Tech Guides

Cloud computing has risen massively in terms of popularity in recent times. This is due to the way it reduces on-premise infrastructure cost and improves efficiency. Primarily, the cloud model has been divided into three major service categories: Infrastructure as a Service (IaaS) Platform as a Service (PaaS) Software as a Service (SaaS) We will discuss each of these instances in the following sections: The article is an excerpt taken from the book 'Cloud Analytics with Google Cloud Platform', written by Sanket Thodge. Infrastructure as a Service (IaaS) Infrastructure as a Service often provides the infrastructure such as servers, virtual machines, networks, operating system, storage, and much more on a pay-as-you-use basis. IaaS providers offer VM from small to extra-large machines. The IaaS gives you complete freedom while choosing the instance type as per your requirements: Common cloud vendors providing the IaaS services are: Google Cloud Platform Amazon Web Services IBM HP Public Cloud Platform as a Service (PaaS) The PaaS model is similar to IaaS, but it also provides the additional tools such as database management system, business intelligence services, and so on. The following figure illustrates the architecture of the PaaS model: Cloud platforms providing PaaS services are as follows: Windows Azure Google App Engine Cloud Foundry Amazon Web Services Software as a Service (SaaS) Software as a Service (SaaS) makes the users connect to the products through the internet (or sometimes also help them build in-house as a private cloud solution) on a subscription basis model. Below image shows the basic architecture of SaaS model. Some cloud vendors providing SaaS are: Google Application Salesforce Zoho Microsoft Office 365 Differences between SaaS, PaaS, and IaaS The major differences between these models can be summarized to a table as follows: Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Software as a service is a model in which a third-party provider hosts multiple applications and lets customers use them over the internet. SaaS is a very useful pay-as-you-use model. Examples: Salesforce, NetSuite This is a model in which a third-party provider application development platform and services built on its own infrastructure. Again these tools are made available to customers over the internet. Examples: Google App Engine, AWS Lambda In IaaS, a third-party application provides servers, storage, compute resources, and so on. And then makes it available for customers for their utilization. Customers can use IaaS to build their own PaaS and SaaS service for their customers. Examples: Google Cloud Compute, Amazon S3 How PaaS, IaaS, and SaaS are separated at a service level In this section, we are going to learn about how we can separate IaaS, PaaS, and SaaS at the service level: As the previous diagram suggests, we have the first column as OPS, which stands for operations. That means the bare minimum requirement for any typical server. When we are going with a server to buy, we should consider the preceding features before buying. It includes Application, Data, Runtime, Framework, Operating System, Server, Disk, and Network Stack. When we move to the cloud and decide to go with IaaS—in this case, we are not bothered about the server, disk, and network stack. Thus, the headache of handling hardware part is no more with us. That's why it is called Infrastructure as a Service. Now if we think of PaaS, we should not be worried about runtime, framework, and operating system along with the components in IaaS. Things that we need to focus on are only application and data. And the last deployment model is SaaS—Software as a Service. In this model, we are not concerned about literally anything. The only thing that we need to work on is the code and just a look at the bill. It's that simple! If you found the above excerpt useful, make sure to check out the book 'Cloud Analytics with Google Cloud Platform' for more of such interesting insights into Google Cloud Platform. Read more Top 5 cloud security threats to look out for in 2018 Is cloud mining profitable? Why AWS is the prefered cloud platform for developers working with big data?

The rise of the bots is nigh! If you can imagine a situation involving a dialog, there is probably a chatbot for that. Just look at the chatbot market - text-based email/SMS bots, voice-based bots, bots for customer support, transaction-based bots, entertainment bots and many others. A large number of enterprises, from startups to established organizations, are seeking to invest in this sector. This has also led to an increase in the number of platforms used for chatbot building. These frameworks incorporate AI techniques along with natural language processing capabilities to assist developers in building and deploying chatbots. Let’s start with how a chatbot typically works before diving into some of the frameworks. Understand: The first step for any chatbot is to understand the user input. This is made possible using pattern matching and intent classification techniques. ‘Intents’ are the tasks that users might want to perform with a chatbot. Machine learning, NLP and speech recognition techniques are typically used to identify the intent of the message and extract named entities. Entities are the specific pieces of information extracted from the user’s response i.e. the content associated with an intent. Respond: After understanding, the next goal is to generate a response. This is based on the current input message and the context of the conversation. After specifying the intents and entities, a dialog flow is constructed. This is basically the replies/feedback expected from a chatbot. Learn: Chatbots use AI techniques such as natural language understanding and pattern recognition to store and distinguish between the context of the information provided, and elicit a suitable response for future replies. This is important because different requests might have different meanings depending on previous requests. Top chatbot development frameworks A bot development framework is a set of predefined classes, functions, and utilities that a developer can use to build chatbots easier and faster. They vary in the level of complexity, integration capabilities, and functionalities. Let us look at some of the development platforms utilized for chatbot building. API.AI API.AI, a code based framework with a simple web-based interface, allows users to build engaging voice and text-based conversational apps using a large number of libraries and SDKs including Android, iOS, Webkit HTML5, Node.js, and Python API. It also supports nearly 32 one-click platform integrations such as Google, Facebook Messenger, Twitter and Skype to name a few. API.AI makes use of an agent - a container that transforms natural language based user requests into actionable data. The software tries to find the intent behind a user’s reply and matches it to the default or the closest match. After intent matching, it executes the actions and responses the developer has defined for that intent. API.AI also makes use of entities. Once the intents and entities are specified, the bot is trained. API.AI’s training module efficiently tracks each user’s request and lets developers see how they are parsed and matched to an intent. It also allows for correction of any errors and change requests thus retraining the bot. API.AI streamlines the entire bot-creating process by helping developers provide domain-specific knowledge that is unique to a bot’s needs while working on speech recognition, intent and context management in the backend. Google has recently partnered with API.AI to help them build conversational tools like Apple’s Siri. Microsoft Bot Framework Microsoft Bot Framework allows building and deployment of chatbots across multiple platforms and services such as web, SMS, non-Microsoft platforms, Office 365, Skype etc. The Bot Framework includes two components - The Bot Builder and the Microsoft Cognitive Services. The Bot Builder comprises of two full-featured SDKs - for the.NET and the Node.js platforms along with an emulator for testing and debugging. There’s also a set of RESTful APIs for building code in other languages. The SDKs support features for simple and easy interactions between bots. They also have a large collection of prebuilt sample bots for the developer to choose from. The Microsoft Cognitive Services is a collection of intelligent APIs that simplify a variety of AI tasks such as allowing the system to understand and interpret the user's needs using natural language in just a few lines of code. These APIs allow integration to most modern languages and platforms and constantly improve, learn, and get smarter. Microsoft created the AI Inner Circle Partner Program to work hand in hand with industry to create AI solutions. Their only partner in the UK is ICS.AI who build conversational AI solutions for the UK's public sector. ICS are the first choice for many organisations due to their smart solutions that scale and serve to improve services for the general public. Developers can build bots in the Bot Builder SDK using C# or Node.js. They can then add AI capabilities with Cognitive Services. Finally, they can register the bots on the developer portal, connecting it to users across platforms such as Facebook and Microsoft Teams and also deploy it on the cloud like Microsoft Azure. For a step-by-step guide for chatbot building using Microsoft Bot Framework, you can refer to one of our books on the topic. Sabre Corporation, a customer service provider for travel agencies, have recently announced the development of an AI-powered chatbot that leverages Microsoft Bot Framework and Microsoft Cognitive Services. Watson Conversation IBM’s Watson Conversation helps build chatbot solutions that understand natural-language input and use machine learning to respond to customers in a way that simulates conversations between humans. It is built on a neural network of one million Wikipedia words. It offers deployment across a variety of platforms including mobile devices, messaging platforms, and robots. The platform is robust and secure as IBM allows users to opt out of data sharing. The IBM Watson Tone Analyzer service can help bots understand the tone of the user’s input for better management of the experience. The basic steps to create a chatbot using Watson Conversation are as follows. We first create a workspace - a place for configuring information to maintain separate intents, user examples, entities, and dialogues for each application. One workspace corresponds to one bot. Next, we create Intents. Watson Conversation makes use of multiple conditioned responses to distinguish between similar intents. For example, instead of building specific intents for locations of different places, it creates a general intent “location” and adds an entity to capture the response, like the “location- bedroom” - to the right, near the stairs, “location-kitchen”- to the left. The third step is entity establishment. This involves grouping entities that might trigger a similar response in the dialog. The dialog flow, thus generated after specifying the intents and entities, goes through testing followed by embedding this into an application. It is then connected with other services by using the conversation API. Staples, an office supply retailing firm, uses Watson Conversation in their “Easy Systems” to simplify the customer’s shopping experience. CXP Designer and Aspect NLU Aspect Customer Experience Platform is an application lifecycle management tool to build text and voice-based applications such as chatbots. It provides deployment options across multiple communication channels like text, voice, mobile web and social media networks. The Aspect CXP typically includes a CXP designer to build chatbots and the inbuilt Aspect NLU to provide advanced natural language capabilities. CXP designer works by creating dialog objects to provide a menu of options for frontend as well as backend. Menu items for the frontend are used to create intents and modules within those intents. The developer can then modify labels (of those intents and modules) manually or use the Aspect NLU to disambiguate similar questions for successful extraction of meaning and intent. The Aspect NLU includes tools for spelling correction, linguistic lexicons such as nouns, verbs etc. and options for detecting and extracting common data types such as date, time, numbers, etc. It also allows a developer to modify the meaning extraction based on how they want it if they want it! CXP designer also allows skipping of certain steps in chatbots. For instance, if the user has already provided their tracking id for a particular package, the chatbot will skip the prompt of asking them the tracking id again. With Aspect CXP, developers can create and deploy complex chatbots. Radisson Blu Edwardian, a hotel in London, has collaborated with Aspect software to build an SMS based, AI virtual host. Conclusion Another popular chatbot development platform worth mentioning is the Facebook messenger with over 100,000 monthly active bots, but without cross-platform deployment features. The above bot frameworks are typically used by developers to build chatbots from scratch and require some programming skills. However, there has been a rise in automated bot development tools of late. Some of these include Chatfuel and Motion AI and typically involve drag and drop functionalities. With such tools, beginners and non-programmers can create and deploy chatbots within few minutes. But, they lack the extended functionalities supported by typical code based frameworks such as the flexibility to store data, produce analytics or incorporate customized AI tasks. Every chatbot development system, whether framework or tool, serves a different purpose. Choosing the right one depends on the type of application to build, organizational needs, and the developer’s expertise.

Many cloud service providers have been marching towards adopting IoT (Internet of Things) services to attract more customers. This league includes top cloud merchants such as AWS, Microsoft Azure, IBM, and much recently, Google. Among these, Amazon Web Services have been the most popular. Its AWS IoT Core service is a fully-managed cloud platform that provides IoT devices with an easy and secure connection to interact with cloud applications and other IoT devices. AWS IoT Core can keep track of billions of IoT devices, with the messages travelling to and from them. It processes and routes those messages to the AWS endpoints and to other devices reliably and securely. This means, with the help of AWS IoT Core, you can keep track of all your devices and have a real-time communication with them. Undoubtedly, there is a lot of competition around cloud platforms to host IoT services. Users are bound to a specific cloud platform for a varied set of reasons such as a yearly subscription, by choice, or other reasons. Here are 5 reasons to choose AWS IoT core for your IoT projects: Build applications on the platform of your choice with AWS IoT Core Device SDK AWS IoT Core Device SDK is the primary mode of connection between your application and the AWS IoT core. It uses the MQTT, HTTP, or webSockets protocols to effectively connect and exchange messages with this service. The languages supported by the AWS IoT device SDK are C, Arduino, and JavaScript. The SDK provides developers with mobile SDKs for Android and iOS, and a bunch of SDKs for Embedded C, Python and many more. It also includes open-source libraries, developer guides with samples, and porting guides. With these features, developers can build novel IoT products and solutions on the hardware platform of their choice. AWS IoT Summit 2018 held recently in Sydney shed light on cloud technologies and how it can help businesses lower costs, improve efficiency and innovate at scale. It had sessions dedicated to IoT. (Intelligence of Things: IoT, AWS DeepLens, and Amazon SageMaker) Handle the underlying infrastructure and protocol support with Device Gateway The device gateway acts as an entry gate for IoT devices to connect to the Amazon Web Services (AWS). It handles multiple protocols, which ensures secure and effective connection of the IoT devices with the IoT Core. The list of protocols include MQTT, WebSockets, and HTTP 1.1. Also, with the device gateway, one does not have to worry about the infrastructure as it automatically manages and scales huge amount of devices at ease. Authentication and Authorization is now easy with AWS methods of authentication AWS IoT Core supports SigV4, an AWS method of authentication, X.509 certificate based authentication, and customer created token based authentication. The user can create, deploy and manage certificates and policies for the devices from the console or using the API. AWS IoT Core also supports connections from users’ mobile apps using Amazon Cognito, which creates a unique ID for app users and can be used to retrieve temporary, limited-privilege AWS credentials. AWS IoT Core also enables temporary AWS credentials after a device has authenticated with an X.509 certificate. This is done so that the device can more easily access other AWS services such as DynamoDB or S3. Determine device’s current state automatically with Device Shadow Device shadow is a JSON document, which stores and retrieves the current state for a device. It provides persistent representations such as the last reported state and the desired future state of one’s device even when the device is offline. With Device Shadow, one can easily build applications to interact with the applications by providing REST APIs. It aids applications to set their desired future state without having to request for device starting state. AWS IoT core differentiates between the desire state and the last reported state. It can further command the device to make up the difference. Route messages both internally and externally using AWS Rules Engine The Rules Engine helps build IoT applications without having to manage any infrastructure. Based on the rules defined, the Rules engine evaluates all the incoming messages within the AWS IoT Core, transforms it, and delivers them to other devices or cloud services. One can author or write rules within the management console using the SQL-like syntax The Rules Engine can also route messages to AWS endpoints such as AWS Lambda, Amazon Kinesis, Amazon S3, Amazon Machine Learning, Amazon DynamoDB, Amazon CloudWatch, and Amazon Elasticsearch Service with built-in Kibana integration. It can also reach external endpoints using AWS Lambda, Amazon Kinesis, and Amazon Simple Notification Service (SNS). There are many other reasons to choose AWS IoT Core for your projects. However, it is purely one’s choice as many might already be using or bound to other cloud services. For those, who haven’t yet started, they may choose AWS for a plethora of other cloud services that they offer, which includes AWS IoT Core too.  

Java has been a master of all in almost every field of application development, making the Java developers not wander much in search for other languages. However, things have changed with the steady evolution of Kotlin. Kotlin, no more the "other JVM language" has even surpassed Java's prominence . So,what makes this language stand-out and why is it growing in adoption for application development? What are the benefits of Kotlin vs Java, and how can it help developers? In this article, we’re going to look at the top 5 reasons why Kotlin takes a superior stand over Java and why it will work best for your next development project. Kotlin is more concise Kotlin is way more concise than Java in many cases, solving the same problems with fewer lines of code. This improves code maintainability and readability, meaning engineers can write, read, and change code more effectively and efficiently. Kotlin exclusive features such as type inference, smart casts, data classes, and properties help achieve conciseness. Kotlin’s null-safety is great NullPointerExceptions are a huge source of frustration for Java developers. Java allows you to assign null to any variable, but if you try to use an object reference that has a null value, then brace yourself to encounter a NullPointerException! Kotlin’s type system is aimed to eliminate NullPointerExceptions from the code. This type of system helps to avoid null pointer exceptions by simply refusing to compile code that tries to assign or return null. Combine the best of Functional and Procedural Programming Each set of programming paradigm has its own set of pros and cons. Combining the power of both functional and procedural programming leads to better development and output. It consists of many useful methods, which includes higher-order functions, lambda expressions, operator overloading, lazy evaluation, and much more. With a list of weaknesses and strengths from both languages, Kotlin offers inexpensive and intuitive coding style. The power of Kotlin’s extension functions Extensions of Kotlin are very useful because they allow developers to add methods to classes without making changes to their source code. Here, you can add methods on a per-user basis to classes. This allows users to extend the functionality of existing classes without inheriting the functions and properties from other classes. Interoperability with JAVA When debating between Kotlin vs Java, there is always a third option: Use them both. Despite all the differences Kotlin and Java are 100% interoperable,you can literally continue work on your old Java projects using Kotlin. You can call Kotlin code from Java, and you can call Java code from Kotlin. So it’s possible to have Kotlin and Java classes side-by-side within the same project, and everything will still compile. Undoubtedly, Kotlin has made many positive changes to the long and most used Java. It helps to write safer code, because with less work it's possible to write a more reliable code, thus making the life of programmers a lot easier. Kotlin is really a good replacement for Java. With time, more and more advanced features will be added to the Kotlin’s ecosystem that will help its popularity to grow towards its apex making the developers world more promising. Also read Why are Android developers switching from Java to Kotlin? Getting started with Kotlin programming  

Is data science important? It's a term that's talked about a lot but often misunderstood. Because it's a buzzword it's easy to dismiss; but data science is important. Behind the term lies very specific set of activities - and skills - that businesses can leverage to their advantage. Data science allows businesses to use the data at their disposal, whether that's customer data, financial data or otherwise, in an intelligent manner. It's results should be a key driver of growth. However, although it’s not wrong to see data science as a real game changer for business, that doesn’t mean it’s easy to do well. In fact, it’s pretty easy to do data science badly. A number of reports suggest that a large proportion of analytics projects fail to deliver results. That means a huge number of organizations are doing data science wrong. Key to these failures is a misunderstanding of how to properly utilize data science. You see it so many times - buzzwords like data science are often like hammers. They make all your problems look like nails. And not properly understanding the business problems you’re trying to solve is where things go wrong. What is data science? But what is data science exactly? Quite simply, it’s about using data to solve problems. The scope of these problems is huge. Here are a few ways data science can be used: Improving customer retention by finding out what the triggers of churn might be Improving internal product development processes by looking at points where faults are most likely to happen Targeting customers with the right sales messages at the right time Informing product development by looking at how people use your products Analyzing customer sentiment on social media Financial modeling As you can see data science is a field that can impact every department. From marketing to product management to finance, data science isn’t just a buzzword, it’s a shift in mindset about how we work. Data science is about solving business problems To anyone still asking is data science important, the answer is actually quite straightforward. It's important because it solves business problems. Once you - and management - recognise that fact, you're on the right track. Too often businesses want machine learning, big data projects without thinking about what they’re really trying to do. If you want your data scientists to be successful, present them with the problems - let them create the solutions. They won’t want to be told to simply build a machine learning project. It’s crucial to know what the end goal is. Peter Drucker once said “in God we trust… everyone else must bring data”. But data science didn’t really exist then - if it did it could be much simpler: trust your data scientists.

Are we DevOps now? The system-wide software development methodology that breaks down the problematic divide between development and operations is still in that stage where enterprises implementing the idea are probably asking that question, working out whether they've reached the endgame of effective collaboration between the two spheres and a systematic automation of their IT service infrastructure. Considered to be the natural evolution of Agile development and practices, the idea and business implementation of DevOps is rapidly gaining traction and adoption in significant commercial enterprises and we're very likely to see a saturation of DevOps implementations in serious businesses in the near future. The benefits of DevOps for scaling and automating the daily operations of businesses are wide-reaching and becoming more and more crucial, both from the perspective of enabling rapid software development as well as delivering products to clients who demand and need more and more frequent releases of up-to-date applications. The movement towards DevOps systems moves in close synchronization with the growing demand for experiencing and accessing everything in real time, as it produces the level of infrastructural agility to roll out release after release with minimal delays. DevOps has been adopted prominently by hitters as big as Spotify, who have embraced the DevOps culture throughout the formative years of their organization and still hold this philosophy now. The idea that DevOps is an evolution is not a new one. However, there’s also the argument to be made that the actual evolution from a non-DevOps system to a DevOps one entails a revolution in thinking. From a software perspective, an argument could be made that DevOps has inspired a minor technological revolution, with the spawning of multiple technologies geared towards enabling a DevOps workflows. Docker, Chef, Puppet, Ansible, and Vagrant are all powerful key tools in this space and vastly increase the productivity of developers and engineers working with software at scale. However, it is one thing to mobilize DevOps tools and implement them physically into a system (not easy in itself), but it is another thing entirely to revolve the thinking of an organization round to a collaborative culture where developers and administrators live and breathe in the same DevOps atmosphere. As a way of thinking, it requires a substantial cultural overhaul and a breaking down of entrenched programming habits and the silo-ization of the two spheres. It's not easy to transform the day-to-day mind-set of a developer so that they incorporate thinking in ops (monitoring, configuration, availability) or vice versa of a system engineer so they are thinking in terms of design and development. One can imagine it is difficult to cultivate this sort of culture and atmosphere within a large enterprise system with many large moving parts, as opposed to a startup which may have the “day zero” flexibility to employ a DevOps approach from the roots up. To reach the “state” of DevOps is a long journey, and one that involves a revolution in thinking. From a systematic as well as cultural point of view, it takes a considerable degree of ground breaking in order to shatter (what is sometimes) the monolithic wall between development and operations. But for organizations that realize that they need the responsiveness to adapt to clients on demand and have the foresight to put in place system mechanics that allow them to scale their services in the future, the long term benefits of a DevOps revolution are invaluable. Continuous and automated deployment, shorter testing times, consistent application monitoring and performance visibility, flexibility when scaling, and a greater margin for error all stem from a successful DevOps implementation. On top of that a survey showed that engineers working in a DevOps environment spent less time firefighting and more productive time focusing on self-improvement, infrastructure improvement, and product quality. Getting to that point where engineers can say “we’re DevOps now!” however is a bit of a misconception, because it’s more than a matter of sharing common tools, and there will be times where keeping the bridge between devs and ops stable and productive is challenging. There is always the potential that new engineers joining an organization can dilute the DevOps culture, and also the fact that DevOps engineers don't grow overnight. It is an ongoing philosophy, and as much an evolution as it is a revolution worth having.

GDPR is an acronym that has been doing the rounds for a couple of years now. It’s become even more visible in the last few weeks, thanks to the Facebook and Cambridge Analytica data hijacking scandal. And with the deadline date looming - 25 May 2018 - every organization on the planet needs to make sure their on top of things. But what is GDPR exactly? And how is it going to affect you? What is GDPR? Before April, 2016, a data protection directive enforced in 1995 was in place. This governed all organisations that dealt with collecting, storing and processing data. This directive became outdated with rapidly evolving technological trends, which meant a revised directive was needed. In April 2016, the European Union drew up General Data Protection Regulation. It has been specifically created to to protect the personal data and privacy of European citizens. It's important to note at this point that the directive doesn't just apply to EU organizations - it applies to anyone who deals with data on EU citizens. A relatively new genre of crime involving stealing data, has cropped up over the past decade. Data is so powerful, that its misuse could be devastating, possibly resulting in another world war. GDPR aims to set a new benchmark for the protection of consumer data rights by making organisations more accountable. Governed by GDPR, organisations will now be responsible for guarding every quantum of information that is connected to an individual, including IP addresses and web cookies! Read more: Why GDPR is good for everyone. Why should organizations bother with GDPR? In December 2017, the RSA, one of the first cryptosystems and security organisations, surveyed 7,500 customers in France, Italy, Germany, the UK and the US, and the results were interesting. When asked what their main concern was, customers responded that lost passwords, banking information, passports and other important documents were their major concern. The more interesting part was that over 60% of the respondents said that in the event of a breach, they would blame the organisation that lost their data rather than the hacker. If you work for or own a company that deals with the data of EU citizens, you’ll probably have GDPR on your radar. If you don’t comply, you’ll face a hefty fine - more on that below. What kind of data are we talking about? The GDPR aims to protect data related to identity information like name, physical address, sexual orientation and more. It also covers any ID numbers; IP addresses, cookies and RFID tags; genetic and any data related to health; biometric data like fingerprints, retina scans, etc; racial or ethnic data; political opinions. Who must comply with GDPR? You’ll be governed by GDPR if: You’re a company located in the EU You’re not located in the EU but you still process data of EU citizens You have more than 250 employees You have lesser than 250 employees but process data that could impact the rights and freedom of EU citizens When does GDPR come into force? In case you missed it in the first paragraph, GDPR comes into effect on 25 May 2018. If you're not ready yet, now is the time to scramble to get things right and make sure you comply with GDPR regulations. What if you don’t make the date? Unlike an invitation to a birthday party, if you miss the date to comply with the GDPR, you’re likely to be fined to the tune of €20 million or 4% of the worldwide turnover of your company. A more relaxed fine includes €10 million or 2% of the worldwide turnover of your company, for misusing data in ways involving failure to report a data breach, failure to incorporate privacy by design and failure to ensure that data protection is applied at the initial stage of a project. It also includes the failure to hire a Data Protection Officer/Chief Data Officer, who has professional experience and knowledge of data protection laws that are proportionate to what the organisation carries out. If it makes you feel any better, you’re not the only one. A report from Ovum states that more than 50% of the companies feel they’re most likely to be fined for non compliance. How do you prepare for GDPR? Well, here are a few honest steps that you could perform to ensure a successful compliance: Prepare to shell out between $1 million to $10 million to meet GDPR requirements Hire a DPO or a CDO who’s capable of handling all your data policies and migration Fully understand GDPR and its requirements Perform a risk assessment, understand what kind of data you store and what implications it might have Strategize to mitigate that risk Review/Create your data protection plan Plan for a 72 hour incident response system Implement internal plans and policies to ensure employees follow For the third time then - time is running out! It’s imperative that you ensure your organisation complies with GDPR before the 25th of May, 2018. We’ll follow up with some more thoughts to help you make the shift, as well as give you more insight into this game changing regulation. If you own or are part of an organisation that has migrated to comply with GDPR, please share some tips in the comments section below to help others still in the midst of the transition.

Introduction In the data-driven world of today, data engineering has emerged as one of the most promising and rewarding careers in the tech industry. As businesses place greater emphasis on data-driven decision-making, the need for skilled data engineers is surging. In this article, we’ll explore why data engineering is a lucrative career, why you should consider pursuing it, and the essential skills required to get started. Additionally, we’ll suggest some in-house tech books that can guide you on your journey to becoming a successful data engineer. What Does a Data Engineer Do? A data engineer is responsible for building, maintaining, and optimizing data pipelines and architectures that ensure data is easily accessible for analysts and data scientists. Their key duties include: Designing and constructing scalable systems to collect, store, and transform large datasets. Ensuring data reliability, security, and efficiency. Collaborating with data scientists and analysts to understand their data needs. Managing data workflows and automating data collection processes. Developing systems that enable businesses to store and analyze data in a structured manner. They essentially create the infrastructure that allows businesses to harness data for insights, machine learning models, and decision-making. If a company needs to process huge amounts of data—whether for improving user experience, predicting trends, or optimizing operations—a data engineer makes that possible. Why Data Engineering is a Lucrative Career: Key Statistics (source) Data Growth: The global datasphere is projected to reach 491 zettabytes by 2027, with an estimated 175 zettabytes expected by 2025. This exponential growth emphasizes the critical role of data engineers in managing and processing vast amounts of information.  Financial Impact of Data Quality: Poor data quality costs global businesses approximately $15 million annually, highlighting the importance of effective data engineering in ensuring high-quality data access and usability. Salary Projections: By 2026, the median salary for data engineers in the U.S. is projected to reach around $170,000, indicating a lucrative career path due to high demand and limited supply of qualified professionals. Talent Gap: The anticipated talent gap in data engineering is expected to reach 2.9 million job vacancies globally, underscoring the urgent need for skilled data engineers. Market Growth: The global big data market is expected to grow significantly, projected to reach $274.3 billion by 2028, with a compound annual growth rate (CAGR) of 23.8% from 2023 to 2028 (source). Essential Skills Required to Get Started in Data Engineering To embark on a successful career in data engineering, you’ll need to acquire a combination of technical skills and domain knowledge. Here are the key skills you should focus on, along with suggested in-house tech books to help you master them: 1. Programming Languages Mastering languages like Python or Java is essential for data engineers to build, automate, and maintain scalable data pipelines.Book: Data Engineering with Python Product Information: This book is a comprehensive introduction to building data pipelines, that will have you moving and transforming data in no time. You'll learn how to build data pipelines, transform and clean data, and deliver it to provide value to users. You will learn to deploy production data pipelines that include logging, monitoring, and version control. Author: Paul Crickard Book: Data Engineering with Scala and Spark Product Information: Learn new techniques to ingest, transform, merge, and deliver trusted data to downstream users using modern cloud data architectures and Scala, and learn end-to-end data engineering that will make you the most valuable asset on your data team. Authors: Eric Tome, Rupam Bhattacharjee, David Radford 2. SQL and Database Management Strong SQL skills enable efficient querying, storage, and retrieval of structured data, making database management crucial for optimizing data workflows.Book: SQL Query Design Patterns and Best Practices Product Information: SQL Query Design Patterns and Best Practices is a practical guide to making queries more efficient and maintainable. This book will help you improve your skills for writing complex queries, formatting, optimizing common query issues, and applying new techniques such as windowing and time-series functionality. Author: Steve Hughes, Dennis Neer, Dr. Ram Babu Singh, Shabbir H. Mala, Leslie Andrews, Chi Zhang Book: Mastering MongoDB 7.0 Product Information: Explore the full potential of MongoDB 7.0 with this comprehensive guide that offers powerful techniques for efficient data manipulation, application integration, and security. This intermediate-to-advanced level book empowers you to harness the power of the latest version of MongoDB. Author: Marko Aleksendrić, Arek Borucki, Leandro Domingues, Malak Abu Hammad, Elie Hannouch, Rajesh Nair, Rachelle Palmer 3. Big Data Technologies Knowledge of Hadoop, Spark, and other Big Data tools helps in handling and processing massive datasets efficiently, which is critical for scalability.Book: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Product Information: Discover the roadblocks you may face in data engineering and keep up with the latest trends such as Delta Lake. This book will help you learn how to build data pipelines that can auto-adjust to changes. Using practical examples, you will implement a solid data engineering platform that will streamline data science, ML, and AI tasks. Author: Manoj Kukreja Book: Mastering Hadoop 3 Product Information: This is a comprehensive guide to understand advanced concepts of Hadoop ecosystem. You will learn how Hadoop works internally, and build solutions to some of real world use cases. Finally, you will have a solid understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable Big Data pipeline. Author: Chanchal Singh, Manish Kumar 4. Data Warehousing and ETL Expertise in ETL processes and data warehousing ensures clean, organized data is available for analysis, improving data accessibility and decision-making.Book: Data Modeling with Snowflake Product Information: Modeling guides are often steeped in theory and formal language. The innovative approach taken in Data Modeling with Snowflake combines practical modeling concepts with Snowflake best practices and unique features, allowing you to create efficient designs that leverage the power of the Data Cloud. Author: Serge Gershkovich  Book: Data Exploration and Preparation with BigQuery Product Information: Data Exploration and Preparation with BigQuery is a comprehensive guide to working with data preparation tools and strategies using BigQuery as a modern data warehouse solution. Through hands-on exercises and projects, you’ll leverage your BigQuery proficiency to overcome common challenges faced by data analysts. Author: Mike Kahn  5. Cloud Computing Proficiency in cloud platforms like AWS, GCP, or Azure is vital for modern data engineering, enabling scalable storage, processing, and real-time data management.Book: Azure Data Factory Cookbook - Second Edition Product Information: T With the help of well-structured and practical recipes, this book will teach you how to integrate data from the cloud and on-premises. You’ll learn how to transform, clean, and consolidate data into a single data platform and get to grips with ADF Author: Dmitry Anoshin ,Dmitry Foshin , Tonya Chernyh, Xenia Ireton Book: Data Engineering with Google Cloud Platform - Second Edition Product Information: This book will help you delve into data governance on Google Cloud. Moreover, you’ll also cover the latest technological advancements in the domain and be able to build and deploy data pipelines confidently. Author: Adi Wijaya 6. Data Governance and Security Understanding data governance and security protocols ensures that data is managed legally and securely, protecting sensitive information and maintaining compliance.Book: Data Governance Handbook Product Information: This book provides a highly focused view of real business outcomes powered by data governance, that resonate with non-data executives such as CFOs and CEOs. You’ll also find useful insights into how to implement data governance initiatives. Author: Wendy S. Batchelder Conclusion Data engineering is not only a lucrative career but also a field full of opportunities for growth and specialization. With the right skills and continuous learning, you can position yourself for success in this dynamic and rewarding profession. If you’re ready to embark on your data engineering journey, the in-house tech books mentioned above are excellent resources to help you get started. Whether you’re just beginning or looking to advance your career, now is the perfect time to dive into the world of data engineering. 

An issue that plagues DevOps is the lack of a clearly defined definition. A Google search displays results that state that DevOps is empathy, culture, or a movement. There are also derivations of DevOps like ChatOps and HugOps. A lot of speakers mentions DEVOPS but no-one seemed to have a widely agreed definition of what DEVOPS actually means. — Stephen Booth (@stephenbooth_uk) November 19, 2015 Proposed Definition of DevOps: "Getting more done with fewer meetings." — DevOps Research (@devopsresearch) October 12, 2015 The real head-scratchers are the number of job postings for DevOps Engineers and the number of certifications for DevOps that are popping up all over the web. The job title Software Engineer is contentious within the technology community, so the job title DevOps engineer is just begging to take pointless debates to a new level. How do you create a curriculum and certification course that has any significant value on an unclear subject? For a methodology that has an emphasis on people, empathy, communications, it falls woefully short heeding its own advice and values. On any given day, you can see the meaning debated on blog posts and tweets. My current understanding of DevOps and why it exists DevOps is an extension of the agile methodology that is hyper-focused on bringing customers extraordinary value without compromising creativity (development) or stability (operations). DevOps is from the two merged worlds of Development and Operations. Operations in this context include all aspects of IT such as system administration, maintenance, etc. Creation and stability are naturally at odds with each other. The ripple effect is a good way to explain how these two concepts have friction. Stability wants to keep the pond from becoming turbulent and causing harm. Creation leads to change which can act as a random rock thrown into the water sending ripples throughout the whole pond, leading to undesired side effects that causes harm to the whole ecosystem. DevOps seeks to leverage the momentum of controlled ripples to bring about effective change without causing enough turbulence to impact the whole pond negatively. The natural friction between these two needs often drives a wedge between development and operations. Operations worry that a product update may include broken functionality that customers have come to depend on, and developers worry that sorely needed new features may not make it to customers because of operation's resistance to change. Instead of taking polarizing positions, DevOps is focused on blending those two positions into a force that effectively provides value to the customer without compromising the creativity and stability that a product or service needs to compete in an ever-evolving world. Why is a clear singular meaning needed for DevOps? The understanding of a meaning is an important part of sending a message. If an unclear word is used to send a message, and then the word risks becoming noise and the message risks becoming uninterpreted or misinterpreted. Without a clear singular meaning, you risk losing the message that you want people to hear. In technology, I see messages get drowned in noise all the time. The problem of multiple meanings In communication's theory, noise is anything that interferes with understanding. Noise is more than just the sounds of static, loud music, or machinery. Creating noise can be simple as using obscure words to explain a topic or providing an unclear definition that muddles the comprehension of a given subject. DevOps suffers from too much noise that increases people's uncertainty of the word. After a reading a few posts on DevOps, each one with its declaration of the essence of DevOps, DevOps becomes confusing. DevOps is empathy! DevOps is culture! DevOps is a movement! Because of noise, DevOps seems to stands for multiple ideas plus agile operations without setting any prioritization or definitive context. OK, so which is it? Is it one of those or is it all of them? Which idea is the most important? Furthermore, these ideas can cause friction as not everyone shares the same view on these topics. DevOps is supposed to reduce friction between naturally opposing groups within a business, not create more of it. People can get behind making more money and working fewer hours by strategically providing customers with extraordinary value. Once you start going into things that people can consider personal, people can start to feel excluded for not wanting to mix the two topics, and thus you diminish the reach of the message that you once had. When writing about empathy, one should practice empathy and consider that not everyone wants to be emotionally vulnerable in the workplace. Forcing people to be emotionally vulnerable or fit a certain mold for culture can cause people to shut down. I would argue that all businesses need people that are capable of empathy to argue on the behalf of the customer and other employees, but it's not a requirement that all employees are empathetic. At the other end of the spectrum, you need people that are not empathetic to make hard and calculating decisions. One last point on empathy, I've seen people write on empathy and users in a way that should have been about the psychology of users or something else entirely. Empathy is strictly understanding and sharing the feelings of another. It doesn't cover physical needs or intellectual ones, just the emotional. So another issue with crossing multiple topics into one definition, is that you risk damaging two topics at once. This doesn't mean people should avoid writing about these topics. Each topic stands on its own merit. Each topic deserves its own slate. Empathy and culture are causes that any business can take up without adopting DevOps. They are worth writing about, just make sure that you don't mix messages to avoid confusing people. Stick to one message. Write to the lowest common denominator Another aspect of noise is using wording that is a barrier to understanding a given definition. DevOps is the practice of operations and development engineers participating together in the entire service lifecycle, from design through the development process to production support. - the agile admin People that are coming from outside the world of agile and development are going to have a hard time piecing together the meaning of a definition like that. What my mind sees when reading something like that is same sound that a teacher in Charlie Brown makes. Blah, Blah Blah. blah! Be kind to your readers. When you want them to remember something, make it easy to understand and memorable. Write to appeal to all personality styles In marketing, you're taught to write to appeal to 4 personality styles: driver, analytical, expressive, and amiable. Getting people to work together in the workplace also requires appealing to these four personality types. There is a need for a single definition of DevOps that appeals to the 4 personality styles or at the very least, refrains from being a barrier to entry. If a person needs to persuade a person with a driver type of personality, but the definition includes language that invokes an automatic no, then it puts people who want to use DevOps at a disadvantage. Give people every advantage you can for them to adopt DevOps. Its time for a definition for DevOps One of the main points of communication is to reduce uncertainity. Its hypocritical to introduce a word without a definite meaning that touchs upon importance of communication and then expect people to take it seriously when the definition constantly changes. Its time that we have a singular definition for DevOps so that people use it for the hiring process, certifications, and that market it can do so without the risk of the message being lost or co-opted into something that is not. About the author Michael Herndon is the head of DevOps at Solovis, creator of badmishka.co, and all around mischievous nerdy guy.

Internet job portal ‘Indeed.com’ links potential employers with people who are looking to take the next step in their careers. The proportion of job posts on their site, relating to ‘Data Science’, a specific job in the AI category, is growing fast (see chart below). More broadly, Artificial Intelligence & machine learning skills, of which ‘Data Scientist’ is just one example, are in demand. No wonder that it has been termed as the sexiest job role of the 21st century. Interest comes from an explosion of jobs in the field from big companies and Start-Ups, all of which are competing to come up with the best AI business and to earn the money that comes with software that automates tasks. The skills shortage associated with Artificial Intelligence represents an opportunity for any developer. There has never been a better time to consider whether reskilling or upskilling in AI could be a lucrative path for you. Below : Indeed.com. Proportion of job postings containing Data Scientist or Data Science. [caption id="attachment_21240" align="aligncenter" width="1525"] Artificial Intelligence skills are increasingly in demand and create a real opportunity for those prepared to reskill or upskill.[/caption] Source: Indeed  The AI skills gap the market is experiencing comes from the difficulty associated with finding an individual demonstrating a competent mixture of the very disparate faculties that AI roles require. Artificial Intelligence and it’s near equivalents such as Machine Learning and Neural Networks operate at the intersection of what have mostly been two very different disciplines – statistics and software development. In simple terms, they are half coding, half maths. Hamish Ogilvy, CEO of AI based Internal Search company Sajari is all too familiar with the problem. He’s on the front line, hiring AI developers. “The hardest part”, says Ogilvy, “is that AI is pretty complex and the average developer/engineer does not have the background in maths/stats/science to actually understand what is happening. On the flip side the trouble with the stats/maths/science people is that they typically can't code, so finding people in that sweet spot that have both is pretty tough.” He’s right. The New York Times suggests that the pool of qualified talent is only 10,000 people, worldwide. Those who do have jobs are typically happily ensconced, paid well, treated appropriately and given no reason whatsoever to want to leave. [caption id="attachment_21244" align="aligncenter" width="1920"] Judged by $ investments in the area alone, AI skills are worth developing for those wishing to stay current on technology skills.[/caption] In fact, an instinct to develop AI skills will serve any technology employee well. No One can have escaped the many estimates, from reputable consultancies, suggesting that Automation will replace up to 30% of jobs in the next 10 years. No job is safe. Every industry is touched by AI in some form. Any responsible individual with a view to the management of their own skills could learn ML and AI skills to stay relevant in current times. Even if you don't want to move out of your current job, learning ML will probably help you adapt better in your industry. What is a typical AI job and what will it pay? OpenAI, a world class Artificial Intelligence research laboratory, revealed the salaries of some of its key Data Science employees recently. Those working in the AI field with a specialization can earn $300 to $500k in their first year out of university. Experts in Artificial Intelligence now command salaries of up to $1m. [caption id="attachment_21242" align="aligncenter" width="432"] The New York Times observes AI salaries[/caption] [caption id="attachment_21241" align="aligncenter" width="1121"] The New York Times observes AI salaries[/caption] Source: The New York times Indraneil Roy, an Expert in AI and Talent Acquisition who works for Edge Networks puts it this way when outlining the difficulties of hiring the right skills and to explain why wages in the field are so high. “The challenge is the quality of resources. As demand is high for this skill, we are already seeing candidates with fake experience and work pedigree not up to standards.” The phenomenon is also causing a ‘brain drain’ in Universities. About a third of jobs in the AI field will go to someone with a Ph.D., and all of those are drawn from universities working on the discipline, often lured by the significant pay packages which are available. So, with huge demand and the universities drained, where will future AI employees come from? 3 ways to skill up to become an AI expert (And earn all that money?) There is still not a lot of agreed terminology or even job roles and responsibility in the sector. However, some things are clear. Those wishing to evolve in to the field of AI must understand the conceptual thinking involved, as a starting point, whether that view is found on the job or as part of an informal / formal educational course. Specifically, most jobs in the specialty require a working knowledge of neural networks, data / analytics, predictive analytics, with some basic programming and database skills. There are some great resources available online to train you up. Most, as you’d expect, are available on your smartphone so there really is no excuse for not having a look. 1. Free online course: Machine Learning & Statistics and probability Hamish Ogilvy summed the online education which is available in the area well. There are “so many free courses now on AI from Stanford,” he said, “that people are able to educate themselves and make up for the failings of antiquated university courses. AI is just maths really,” he says “complex models and stats. So that's what people need grounding in to be successful.” Microsoft offer free AI courses for technical professionals: Microsoft’s training materials are second to none. They’re also provided free and provide a shortcut to a credible understanding in an area simply because it comes from a technical behemoth. Importantly, they also have a list of AI services which you can play with, again for free. For example, a Natural Language engine offers a facility for you to submit text from Instant Messaging conversations and establish the sentiment being felt by the writer. Practical experience of the tools, processes and concepts involved will set you apart. See below. [caption id="attachment_21245" align="aligncenter" width="1999"] Check out Microsoft’s free AI training program for developers.[/caption] Google are taking a proactive stance on Machine Learning. They see it’s potential to improve efficiency in every industry and also offer free ML training courses on their site. 2. Take courses on AI/ML Packt’s machine learning courses, books and videos: Packt is working towards a mission to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. It has published over 6,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools. You can choose from a variety of Packt’s books, videos and courses for AI/ML. Here’s a list of top ones: Artificial Intelligence by Example [Book] Artificial Intelligence for Big data [Book] Learn Artificial Intelligence with TensorFlow [Video] Introduction to Artificial Intelligence with Java [Video] Advanced Artificial Intelligence Projects with Python [Video] Python Machine learning - Second Edition [Book] Machine Learning with R - Second Edition [Book] Coursera’s machine learning courses Coursera is a company which make training courses, for a variety of subjects, available online. Taken from actual University course content and delivered with tests, videos and training notes, all accessed online, each course is roughly a University Module. Students pick up an ‘up to under-graduate’ level of understanding of the content involved. Coursera’s courses are often cited as merit worthy and are recognizable in the industry. Costs vary but are typically between $2k and $5k per course. 3. Learn by doing Familiarize yourself with relevant frameworks and tools including Tensor Flow, Python and Keras. TensorFlow from Google is the most used open source AI software library. You can use existing code in your experiments and experiment with neural networks in much the same way as you can in Microsoft’s. Python is a programming language written for a big data world. Its proponents will tell you that Python saves developers hundreds of lines of code, allowing you to tie together information and systems faster than ever before. Python is used extensively in ML and AI applications and should be at the top of your study list. Keras, a deep learning library is similarly ubiquitous. It’s a high level Neural Network API designed to allow prototyping of your software as fast as possible. Finally, a lesser known but still valuable resources is the Accord.net. It is one final example of the many  software elements with which you can engage with to train yourself up. Accord Framework.net will expose you to image libraries, natural learning and real time facial recognition. Earn extra points with employers AI has several lighthouse tasks which are proving the potential of the technology in these still early stages. We’ve included a couple of examples, Natural Language processing and image recognition, above. Practical expertise in these areas specifically, image or voice recognition or pattern matching are valued highly by employers. Alternatively, have you patented something? A registered patent in your name is highly prized. Especially something to do with Machine Learning. Both will help you showcase Extra skills / achievements that will help your application.’ The specifics of how to apply for patents differ by country but you can find out more about the overall principles of how to submit an idea here. Passion and engagement in the subject are also, clearly appealing characteristics for potential employers to see in applicants. Participating in competitions like Kaggle, and having a portfolio of projects you can showcase on facilities like GitHub are also well prized. Of all of these suggestions, for those employed, any on the job experience you can get will stand you in the best stead. Indraneil says "Individual candidates need to spend more time doing relevant projects while in employment. Start ups involved in building products and platforms on AI seem to have better talent." The fact that there are not many AI specialists around is a bad sign There is a demand for employees with AI skills and an investment in relevant training may pay you well. Unfortunately, the underlying problem this situation reveals could be far worse than the problems experienced so far. Together, once found, all these AI scientists are going to automate millions of jobs, in every industry, in every country around the world. If Industry, Governments and Universities cannot train enough people to fill the roles being created by an evolving skills market, we may rightly be concerned to worry about how they will deal with retraining all those displaced by AI, for whom there may be no obvious replacement role available. 18 striking AI Trends to watch in 2018 – Part 1 DeepMind, Elon Musk, and others pledge not to build lethal AI Attention designers, Artificial Intelligence can now create realistic virtual textures

How would you describe programming? Can you describe programming in 5 words? It's pretty difficult. Even explaining it in a basic and straightforward way can be challenging. You type stuff... and then it turns into something else or makes something happen. Or, as is often the case, something doesn't happen. Twitter account @abstractionscon asked its followers "what 5 words best describe programming?" The results didn't disappoint. There was a mix of funny, slightly tragic, and even poetic evocations and descriptions of what programming is and what it feels like. It turns out that more often than not, it simply feels frustrating. Things go wrong a lot. One of the most interesting aspects of the conversation was how it brings to light just how challenging it is to put programming into language. That's reflected in many of the responses to the original tweet. One of the conclusions we can probably draw from this is that not only is describing programming pretty hard, it's also pretty funny. And from that, perhaps it's also true that programming is generally a pretty funny thing to do. But then why would that be surprising? You learn from an early age that getting a computer to do what you want is difficult, so why should writing software be any different? Take a look at some of the best attempts to describe programming below. Which is your favourite? And how would you describe programming? https://twitter.com/alicegoldfuss/status/988818057219854336 https://twitter.com/jennschiffer/status/988849269552578560 https://twitter.com/lindseybieda/status/988941397544890368 https://twitter.com/sarahmei/status/988600171075268608 https://twitter.com/tef_ebooks/status/988752549552578560 https://twitter.com/jckarter/status/988828156386684928 https://twitter.com/cassidoo/status/988920470907961344 https://twitter.com/kelseyhightower/status/988646191679209472 https://twitter.com/francesc/status/988653691669446658 https://twitter.com/shanselman/status/988919759377915904 https://twitter.com/chriseng/status/988674723516207104 https://twitter.com/EricaJoy/status/988649667914186755 https://twitter.com/brianleroux/status/988628362355773440 https://twitter.com/ftrain/status/988759827731148800 https://twitter.com/jbeda/status/988634633087545344 https://twitter.com/kamal/status/988749873347375104 https://twitter.com/fatih/status/988695353171030016 https://twitter.com/innesmck/status/989067129432498176 https://twitter.com/franckverrot/status/988611564168036352 https://twitter.com/dewitt/status/988609620536053760 Thank you Twitter for your insights and jokes. It does make you feel better to know that there are millions of people out there with the same frustrations and software-induced high blood pressure. The next time something goes wrong remember you're really just meat teaching sand to think. Hopefully that should put everything into perspective. Read more: Slow down to learn how to code faster

Clearly, there’s lot of competition for web designer jobs, - with the salary rising each year - so it’s crucial you find a way to make your resume stand out. You need to balance creativity with professionalism, all while making sure your experience, personality, and skills shine through. Over the years, people have created numerous resumes for web designers and everybody knows what it takes to get that job. Follow this guide to write a creative and attention-grabbing web designer resume. Note: All images in this article are courtesy of zety.com resume templates page and the guide. Format is a window to your mind Because you’re applying for a design position, the look and format of your resume is very important. It gives prospective employers a sense of your design philosophies. Use white space, and clear, legible fonts to help a hiring manager easily find your information. To stand apart from the crowd, avoid using a word processor to create your resume. Instead use InDesign or Illustrator to design something creative and less cookie-cutter like. Submit your resume in PDF format to avoid formatting errors that will ruin the look of your document. Sometimes a job posting will specifically forbid submitting in PDF though, so watch out for that. Highlight your Experience The key to writing a good experience section for a web designer is keeping it brief and relevant, while highlighting your career achievements. Add no more than three to five bullet points with measurable achievements per past position. Don’t just list your past company and when you worked there. “Discuss what you did and include some tangible accomplishments. If you created a custom graphic set for clients, mention that, and also what percentage were satisfied (hopefully it’s very high.) Prove you have the necessary experience to do the job well,” advises Terrence Wood, resume proofreader at Paper Fellows. Education Your education is not nearly as important as your experience, but you still need to present it right. That means using this section to talk about your strong points. Include coursework and achievements that are relevant to the job description. Maybe you wrote a column about web design in your college’s newspaper, things like this help you stand out to a hiring manager, especially if you are just starting your career in web design. Also, include the GPA here. Showcase your skills Everyone is going to list their skills, if you want that interview you need to do something to make yourself seem exceptional. The first thing you’ll do is take a good look at the job description and note what skills and responsibilities they mention. Now you know what skills to include, but including them is not enough. You need to prove you have them by giving examples of times you used them at past jobs. Don’t just list that you are proficient in Adobe Creative Suite, prove it by describing how you used it to tackle web design for 90% of client projects. Up the ante even more and include samples of your past work. This is a good spot to include your portfolio, any certifications that you have or anything that can help you let them know just how good you are at your job and confirm your skills. If you've had a predominantly freelance career, list the companies or individuals that you've worked for and include examples of work for each of them. You can do the same thing if you had a '9 to 5' career – simply list your previous jobs and show examples of your best work. It's a good idea to let your potential employers know about any future skills you plan to acquire. Use infographics You can give your resume a really unique look by using infographics, while still keeping it professional. Divide your resume layout into a grid with two columns and four or five rows. Now place one category of data into each square of your grid. Next, transform each section into an infographic. Use icons to represent different skills or awards. You can use your design software’s shape tools to create charts and graphs. Programs like Adobe InDesign can be used to create your infographics. You can also use Canva or Visme. Keep it professional It’s a great idea to inject some creativity into you resume. You want to stand out, and after all, it is a design resume. But it’s also important to balance that creativity with professionalism. A hiring manager will make some judgements about your personality based on how your resume looks. Be subtle in your creativity. Use colors that are easy on the eyes, and keep fonts reasonable. There can be a lot of beauty in simplicity. Stick to the basics, place content in an order familiar to recruiters to avoid making them have to work for the information they need. Remember that your primary goal is to communicate your information clearly. Write a cover letter Some people say that it's not really necessary but it's your chance to stand out. Maybe there is something that isn't on your resume or you want to seem more appealing and human – cover letter is a good chance to do all of that. Cover letter is a great place to elaborate how you'll be able to meet their needs. It's a good opportunity to also show them that you have done your research and know their company. Writing resources for your resume ViaWriting and Writing Populist: These are grammar resources you can use to check over your resume for grammatical mistakes. Resume Service: This is a resume service you can use to improve the quality of your web designer resume. Boomessays and EssayRoo: These are online proofreading tools, suggested by Revieweal, you can use to make sure you resume is polished and free of errors. My Writing Way and Academadvisor: Check out these career writing blogs for tips and ideas on how to improve your resume. You’ll find posts here by people who have written web designer resumes before. OXEssays and UKWritings: These are editing tools, recommended by UKWritings review, you can use to go over your resume for typos and other errors. StateofWriting and SimpleGrad: Check out these writing guides for suggestions on how to improve your resume. Even experienced writers can benefit from some extra guidance now and then. ResumeLab: Learn what to include in a cover letter. The job market for web designers is competitive. Make sure you lead with your best qualities and skills, and be sure they fit the job description as closely as possible. Be creative, but ensure you keep your resume professional as well. Now go have fun using this guide to write a creative and attention-grabbing web design resume. Author bio Grace Carter is a resume proofreader at Assignment Writing Service and at Australian Help, where she helps with CV editing and cover letter proofreading. Also, Grace teaches business writing at Academized educational website.   Is your web design responsive? “Be objective, fight for the user, and test with real users on the go!” – Interview with design purist, Will Grant Tips and tricks to optimize your responsive web design  

What is Tao? It's a complex idea - but one method of thinking of it is to find the natural way of doing things, and then making sure you do things that way. It is intuitive knowing, an approach that can't be grasped just in principle but only through putting them into practice in daily life. The principles of Tao can be applied to almost anything. We've seen the Tao of Physics, the Tao of Pooh, even the Tao of Dating. Tao principles apply just as well to DevOps - because who can know fully what DevOps actually is? It is an idiom as hard to define as "quality" - and good DevOps is closely tied to the good quality of a software product. Want a simple example? A recipe for cooking a dish normally starts with a list of ingredients, because that's the most efficient way of describing cooking. When making a simple desert, the recipe starts with a title: "Strawberries And Cream". Already we can infer a number of steps in making the dish. We must acquire strawberries and cream, and probably put them together on a plate. The recipe will continue to describe the preparation of the dish in more detail, but even if we read only the heading, we will make few mistakes. So what does this mean for DevOps and product creation? When you are putting things together and building things, the intuitive and natural way to describe the process is to do it declaratively. Describe the "whats" rather than the "hows", and then the "hows" can be inferred. The Tao of Building Software Most build tools have at their core a way of declaring relationships between software components. Here's a Make snippet: a : b cc b And here's an Ant snippet: cc b </build> And a Maven snippet: <dependency> lala </dep> Many people think they wound up in a Lovecraftian hell when they see XML, even though the brackets are perfectly euclidean. But if you squint hard enough, you will see that most tools at their core describe dependency trees. The Apache Maven tool is well-known, and very explicit about the declarative approach. So, let's focus on that and try to find the Tao of Maven. When we are having a good day with Maven and we are following the ways of Tao, we describe what type of software artifact we want to build, and the components we are going to use to put it together. That's all. The concrete building steps are inferred. Of course, since life is interesting and complex, we will often encounter situations were the way of Tao eludes us. Consider this example: type:pom antcall tar together ../*/target/*.jar Although abbreviated, I have observed this antipattern several times in real world projects. Whats wrong with it? After all, this antipattern occurs because the alternatives are non-obvious, or more verbose. You might think it's fine. But first of all, notice that we are not describing whats (at least not in a way that Maven can interpret). We are describing hows. Fixing this will probably require a lot of work, but any larger build will ensure that it eventually becomes mandatory to find a fix. Pause (perhaps in your Zen Garden) and consider that dependency trees are already described within the code of most programming languages. Isn't the "import" statement of Java, Python and the like enough? In theory this is adequate - if we disregard the dynamism afforded by Java, where it is possible to construct a class name as a string and load it. In practice, there are a lot of different artifact types that might contain various resources. Even so, it is clearly possible in theory to package all required code if the language just supported it. Jsr 294 - "modularity in Java" - is an effort to provide such support at the language level. In Summary So what have we learned? The two most important lessons are simple - when building software (or indeed, any product), focus on the "Whats" before the "Hows". And when you're empowered with building tools such as Maven, make sure you work with the tool rather than around it. About the Author Joakim Verona is a consultant with a specialty in Continuous Delivery and DevOps, and the author of Practical DevOps. He has worked as the lead implementer of complex multilayered systems such as web systems, multimedia systems, and mixed software/hardware systems. His wide-ranging technical interests led him to the emerging field of DevOps in 2004, where he has stayed ever since. Joakim completed his masters in computer science at Linköping Institute of Technology. He is a certified Scrum master, Scrum product owner, and Java professional.

Lately, IoT is beginning to play an integral part in various industries, be it at the consumer-level, or at the enterprise side of it. With a lot of big players like Apple, Microsoft, Amazon, and Google entering this market, IoT adoption has scaled tremendously. It is said to have jumped from a hobbyist level to an industry infrastructure where everything functions on smart devices, that can talk. The bulk release of popular IoT products prove that this market is getting bigger and a lot of individuals have been amazed with home automation products such as Amazon Alexa, Apple Homepod, Google Home and others. These devices are one of the most sought-after things for hobbyist and enthusiasts who are interested to do simple automation with sensors. Following are 5 IoT projects ideas that you can build without a hole in the pocket. To learn how to actually build similar kind of projects, check out our books; Internet of Things with Raspberry Pi 3 Smart Internet of Things Projects Raspberry Pi 3 Home Automation Projects Weather control station This project will not only help you measure the room temperature but will also help you measure the altitude and the pressure in the room. For this project you will need the Adafruit Starter Pack for Windows 10 IoT Core on the latest Raspberry Pi kit. Along with the Raspberry Pi Kit you will also be using other sensors that read temperature, pressure, and altitude. To make your weather station advanced, you can connect the device to your cloud account to store the weather data. Hardware Raspberry Pi 2 or 3 Breadboard Adafruit BMP280 Barometric Pressure & Altitude Sensor Software Windows 10 IoT Core Approximate total cost Less than $60 Facial Recognition Door Self-built home security projects are some of the most popular DIY projects because they can be cheaper and simple compared to bulky professional installations. Here's a project that controls entry access using facial recognition, thanks to Microsoft Project Oxford. This project from Mazudo, based on Raspberry Pi and Windows IoT, is posted on Hackster.io. This is a handy project for DIY enthusiasts who want to build a quick security lock for their homes. Hardware Raspberry Pi 3 Breadboard USB camera Relay switch Speaker Software Windows 10 IoT Core Approximate total cost Less than $50 Your very own Alexa Echo Alexa Echo has always been a handy device, which can take notes, schedule reminders for your appointments, and play podcasts for you. Brilliant, isn’t it?  You can build a fully functional customized Alexa Echo with all the features of Alexa, apart from accessing official music servers like Amazon prime. It will also have an integration with recently included third party apps like todoist and Any.do. This DIY Echo can also be connected to your cell phone devices to manage notifications when the timer goes off, and so on. Only one thing that your DIY will be missing is the ability to function as a bluetooth speaker. Hardware Raspberry Pi 3 Breadboard USB speaker and mic Software Raspbian Approximate total cost Less than $50 Pet Feeder You surely don’t want your pet to starve when you’re away, do you? This customized pet feeder is controlled via the internet; set timings and feed your pet automatically later. These pet feeders are directly connected to WiFi using ESP8266 chip. We can easily add features like controlling the device using cell phone and making dashboards using Freeboard. This project can be later upgraded or rightly reprogrammed to fill your snack bowl at regular intervals as well. Hardware Arduino PIR motion sensor ESP8266 ESP-01 Software Arduino IDE ESP8266Flasher.exe Approximate total cost Less than $40 Video Surveillance Robot Video surveillance is a process of monitoring a scenario, person or an environment as a whole. A video surveillance robot can capture the activities happening in the surrounding where it is deployed and can be controlled using a GUI Interface. For further enhancements, you can even connect your device to the cloud and save the recorded data there. Hardware Raspberry Pi ARM Cortex- A7 CPU L293 motor driver Software Raspbian Approximate total cost Less than $50 These are few economical yet highly useful Internet of Things projects, which can be leveraged to improve your daily activities. Still not convinced?. Think of it this way. Buying the microchip board is a one time investment as it can be reused in separate projects. The sensors and other peripherals aren’t that expensive. You might say, it’s just way easier to buy an IoT device. I would argue that, buying an IoT device is not as satisfying as building one for the same purpose. In the end, there are multiple advantages of building one as you can brag about it to your friends and most importantly include it in your resume to give you that edge over others in an interview. Cognitive IoT: How Artificial Intelligence is remoulding Industrial and Consumer IoT Windows 10 IoT Core: What you need to know 5 reasons to choose AWS IoT Core for your next IoT project  

As a data developer, the concept or process of data analysis may be clear to your mind. However, although there happen to be similarities between the art of data analysis and that of statistical analysis, there are important differences to be understood as well. This article is taken from the book Statistics for Data Science by James D. Miller. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. In this article, we've broken things into the following topics: What is statistical analysis and it's best practices? How to establish the nature of data? What is Statistical analysis? Some in the study of statistics sometimes describe statistical analysis as part of statistical projects that involves the collection and scrutiny of a data source in an effort to identify trends within the data. With data analysis, the goal is to validate that the data is appropriate for a need, and with statistical analysis, the goal is to make sense of, and draw some inferences from, the data. There is a wide range of possible statistical analysis techniques or approaches that can be considered. How to perform a successful statistical analysis It is worthwhile to mention some key points, dealing with ensuring a successful (or at least productive) statistical analysis effort. As soon as you can, decide on your goal or objective. You need to know what the win is, that is, what the problem or idea is that is driving the analysis effort. In addition, you need to make sure that, whatever is driving the analysis, the result obtained must be measurable in some way. This metric or performance indicator must be identified early. Identify key levers. This means that once you have established your goals and a way to measure performance towards obtaining those goals, you also need to find out what has an effect on the performance towards obtaining each goal. Conduct a thorough data collection. Typically, the more data the better, but in the absence of quantity, always go with quality. Clean your data. Make sure your data has been cleaned in a consistent way so that data issues would not impact your conclusions. Model, model, and model your data. Modeling drives modeling. The more you model your data, the more questions you'll have asked and answered, and the better results you'll have. Take time to grow in your statistical analysis skills. It's always a good idea to continue to evolve your experiences and style of statistical analysis. The way to improve is to do it. Another approach is to remodel the data you may have on hand for other projects to hone your skills. Optimize and repeat. As always, you need to take the time for standardizing, following proven practices, using templates, and testing and documenting your scripts and models, so that you can re-use your best efforts over and over again. You will find that this time will be well spent and even your better efforts will improve with use. Finally, share your work with others! The more eyes, the better the product. Some interesting advice on ensuring success with statistical projects includes the following quote: It's a good idea to build a team that allows those with an advanced degree in statistics to focus on data modeling and predictions, while others in the team-qualified infrastructure engineers, software developers and ETL experts-build the necessary data collection infrastructure, data pipeline and data products that enable streaming the data through the models and displaying the results to the business in the form of reports and dashboards.                                                                                                            - G Shapira, 2017 Establishing the nature of data When asked about the objectives of statistical analysis, one often refers to the process of describing or establishing the nature of a data source. Establishing the nature of something implies gaining an understanding of it. This understanding can be found to be both simple as well as complex. For example, can we determine the types of each of the variables or components found within our data source; are they quantitative, comparative, or qualitative? A more advanced statistical analysis aims to identify patterns in data; for example, whether there is a relationship between the variables or whether certain groups are more likely to show certain attributes than others. Exploring the relationships presented in data may appear to be similar to the idea of identifying a foreign key in a relational database, but in statistics, relationships between the components or variables are based upon correlation and causation. Further, establishing the nature of a data source is also, really, a process of modeling that data source. During modeling, the process always involves asking questions such as the following (in an effort establish the nature of the data): What? Some common examples of this (what) are revenue, expenses, shipments, hospital visits, website clicks, and so on. In the example, we are measuring quantities, that is, the amount of product that is being moved (sales). Why? This (why) will typically depend upon your project's specific objectives, which can vary immensely. For example, we may want to track the growth of a business, the activity on a website, or the evolution of a selected product or market interest. Again, in our current transactional data example, we may want to identify over- and under-performing sales types, and determine if, new or repeat customers provide more or fewer sales? How? The how will most likely be over a period of time (perhaps a year, month, week, and so on) and then by some other related measure, such as a product, state, region, reseller, and so on. Within our transactional data example, we've focused on the observation of quantities by sale type. Another way to describe establishing the nature of your data is adding context to it or profiling it. In any case, the objective is to allow the data consumer to better understand the data through visualization. Another motive for adding context or establishing the nature of your data can be to gain a new perspective on the data. In this article, we explored the purpose and process of statistical analysis and listed the steps involved in a successful statistical analysis. Next, to learn about statistical regression and why it is important to data science, read our book Statistics for Data Science. Estimating population statistics with Point Estimation. Why You Need to Know Statistics To Be a Good Data Scientist. Why choose IBM SPSS Statistics over R for your data analysis project.