professionals is now. 

The first step you can take is talk to us for tailored programs on cybersecurity. With real-world case studies and hands-on experience modules, you’ll transform your current tech force into super-skilled cybersecurity experts.  

 What do you think? 

The post Expensive Enterprise Hacks That Serve As A Lesson In Cybersecurity appeared first on Jigsaw Academy.

While this often sounds exciting, what these sudden changes put an end to are existing conventions and practices. 

We are living at one such time. 

This is when new skills are hitting the market, newer work cultures are being introduced and a whole new spectrum of operational policies are being strategized.

A lot of skills that were in-demand a few years back are now extinct. Or to be precise, they have evolved into something more specific, challenging and mostly rewarding. With tech advancing by the day, it is indeed hard for businesses like yours to keep pace with it. 

More than keeping pace with it, businesses that adapt to changing trends and demands are the ones to seamlessly ride the way and consistently manage to sustain their spots. The perfect metaphorical example comes through our very own Apple iPod.

Photo by Christine Sandu on Unsplash

iPod Becomes Irrelevant After 20 Years Of Launch

If you’re as old as we are (not revealing the numbers here), we would all agree that we come from a generation that grew up flaunting a device dedicated to just music. It wasn’t the Walkman or portable music players that allowed us to listen to a maximum of 10 songs. This was different. For the first time, we were introduced to a new format called MP3 and unbelievably, we could store over 50 songs at once and have a digital interface to select preferred songs as well.

Futuristic it was. Iterations and variations followed and in 2007, a new and advanced music player hit the market from Apple. Arriving as a game-changing phenomenon in the consumer tech space, it redefined aesthetic and functional conditions with its features and sort of foreshadowed the onset of a whole new generation of smartphones called the iPhone.

It’s been 20 years since its launch and with its introduction, our auditory experience has never been the same. However, Apple recently announced that it’s shutting its shop of iPod Touch forever and that it will be available only till stocks last. This is the dusk of an era most of us grew up in. This announcement marks the end of a 20-year-old pioneer that will now move on to attain the status of vintage.

What’s The Takeaway Here?

When something new hits the market, it garners attention and probably commands respect if it is exceptional. It puts an end to previously existing entities and becomes a benchmark and competition.

Being a market disruptor or a game-changer is good but the attention fetched is short-lived. To stand the test of time, one needs to consistently adapt to changing trends and demands. We could draw parallels from the lifespan of the iPod and connect it to the skills of your workforce. It could be true that once upon a time, your talent pool’s skills were phenomenal and in demand but have they upgraded to stay relevant to cater to the dynamic needs of today?

The existing skills of your workforce could be similar to iPod Touch, where without their knowledge, they are getting a step closer to fading away from the market.

It’s on us stakeholders to enable talent transformation in our talent pool and ensure their skills and new competencies evolve. Just like how iPhones were spiritual sequels to the iPod Touch, the new skills of your workforce should evolve into something more to serve larger purposes and become future-proof.

What do you think?

Check out our talent transformation programs today or get in touch with us to personalize your journey in emerging technologies.

The post Available Only Till Stocks Last. Employable Only Till Skills Are Relevant appeared first on Jigsaw Academy.

NFTs are revolutionary.

Blockchain is super airtight.

Agreed.

However, amidst all the news on people becoming millionaires through NFTs and cryptocurrencies rewriting conventions, there are also news that are quite alarming – crypto scams.

Yes, with the world gradually adapting blockchain applications and concepts and several countries revisiting their policies on cryptocurrencies, this comes at the wrong time. Fortune also recently published an article that brings out the different types of crypto scams to rattle the internet, targeting people who are genuinely enthusiastic about riding the new wave.

From investment frauds to manipulating smart contracts, hackers and attackers are coming up with new ways to exploit vulnerabilities of the tech and people to swindle money’s worth. The social media acts as their playground, specifically Facebook and Instagram.

This only calls for more tech literacy and awareness through airtight cybersecurity measures. The more individuals know about the tech, the more they are aware of loopholes and vulnerabilities. So, this is the time for any organization to revisit their strategize and prioritize cybersecurity over anything else this year.

You can read the entire article here. 

The post Crypto Scams Row: How Safe Are Blockchains Actually? appeared first on Jigsaw Academy.

With its application across diverse industries, market segments and real-world concerns, the role of AI is becoming increasingly inevitable by the day. This is to the extent that we see AI as a savior to some of the most plaguing concerns of humankind.

For instance, climate change, gender-based wage gap, education and more.

While we have been only looking at the brighter side of AI’s role and contributions, we have conveniently overlooked the fact that all this comes at a cost. The cost that is affecting the very problem we intend to get solved by AI – climate change.

In this eye-opening article published by Indian Express, Trisha Ray sheds light on how AI has an impact on climate and how the application of AI by thousands of companies is actually harming the concept of sustainability.

This also brings us to how negligence, lack of awareness and commitment in the implementation of AI could ironically trigger consequences in the future. Ideally, the best way to implement AI is by having a 360-degree understanding of it and ensuring responsible or ethical AI is understood by every employee in an organization. Only when the practice is embedded in the mindset will the attitude and behaviors would change as well.

What do you think?

You can read the article here.

The post Is It Too Late To Talk About Responsible AI? appeared first on Jigsaw Academy.

Firstly, the demand and supply factors in the analytics job market have been inversely proportional. Meaning, there has been more demands in the market than available talent currently (close to 2x). Despite being a 12% contributor to data science jobs in the world, we have been still failing in filling up the vacancies for data scientists in companies around the country.

This does not end here. Industry veterans have more insights to share on the demand supply gap in analytics. Check out a panel discussion on this, which was conducted by Sarita Digumarti, the COO of Jigsaw Academy and is full of resourceful information.

A report from Gartner also points out the fact that there are over a million registered companies in the country, out of which over 75% of them will invest or already have invested in data science and machine learning. Besides, the Banking and Financial Services industry (BFSI) create the most job opportunities for aspirants, numbering close to 44%.

To help you understand the demand from each industry better, here’s a breakdown of them, the number of jobs they would create and their purpose of requirement.

Industry:

Healthcare

Vacancies:

15,000

Purpose

The estimate for the number of opportunities in healthcare is 15,000 mainly because of the increase in the amount of clinical data being generated. With Electronic Health Records (EHRs) gaining commendable momentum in the country and with the government bringing out new policies on its implementation and security, the need to analyse complex clinical data is increasingly becoming inevitable, in turn increasing the demand for data sientists.

Industry:

Autonomous Vehicles

Vacancies:

7000

Purpose

We’ve read about autonomous vehicles in the US with major market players working on prototypes. We’ve even seen a couple of videos on them, too. However, back in our country, there are several startups working on this technology as well. Establishments like Flux Auto, Fisheyebox, Swaayatt Robots, Auro Robotics and more are working on self-driving cars. Like you already know, self-driving cars are built on complex architecture of data collection, processing, decision making and other crucial elements. The involvement of such complexities call for equally efficient data handlers (analysts and scientists) to optimize the operations of cars.

Industry:

Agriculture

Opportunities:

5500

Considered as the backbone of Indian economy, we are yet to completely leverage the full potential of technology like the Internet of Things and data science to improve agriculture and harvests. With smart farming being implemented across multiple states (and with the government being a key role in its implementation), technologies like machine learning, data science and artificial intelligence are being incorporated to predict forecasting of crops, enhance farm output, optimize pricing and more. This is leading to the requirement of pro data scientists working at the backend.

Apart from these industries, cyber security, genomics and aviation are the ones contributing the growing demand for data scientists with 5000, 2000 and 4500 opportunities respectively.

With information about the vacancies in hand, what becomes inevitable is the ways to make ourselves the best fit for these roles. And we have to do this by understanding the roles, tools and skill sets. The report reveals that the following skills are essential to land a dream job in data science:

• Big data analytics
• Artificial Intelligence
• Machine Learning

As far as mastery over tools in the industry are concerned, Python is the most in-demand tool in the country, with startups and companies looking for people with sound hands on experience. There can’t be a better time to shift to a more lucrative career path with analytics. If you’re serious about a career shift, get started with analytics today. 

The post 50K Jobs Open for Data Scientists in India. Are You Ready? appeared first on Jigsaw Academy.

For the last few decades, artificial intelligence has been on the rise, and it’s played a crucial role in helping individuals and organizations do the impossible. Lending the powers of reason and judgment to robots has been one of the most staggering examples of AI. 

Businesses have been able to streamline most of their operations and cost-intensive activities and concentrate on addressing new and important challenges resulting from Artificial Intelligence and Machine Learning implementation. Here are some of the most common examples of AI and how it is now being utilized in our daily lives.

• Investment Control 

Companies rely on computers and data analysts to forecast potential demand trends. Stock trading is primarily dependent on the capacity to predict the future correctly. Machines excel at this since they can quickly crunch a large volume of info. 

Machines learn to recognize trends in historical data and forecast how these patterns could replicate in the future. Financial institutions are looking to artificial intelligence to enhance their stock exchange success and profits in an era of ultra-high-frequency trading. 

Nomura Securities, Japan’s largest brokerage firm, is one such company. For quite some time, the firm has been doggedly following a single objective, namely to use machines to study the insights of seasoned stock traders. 

Nomura is expected to soon launch a revolutionary stock exchange scheme after years of testing. Using the pool of data available, the computers can determine existing market dynamics as early as two weeks before and forecast how share rates will change a few minutes later. This enables traders to make more informed trading decisions based on predicted commodity values.

• Social Networking Sites 

Social networking sites are real-time examples of AI. There is an incalculable amount of data generated through conversations, tweets, and blogs, among other things. This large amount of data makes AI and Machine Learning models more intelligent and prone to making the right predictions. 

AI is used for face detection on social networking sites such as Facebook, where machine learning and deep learning algorithms are used to identify facial expressions and tag contacts. Using a slew of neural networks, deep learning is used to extract every bit of information from a picture. On the other hand, machine learning algorithms are used to personalize the feed. 

Another indicator is Twitter’s examples of AI technology used to detect hate speech and hate-inciting content in tweets. It filters out objectionable material using Machine Learning, Deep Learning, and Natural Language Processing. Twitter uncovered and blocked 300,000 accounts associated with terrorists, 95 per cent of which were discovered by artificially intelligent computers.

• Online Shopping with a Personalised Experience 

Personalization has been the new pantheon for many of the world’s top technology companies. eCommerce stores are not far from latching on to this trend, having been among the first to foray into the AI domain in the first place. The new artificial intelligence-centred apps curate a catalogue of purchasing tips and filtering options for consumers using AI-powered algorithms. 

They begin by examining the user’s most recent search history. Then, these AI algorithms generate a collection of items that meet the criterion of being valid or comparable and if consumers might be interested in seeing and possibly purchasing items in related and distinct categories. 

Artificial intelligence for example –

if a consumer purchases a device, the AI algorithms recommend different accessories for the same model, such as screen protectors, back covers, and earphones. If the algorithms are sufficiently intelligent, they may also recommend customizable items such as compatible computers and extended warranty plans. 

Users are presented with items that they might be interested in as a result of this personalization. Some examples of AI software include Amazon’s suggestions that illustrate how intelligent AI should be implemented in eCommerce.

• Surveillance 

Traditionally, surveillance cameras are monitored by a human operator. Humans are fallible, and any human error in this domain may be dangerous. Accidents are sure to occur as a result of the difficulty of monitoring several monitors concurrently. 

As with other examples of AI in daily life, AI can be implemented by controlled experiments, including designing authentication algorithms, recognition protocols, and much more to process data from security cameras. Eventually, AI may detect possible risks and warn human resources officers to conduct further investigations. 

AI has advanced considerably in the monitoring domain and can identify many risks, including intruders, unauthorized entry, and unidentified persons on designated premises. Though AI’s capabilities are minimal, it is projected to be a significant player in the surveillance domain globally over the next decade.

• Agriculture 

Agriculture has been one of the most recent AI applications and has also had a significant influence on the market. With an increased demand for food, organizations are using automation and robotics technologies embedded with AI to assist farmers in finding more effective ways of protecting their crops from various factors such as temperature, pests, and consumer usage patterns. 

Climate change, population increase, and questions about food sustainability have driven the industry to pursue more creative approaches to crop yield improvement and concentrated development. 

The most advanced examples of AI in agriculture are image recognition and recognizing potential crop defects using photographs taken by the user’s mobile camera. After identifying defects, users are presented with soil regeneration procedures, guides, and other possible solutions.

• Customer Support 

AI-enabled chatbots provide consumers with an immersive and intuitive way to find responses on their own. Chatbots comprehend the customer’s query and recommend the most appropriate response from their knowledge base. AI-enabled chatbots improve the user experience by detecting their purpose and following up with the appropriate query or message, resulting in a wonderful customer experience.

Chatbots never tire and will communicate with customers 24 hours a day, seven days a week. Furthermore, chatbots deployed in several languages help ensure that language is not an obstacle when communicating with customers.

• Smart Houses 

In the modern world, the most sophisticated examples of AI in real life have probably been smart homes, and they are getting smarter by the day. Numerous gadgets, such as smart locks, smart switches, etc. that are now increasingly compliant with one another have emerged. The implementation of smart homes has become easier than ever before.

Numerous mobile devices have emerged in recent years that can now recognize your activity and assist you in saving money by conserving electricity, recommending steps that can save you time and effort, and introducing cost-effective operations. These gadgets help you in leading a more efficient life.

Thermostats and construction control devices, examples of AI, assist with automating building heating and cooling. They learn and forecast when to switch on or off your boiler for maximum convenience while still considering outside weather conditions.

• Google Search 

Most of us cannot go a single day without using Google to look up an answer or a service we cannot live without. Without the help of artificial intelligence, search engines will be unable to crawl the whole internet to provide what you’re looking for. This is one of the strong AI examples. Those advertisements that appear to track your every move? Yes, they are all enabled by AI, based on your search experience, and aim to present you with things that the algorithms think you would enjoy.

• Send a Message or an Email 

We send and receive messages and emails throughout the day, whether they are personal or at work. When you write a text, tools such as Grammarly carry out spell checks, and grammar corrections activate to assist you in creating error-free texts. 

Artificial intelligence and natural language processing are included in these techniques. Spam filters on the receiving end of your communications use artificial intelligence to either block emails suspected of being spam or mark an email as something the users want to receive in their inbox. Examples of AI include the anti-virus tech that makes use of machine intelligence to safeguard an email address.

• Automotive Intelligence 

Self-driving vehicles are the most prevalent examples of AI technologies in the modern world, and they are becoming rapidly stable and dispatchable on a daily basis. From Google’s self-driving car initiative to Tesla’s “autopilot” mode, it’s just a matter of time before artificial intelligence becomes a regular component of the automobile industry. 

Advanced Deep Learning algorithms are capable of correctly predicting the behaviour of objects in the vehicle’s vicinity. The AI device gathers data from the vehicle’s radar, cameras, GPS, and cloud networks to generate control signals for the vehicle’s operation. Additionally, certain high-end cars also have AI-based parking solutions. Through the advancement in artificial intelligence, completely autonomous vehicles can shortly be available for public use.

• Enhancements of Images 

Cameras and apps use artificial intelligence to add various effects to photographs, improve their accuracy, and even recommend tools and additional filters you can use!

AI assists in recognizing objects in photographs and even optimizes the picture to its full potential by determining the depth, illumination, and focus. Users may use artificial intelligence features to blur the background, enhance the focus on a specific target, apply filters, and perform various other awesome experiments on the clicked picture. 

Examples of AI include how Google Photos uses AI to allow users to search for photos of specific individuals in their contact lists or tags. It recognizes the images of various individuals in your photographs and allows you to tag or look for them.

• Video Games

Artificial intelligence has been ingrained in the game business over the last few years. Indeed, one of AI’s most outstanding achievements is in the game business. 

The game industry is the perfect example of AI implementations of artificial intelligence because it is a medium that allows for a great deal of function variation. AI is used to plan the game, create the characters, and to some degree, frame the plot too.

• Medical Treatment 

The healthcare industry is an emerging leader in adopting AI technologies. It all comes down to AI’s ability to quickly crunch statistics and benefit from previous data, which is crucial in the healthcare sector. 

AI has made significant strides in assisting humans with health care. Automated bots and healthcare apps ensure patients get detailed prescriptions and services in hospitals. In medical examples of AI, AI applications have been known to support doctors during operations too.

• Travel 

Travel firms use artificial intelligence for a variety of projects. Apart from improving customer service, they are now using AI resources to determine pricing for many different locations by comparing historical and real-time data from the various open data silos. 

AI and machine learning technologies assist travel firms in determining profitable yet competitive pricing based on multiple criteria to draw a diverse consumer base. 

Additionally, in the travel business, uses of AI include assessing the amount of traffic on the roads and providing a correct trip forecast, and approximating the time required to travel to a specific location through various modes of transport, including buses, rails, and flights.

Conclusion

The evolution of artificial intelligence and deep learning systems has accelerated exponentially during the last decade. These innovations have emerged as critical requirements in various industries and continue to grow in their presence.

The day is not far when your refrigerator would have a better understanding of your lifestyle and suggest the foods you should consume. With the breakneck pace at which innovators have progressed in AI and the uses of artificial intelligence, it’s no wonder that AI is all set to become a mainstay of the tech industry.

There are no right or wrong ways of learning AI and ML technologies – the more, the better! These valuable resources can be the starting point for your journey how to learning Artificial Intelligence and Machine Learning. Do pursuing AI and ML interest you? If you want to step into the world of emerging tech, you can accelerate your career with the Machine Learning And AI Courses by Jigsaw Academy.

ALSO, READ

• Statistics for Machine Learning: A Basic Overview In 2021

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The truth is, we are nowhere close to such instances. Today, researchers and scientists are working on what is called general artificial intelligence, which is nothing but what we see in our everyday lives. Your voice assistant, for instance, is one of the best examples of artificial intelligence at work. If you had seen the videos on Amazon’s voice assistant, Alexa, you would know that these machines are designed to think, react, respond and behave like how we as humans would do. Though the gap between a human and a robot is too massive at the moment to be filled in, we are on the right track.

For those of you living under a rock, the best progress we have made so far in artificial intelligence is coming up with Sophia – world’s first robot to be given a citizenship in Dubai. She is smart and talks autonomously and even cracks sarcastic comments when out on a date with Will Smith. However, that is how far we have gone. Other instances of narrow artificial include the activities of systems such as playing chess (Deep Blue), coming up with purchase recommendations, face and fingerprint detection on smartphones, speech recognition and translation and more.

What we are trying to build, at least in the near future, is general AI. This is the part that the sci-fi movies showed us, where robots reach a certain stage they don’t need a human to give instructions. This involves refining thought processes and neurons at the finest levels to give the ability to a machine to think like humans. This comes with the use of technologies like machine learning, deep learning and neural networks. For the best comprehension, we will quickly see what machine learning is all about.

Machine Learning

Machine learning is a subset of AI, where we make use of algorithms and technologies like sensors and connected devices to teach the system to learn autonomously. Instead of feeding hundreds of instances to make a decision, we give machines the ability to filter out wrong decisions and choose the most appropriate ones (again like humans). But that is not as easy as it sounds. The working behind virtual voice assistants and any other technology we have talked about earlier are based on machine learning. To give the right response, Alexa has to understand if your question is genuine or sarcastic, filter out on replies that sound dumb and respond in a way that matches the tone of your question.

That is exactly why such assistants and tools take a little while to get used to you, your questions and get better at delivering better and appropriate results. On the coming articles, we are going to delve deeper into this topic. As of now, this distinction is what you need to get started and the video of Will Smith dating Sophia.

P.S. A quick trivia, Facebook shut down it’s AI wing when the machines started developing and learning a new and bizarre language autonomously.

If you want to build your future in Machine Learning & AI CLICK HERE.

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IoT or the Internet of Things is opening up newer avenues in the IT industry scene in India in tandem with Big Data and Data Science. However, innovations are still on the cards and are happening at places around the country, where countless experts and researchers are working on real-life concerns and coming up with technical solutions. Today, if IoT stands relevant to the Indian lifestyle, it has four big opportunities in front of it for innovation. Have a look.

Agriculture

As you know, the agriculture sector in India is in a bad shape. Being predominantly a country that depends on monsoon for crop irrigation, we desperately need a solution that could better crop yields, bring down water requirements and let farmers take better-informed decisions on what to harvest. With data and sensors, this is completely possible and in states like Andhra Pradesh, smart farming is a reality, where farmers are trained on using technology for farming. With smartphones, apps, sensors and controllers, farmers can control field conditions from remote and implement precision irrigation for optimized harvest.

Energy Consumption

One of the other biggest concerns we have in front of us is energy consumption. With growing population, we have lost track of energy consumption today and thanks to our lethargic attitude, we hardly care about the environment. In such cases, it is technology that has to offer an ideal solution and thankfully, it does. With IoT, it is easier to limit the consumption of electricity and fuel. Smart homes and smart illumination are all designed to automatically turn off when there is no one in a room and give real-time updates on energy consumption. This eliminates us from taking energy requirements for granted and paves the way to make informed decisions on electricity and fuel usage.

Healthcare

Of all the basic needs, healthcare is very crucial and it is saddening to note that there are tons of counterfeit medicines in the market out there. What’s the worse part? People are unaware of such medicines and intake them without their knowledge. This is a serious concern that IoT has the potential to solve. As a relieving factor, there are some companies working on offering genuine medicines by using IoT infrastructures and QR codes to assess the legitimacy of medicine purchases. If these reach more people, fake medicines can be permanently be done away with.

Transportation

Connected cars are increasingly making their ways to the market. High-end models now come connected to the internet and offer you a smartphone app for various usage. However, more innovation on this can be more beneficial to owners in terms of fuel usage, emissions per trip, notifications on a car or spare part malfunctioning, driver behaviour, speed limiter, geographic locations, door and trunk lock/unlock, social media sharing and more. In fact, features to add carpooling facilities can also have a huge impact on the environment and we are sure this would soon be a reality.

These were our thoughts on 4 big opportunities for IoT innovation and if you have something different in mind, share them over your comments.

The post 4 Biggest Opportunities for IoT Innovation appeared first on Jigsaw Academy.

As Big Data, analytics, Machine Learning and artificial intelligence started becoming mainstream, the term IoT also evolved and made its way into our everyday conversations. Now, most of us know what the technology is and how it has been influencing industries, companies, and our day-to-day lives. Some of us have even gained commendable exposure that we intend to switch our career to this booming industry.

As far as India is concerned, the IoT industry is gaining increased momentum, where researchers are turning to connected devices to carry out everyday tasks and companies are increasingly reinvesting on IoT for optimized performances. There have been several indigenous IoT devices that have their prototypes ready (Read Ramudroid) and the industry is now open to diverse experts such as IoT product engineers, IoT software developers, cloud architects, designers and more.

You would also be surprised to know that corporate giants like Amazon, Wipro, Cisco, IBM, Qualcomm and more are hiring IoT experts for various niche roles. Besides, there are several IoT-based startups that are making their way to becoming profitable ventures. However, to venture into this booming industry, you need more exposure to the pros and cons of IoT and in this article, we’ve compiled 5 crucial things you should know before you get started with IoT. So, no matter if you’re an IoT aspirant or a businessman looking to invest in IoT, these factors should help you out.

IoT Components

As we said, IoT is still not a flourished industry yet. It’s not like the other IT products and services such as smartphones, where you easily get access to the hardware peripherals required to build them. Though every IoT architecture boils down to just four components – devices, communication networks, cloud services, and applications – the market for the hardware components has not just flourished yet. If you go to some of the IoT meetups, you will understand that there are very few people who have confidently ventured into the IoT industry. Manufacturers of IoT devices are less and there’s also a gap in the standard pricing, availability and more. In the video, the co-creator of Ramu Droid shares the challenges faced by her in developing an IoT device. From funding to components, she shares how the indigenous market is yet to open up to the IoT market.

Skills and Manpower

Developing an end-to-end IoT solution isn’t easy. It requires a pool of talent that is multidisciplinary. Skills in designing embedded systems, cloud computing, cloud architecture, IoT application development, security designs, back-end management and data analytics are crucial. If you take a closer look, there aren’t colleges or courses that offer a degree program in IoT. It’s all specialization classes that take care of preparing individuals for the diverse roles in IoT.

So, if you’re an IoT aspirant, this is the best time to venture into the industry as the lack of talent will allow you to gain an edge over your competitors. You grow quicker and enjoy a paycheck that is ahead of the current salary trends. If you’re a company, you need to cross-train, upskill and bridge the skill gaps in your in-house talent pool and make the industry-ready.

IoT Security

If privacy in the digital space is a myth, security in IoT is speculative. Still in its initial stages, security is a major concern in IoT and there are no organizational or governing bodies to formulate data policies and regulations. Often, security in IoT devices are overlooked to accommodate more user-friendly features and added functionalities. With communication devices still vulnerable to hacks and brute-force entries, IoT security still needs to strengthen up.

Market-readiness

It’s a very niche and young market out there. As common people, we are in still awe of the technology’s potential. We share articles and videos on home automation systems and self-driving cars but when asked to sit in one personally, we would hesitate. For us, letting a machine take control of crucial things is still a major concern and that’s the thought process from a market perspective. Though IoT can assure of top-notch home security solutions, we still feel hesitant to leave our home under its control and supervision. Evolution in this will gradually happen as more people wake up to the technology’s worth. As the takers for IoT products and services increases, there will be better regulations, support, bug fixes, supervising bodies and more to cater to the demands of customers.

Google Glass was shelved and some automobile giants also silently scrapped their experiments on self-driving cars. The technology’s potential is much more than these shelved products and there are tons of researches and experiments going to bring out better variants of these ambitions. This technology is evolving, tackling and adapting the changes coming along its way.

Like any other technology, IoT is seeing a rise in its adoption and is requiring skilled people to help the technology evolve. The shortcomings in this industry are just temporary and researchers predict that it’s the next big technology to enter our everyday life. Lastly, this is the best time to venture into IoT. The blooming market is a blessing for any aspirant and if you’re serious about IoT, take the next step today.

Also Read

Top Uses of IoT

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When charting our path, we had several solid reasons on why this course would resonate with candidates and data science enthusiasts. And this post sums up our top 5 reasons. We believe these reasons are some of the factors you should consider while joining an offline data science course.

In-Person Classroom Learning with Extensive Access to Faculty

For some people, the best learning happens in a traditional classroom environment combined with live faculty interaction. This is how the idea of the Bootcamp program evolved – to suit professionals who were looking for a more personal learning experience and who wanted a more focused program that suited their time commitments.

Comprehensive End-to-End Learning

The bootcamp program packs in all the essential ingredients you require for a successful career in data science. You learn the concepts, the tools required, their practical applications and the trends in the industry. We would also train you on coding aspects required for the industry. By the end of the course, you would gain extensive business acumen, data science skills and the abilities to work on multiple programming languages and tools.

Predefined & Preset Intensive Timelines

With our online classes, we figured out that not many of us have the ability to channelize our interest and passion for career growth consistently through the duration of the course. Some of us need motivation on a weekly or a daily basis to get us going. That’s when we decided to bridge the gap between self-paced learning and mentored learning with our bootcamp program. The simple sight of other Data Science aspirants working on moving ahead in their career path is just the motivation some of us need to stay in our tracks as well. We decided to blend healthy competition and collective learning in our environment.

Weekend Classes

One of the major reasons why some of us tend to stay behind in our learning is because we don’t get to spend the required amount of time for the course. It may be due to work, work travel schedules or other personal reasons. We realized that weekends were the times aspirants dedicated to improving themselves. This was the perfect avenue for us to help out serious aspirants like you to surge ahead in your career goals.

Minimal Prerequisites

You only need to have studied stats and math until the higher secondary levels to join the program. All coding basics for data science would be taught during the program though past coding experience would be a plus.

Now that you have five real reasons why you should join the Jigsaw Academy full stack data science program, we feel the time is right to enrol today. You can get in touch with us here to get started with the bootcamp program. Here’s to the exciting career path that lies ahead!

The post 5 Reasons You Should Choose The Jigsaw Academy Full Stack Data Science Bootcamp appeared first on Jigsaw Academy.