How Data Science Help You To Enhance Your Business In 2020

Data is the king; we have been listening to it for a long time and its true. A recent study by McKinsey highlighted the fact that around 36% of the companies believe that the data has impacted the industry-wide completion. 32% of them are adopting strategies to adopt new data analytics technology. But, here, it becomes essential to mention that you need to have valuable data with you before heading and using the data for the formulation of strategies. So, if you are planning to expand your business in the new decade of 2020, it becomes essential that you must choose the right tools to get the right analysis of the data. Read ahead to know how you can enhance your business using data science in the years to come.

How Data Science Help You To Enhance Your Business In 2020

Use of data science for business expansion

  • Introducing relevant products–  With the help of data science technology, you can explore the history of a product, analyze market, get an understanding of what kind of products are trending or preferred by the customers will help in formulating the right strategy for the future developments and scope of improvisation. Constant analysis and reflection of data science help in having a better understanding of the market’s response to a particular product or service.
  • Hiring the best talent- Hiring can be simplified by making use of data science one can have a complete record of data points on talent making use of job sites, social media, corporate database etc. With these data points readily available, it becomes easier to find the best fit for the organization. Well, data science gives you a smarter way to extract the best talent.
  • Finding the target audience- There is a huge chunk of data that gets created every day, and you cannot solely rely on all the information that is available to you. In this case, it becomes important to extract the most useful data. With the help of data science technology, you can work on this data, find the relevant one and start using the same.
  • Customization is the key- One of the factors that are going to differentiate your company from the others is to present them the information or data which matches their requirement. Knowing the preferences and choices of the customer makes it easier for them to understand the same and present your products or services accordingly.

Conclusion- All-in-all, the work of data science, is to save your time and help you derive the right conclusion for your requirement. It is going to be the need for the future, and if you too wish to expand your horizons of growth, its time that you must start investing in learning this technology. Global Tech Council is providing an online platform for learning about Data Science. After successful completion of this course, as a Data Science expert, you will not only have a complete insight into the concepts of Data Science but at the same time, you would also get to know the practical implications of the same.

Different Types Of Text Annotation For Machine Learning

Machine Learning is one of the most talked-about concepts. It is a part of AI that works on the analysis of data and interpreting it in a way that the response arisen from it is accurate and human-like. Well, for the Machine Learning algorithm to work properly and assess the data well, the data need to be labeled in a way that is easily and accurately understandable by the Machine Learning algorithm. In this blog, we will be unfolding popular types of annotation for machine learning.

Different Types of Text Annotation for Machine Learning

What is an annotation?

Before heading further, let’s have a recap at what annotation is. For any data to become comprehendible by the machine learning system, it is improved that the data is prepared in a  manner that the system can easily find the pattern and inferences from them. This is done by adding metadata to the dataset. Any metadata tag which is used to mark the data is known as an annotation. For the machine learning system to understand it more accurately, it is important that this marking of data is done more accurately. 

Types of annotation for machine learning :

  • Phrase chunking- It consists of tagging parts of speech along with their grammatical meaning.
  • Semantic annotation- In these various concepts are annotated within texts like people, company names, objects. Machine Learning makes use of this kind of annotation to categorize new concepts. It helps in improving search relevance and preparing chatbots to answer more aptly.
  • Entity annotation–  It is used for labeling unstructured sentences with the right information, which can be easily comprehended by machine. There are different processes to come together to make the language easy to understand.
  • Image and video annotation– This method is used to train the machine learning system to analyze and block sensitive content,  e-commerce product listing, and guiding autonomous vehicles. With this annotation, it becomes easier for a machine learning system to understand images and videos.

Machine Learning System In Natural Language Annotation

Our text analysis functions are based on patterns and rules. Each time we add a new language, we begin by coding in the patterns and rules that the language follows. Then our supervised and unsupervised machine learning models keep those rules in mind when developing their classifiers. We apply variations on this system for low-, mid-, and high-level text functions.

Low-level text functions are the initial processes through which you run any text input. These functions are the first step in turning unstructured text into structured data; thus, these low-level functions form the base layer of information from which our mid-level functions draw on. Mid-level text analytics functions involve extracting the important content of a document of text. This means who is speaking, what they are saying, and what they are talking about.

The Future– Any individual who wishes to become a machine learning expert should know about annotation. At Global Tech Council, you will not only gain an insight into the concepts of machine learning, but you will also learn about allied concepts. Annotation is an integral step for making machine learning models more effective and efficient. Knowing all about annotation, you can become a machine learning expert holding great prospects in the future.

How Is Python Used In Machine Learning?

Artificial Intelligence and Machine Learning have transformed the industry. Both these concepts are no more a part of some sci-fi film. Instead, they have become the reality of today. While we talk so much about its use, there is also a great demand for AI and ML experts. To gain complete knowledge about these concepts and to use the same in real life, it is mandatory to have knowledge about a programming language that forms the base of the same. Python is considered to be apt for Machine Learning.

How Is Python Used In Machine Learning

Why Python?

While there are so many programming languages in the world, there is a special emphasis on Python. Let’s understand why so?

  1. Ease of learning- One of the major reasons why Python is preferred over other languages is because it is easy to learn. When compared with Java or C++, the syntax is comparatively simpler in Python, which makes it easy to use.
  2. Great choice of libraries– Another reason which makes Python a popular language for Machine Learning is because it offers a wide spectrum of libraries. Machine learning needs a lot of data processing, and with Python’s libraries, you can easily access and transform a large volume of data.  
  3. Flexibility- One of the notable features of Python is that it offers flexibility:  
  • The developer can choose either description or OOPs
  • Recompilation of source course is not required. Implementing change is easier in this
  • Python can be easily combined with other languages 
  1. Accessible across different platforms- One of the things that makes Python preferable is that you can run it on any platform like Linux, Windows, macOS, Unix etc. In case there is a need to transfer it from one platform to another, only minor modifications are needed to create an executable form of code.  

All these factors make Python a popular language for Machine Learning. If you also want to gain expertise in Python and become a machine learning expert, Global Tech Council offers you the right platform. Here you will learn about different aspects of machine learning, implementation of the same, thus making you adept with the latest development in this field.

5 Beginner Tips To Learn Python

Python is an important programming language that every developer must have at his fingertips. Many programmers make use of Python to create learning algorithms, build websites, and perform other significant tasks. In this article, I will provide you with a few sure shot tips that you can apply to master the art of coding using Python.

5 Beginner Tips To Learn Python

Beginner Tips: Learning Python the right way

Here a few tips to learn Python in a simple manner.

1. Coding Everyday

Consistency is of the utmost importance when it comes to learning any new language. You must make a commitment to yourself to code every day. Though it may be hard to believe, muscle memory plays a vital role in programming and committing yourself to code every day will help you improve muscle memory to a great extent.

2.  Write Down What You Learn

While progressing on your journey as a new programmer, you may wonder if taking notes is a necessary aspect. But the fact is, Yes, it absolutely is! As per research reports, taking notes by hand helps in long-term concept retention. This will be highly beneficial, especially if you are looking to become a full-time developer, as many interviews will require you to write down codes on a whiteboard. When you start working on small projects and programs, writing by hand will help you to plan your code before moving it to the computer. This will help you save a lot of time and will help you write down the functions and classes you need.

3. Become a Bug Bounty Hunter

Hitting a bug is inevitable once you start writing programs you will run into bugs in your code. It is important to follow a methodological approach while debugging, to help you find out where things are breaking down. An ideal way to achieve this is by going through your code in the order in which it is executed and make sure that each part functions properly. After you get a fair idea of where things are breaking down, you can use a Python debugger. Python’s built-in debugger is called PDB. This debugger will drop you into the interactive mode. It will open up an application in a certain spot and let you have a look at the variables and see what you want to see.

4. Take Breaks

While learning, it is highly important to step away and absorb the concepts. You can use the Pomodoro technique. It is a widely used technique that can help you. It refers to working for twenty-five minutes, taking a short break, and then repeating the process. It is critical to take breaks to have an effective study session, particularly when it involves taking in a lot of new information. If you hit a bug and are not able to figure out what is going wrong, take a break. In programming, you must make sure that your code follows the rules of a language and logic exactly. Hence, missing even a question mark will affect everything.

5. Indulge in Pair Programming

Pair programming refers to a technique that involves two developers who work at one workstation to complete a task. The two developers will switch between being the navigator and the driver. The driver is the one who writes the code, and the navigator is the one who reviews the code once it is written and guides problem-solving. You must frequently switch to get the benefit of both sides. Pair programming offers many benefits. It exposes you to multiple ideas and ways of thinking that will aid in problem-solving once you start coding on your own. Apart from having someone review your code, it also allows you to see how someone else thinks about a problem.

Conclusion

Now that you have these strategies for learning, you are ready to begin your Python journey! Find Real Python’s Beginners Roadmap for Learning here! We also offer a beginner’s level Python course, which uses interesting examples to help you learn programming and web development. To know more about Python certifications and become a Python expert, check out Global Tech Council.

AI Help eCommerce Business To Drive A Sales Higher

Artificial intelligence deals with using computers to understand human intelligence, and it is the science and engineering of creating intelligent machines, especially intelligent computer programs. It refers to adding human capabilities into machines. AI initiates problem-solving, common sense, and analytical reasoning power in machines. Put simply, AI involves making computer programs that imitate human behavior.

AI Help eCommerce Business To Drive A Sales Higher

Application of AI in E-Commerce to Drive Sales

Here are some ways in which e-commerce companies use artificial intelligence technology to boost sales.

1. Buiding an Assortment Intelligence Tool

Customers are now forcing retailers to change their pricing strategy. It is thus imperative that multichannel retailers apply flexibility to retain customers when it comes to price structuring. Retailers are now turning to a new tool called assortment intelligence. It is a tool facilitating an unprecedented level of 24*7 visibility and provides valuable insights into the product assortments of competitors. It provides businesses with the ability to adjust their product mix and pricing with high accuracy. It provides an impressive competitive advantage as it provides complete visibility into the products being offered in the market. Businesses will be able to monitor the product mix of the competitors that will be segmented based on product, brand and the percentage of overlap. It puts retailers in a strong position to make specific planning decisions and track the impact of the same.

2. Personalized recommendations

AI suggests free recommendations by text or popup. All the searches related to products are gathered and compiled, following which the appropriate recommendations are sent to the customers. Product recommendations provide a personal touch to the users. The search engine analyzes the identified user’s preferences using AI with respect to the brands they follow. Product recommendations are a reflection of the user’s past purchases and his activities on the website. This will help users find what they are looking for and will eventually lead to sales. The users rate the services and brands that are directly proportional to the Return on Investment (ROI).

3. Voice Assistants

It is highly important to make the shopping experience worthy and memorable as the growth of technology in today’s era is immense. AI-enabled voice assistants offer customers with pleasant and interactive solutions. Voice assistants can help smoothen the user experience. Big e-commerce giants are providing users with the power of voice for better analysis and recommendations. It makes the shopping process hassle-free for the user as there is no need for them to type their requests every time. It offers a personal and human touch to the entire online shopping experience.

4. Advance Visual Search Engine

Recently, the visual search engine was introduced by artificial intelligence in the e-commerce sector. It is one of the stimulating trends that help users discover what they want just through a single click. AI can be regarded as a driven technology that enables the visual search. The user will be able to get appropriate results in a simple click. An image will be helpful to find the better size, color, and quality to identify the names and brand of products on the search engine. The users can get hold of the desired results based on the image.

Conclusion

Artificial Intelligence enabled applications with smart capabilities are solving real-world problems in today’s scenario. AI, no doubt, offers a consistent and personalized experience to users, brands, and services. To become an AI expert and learn more about artificial intelligence certifications, check out Global Tech Council.

Few Chatbots Expert Interview Questions & Answer For Freshers

Nowadays, you can find the prevalence of many tools and software that have transformed business operations. A chatbot is one such tool. Powered by AI, you can find almost every company making use of the same. In fact, it has also emerged as a popular career option. You can also become a chatbot certification expert. Companies like Global Tech Council is providing a great platform where you can learn about chatbots, preparing apps, and syncing them with the current business operations. 

Few Chatbots Expert Interview Questions & Answer For Freshers

After pursuing a certification course in a chatbot, you can apply for a job across different industries. The work of a chatbot developer includes developing chatbots, understand software development, construct new chatbots, and other AI-based chatbot systems. Now that you know about chatbot developers, it becomes important to acquaint yourself with a few question and answers which can help you crack the interview.

Few Questions :

1. How well you know about NLP or Natural Processing Language?

Chatbots are developed to provide the user with a conversation interaction between human and machine, and Natural Processing Language makes this happen easily. This language ensures that chatbots are more human. So, knowing NLP or Natural Processing language is important. 

2. What are the other programming language, you know?

As a Bot developer, you are required to have knowledge about different programming languages and technologies. Some of the popular languages that you should be knowing are Python, Java, Clojure, jQuery, Java, Angular, SQL, JavaScript, and Amazon Simple Service. 

3. Is it possible to control Bot once it has gone live?

Once you have made Bot live, it can be controlled. It has been programmed to understand a set of questions. The company developing the bot can decide when to expand or shrink the understanding of questions. 

4. What is Mitsuku consumer bot?

It is one of the most popular and important bots which runs on Pandorabots. It is the world’s leading conversation; AI chatbot platform. Pandorabots offers free web service for building bots. 

5. How can you use AIML or Artificial Intelligence Markup Language or AIML to simplify bot models’ conversation?

AIML is a flexible and universal language that the bot develops uses to write pattern templates. The developer uses AIML to match words and phrase patterns to keywords so that bits can have a more humane interaction with the user. 

Wrapping it up- Chatbots is one of the most human ways the software can interact with humans and answer their queries. There are many bots that are being used to enhance customer interaction and customer satisfaction.

How Augmented Reality Change The Way Human Life?

Technology has transformed our lives in different ways. With the easy accessibility to the latest development coupled with easy to use hardware and software, we now have technology penetration in our lives in a totally spectacular way. One such development is augmented reality. In this blog, we will be unfolding different ways in which augmented reality has changed human life.

How Augmented Reality Change The Way Human Life?

Augmented reality and its impact on human life:

  • Engrossing entertainment – Video games and films are the best mode of entertainment, but over a period of time, the source and mode of delivering this entertainment have transformed the lives of the individual. Augmented Reality changes the way people use to play a game or see videos. It is about bringing life to the pictures or taking the individual to a whole new world. The 3D game engine is one such example which gives the player a real-life experience of gaming. Besides, the Sundance Film Festival featured more than 30 VR experiences. All this has completely transformed the mode of entertainment. 
  • Better architecture– Wouldn’t it be great to have a 3D view of what the final product would look like. Well, this is now possible with augmented reality. This technology allows you to create a 3D view of any building’s design, which gives a better insight into the final layout of the project. It eventually helps in looking at the minor details of the project and then making changes in case of any error.
  • Better learning experience- When we talk about augmented reality, then its application is not merely restricted to marketing and architecture, rather, it finds wide usage in the education field. With the help of AR or Augmented Reality, it becomes easier for the students to grasp the concepts. Besides, AR ensures that kids gain interest in learning as it imparts real-life learning experience to them.
  • Thorough training– Good training ensures creating a well-trained workforce. Augmented reality can help in this process. It helps in giving the trainees a “you are their experience” without them actually going to that place. 

The future is waiting to unfold– These are just one of the few uses of augmented reality. If you are also looking to make a career in this field, then one of the ways is to enroll for augmented reality certification. After successful completion of this course, you will become an augmented reality expert. Global Tech Council offers a great platform to those who are willing to learn augmented reality along with hands-on experience. The future of this field is bright, and gaining skills in this field promises a better future.

Important Tools Used In The Internet Of Things

App development is trending; they have simplified the web interaction and completion of a task. While building apps based on IoT can be a strenuous task, but with a wide number of development tools, it has become easier to develop the app. If we take a look at the numbers, then in 2015 there were 15.41 billion IoT devices which have increased to 26.66 billion and are expected to surpass 75 billion by 2025. Most of these devices are used in the manufacturing industry, businesses, and healthcare industry. The growing demand for apps has led to the surge in the number of development tools which eventually makes app development faster and easier. In this blog, we will be highlighting some of the key tools of the internet that you should be looking at if you are willing to develop IoT apps.

Internet of Things

Important development tools in IoT:

  1. Arduino– It is an Italy based company that provides an open-source prototyping platform that has an easy to use hardware and software. It comes in the form of an Arduino programming language and Integrated Development Environment.  It is one of the most preferred choices in all IoT development tools. 
  2. Eclipse IoT– If you are ordained to build Cloud platforms, IoT devices, then the tool that you should be looking at is Eclipse IoT. It is a collaboration of various companies that are aiming at establishing IoT open technologies. Eclipse IoT includes application frameworks and services. Besides, it also has an open course implementation of IoT protocol.  
  3. Tessel 2– It can be used for building IoT solutions and prototypes. It has the capacity to hold different modules including RFID, GPS, camera and the accelerometer. If you are a Java developer and are proficient in Node.JS, then you can use Tessel. 
  4. Kinoma- Is is a semiconductor hardware prototyping platform that allows a connection between two devices without comprehensive knowledge about JavaScript. The tool offers all the features which are required for building small IoT applications like the movement of sensors, controlling temperature, etc. This app is free on iOS and Android app. 
  5. Node-RED- This is a visual tool for wiring the IoT. The wiring here means connecting hardware devices, online services, APIs, etc.  This app is built on Node.JS. With the help of this tool, the developers can connect devices and APIs using browser-based flow editor. Node-RED can run on Raspberry Pi, and it has more than 60,000 modules.
  6. IBM Watson– This API allows you to connect a wide spectrum of cognitive computing features to your IoT applications. It can also be used to predict the future. It helps to unveil chatbots that can understand the natural language. It can be used on messaging platforms an websites that can be seen on different devices. 

Conclusion- We are living in the digital era, which is changing and leading to the rise in the demand for tools that can make working easy and simplified. IoT has the potential to connect different devices together so that they can work seamlessly. If you are also willing to make a future in this field, it is the right time to enroll for certification in IoT and become an Internet of Things expert & Internet of Things Certification Developer. Global Tech Council offers you the right platform where you can learn about IoT and different tools like the ones mentioned above. It will help you become more equipped and stay abreast with the latest development in this field.

How AI Will Impact Financial Services?

A perfect example of AI changing even the business landscape, even in the traditionally conservative areas, is the rising popularity of AI in the financial services sector.

Understanding the term ‘Artificial Intelligence’

Artificial intelligence deals with using computers to understand human intelligence, and it is the science and of making intelligent machines, especially intelligent computer programs.

How AI will Impact The Financial Services

How AI Transforms the Finance Industry

Let us now understand the various ways in which AI transforms the finance sector.

  1. Fraud Detection and Management

Any business will only want to minimize the risk conditions surrounding it. This holds true for financial institutions also. Financial institutions take fraud very seriously as the loan a bank gives you is someone else’s money. AI helps in fraud detection as it uses past spending behaviors for pointing out odd behavior such as using a card from another country hours after it was used somewhere else or attempting to withdraw an unusual sum of money. An excellent feature of fraud detection using AI is that the system does not have any qualms about learning. If a red flag is raised for a regular transaction and a human being corrects it, the system can learn from experience and make sophisticated decisions.

  1. Trading

Investment companies rely on data scientists and computers to determine future patterns in the market. Trading and investments depend greatly on the ability to predict the future accurately. Machines are great at this as they can crunch large amounts of data within a short time. Machines can also predict and observe patterns in past data. AI can be used to suggest portfolio solutions for fulfilling each person’s demand. So, one can even count on AI for decisions such as buying, holding, and selling stocks. Those with lower risk appetite can use AI to receive alerts for when the market is expected to fall so that they can make a decision about staying in the market or moving out.

  1. Risk Assessment

AI will certainly succeed in the financial services sector as the very basis of AI is learning from past data. Let us consider the example of credit cards for a better understanding. Today, the credit score is used to decide who is eligible for a credit card and who is not. But grouping people into haves and have-nots is not good for any business. Instead, using data such as the number of loans that are currently active, each individual’s loan repayment habits, and the number of existing credit cards can be analyzed to customize the interest rate on a card. This is where AI comes into the picture. As AI is data-driven, it can scan through these records to make recommendations on loans and credit offerings.

  1. Managing Finances

Managing finances in today’s materialistic and well-connected world is a challenging task for many. One most recent development in the AI-based wallet is personal financial management (PFM). The wallet that has been developed by a startup based in San Francisco uses AI to build algorithms to aid consumers in making smart decisions while spending their money. The wallet creates a spending graph by accumulating the data from your web footprint.

As AI is built upon machine learning, it learns over time, analyzes vast volumes of data, and offers fewer possibilities for mistakes. Though advocates of privacy breaches on the internet may find it offensive, this is what the future awaits. AI proves to be extremely useful in managing finances for both small-scale and large-scale investments.

Conclusion

AI is undoubtedly the future of the financial services industry. It will soon replace humans and provide faster and efficient solutions. To learn more about artificial intelligence and become an AI expert, check out the artificial intelligence certifications offered by Global Tech Council.

What Are the Differences Between Data Science & Big Data?

Data is the king, and most of the companies are relying on this to create strategies for their growth and gaining an edge over their competitors. As per IBM, in 2012, around 2.5 billion gigabytes of data were created. This number has grown steadily since then. There has been huge growth and reliance on data science and big data. The latter two are being worked on, and many companies are now looking for Big Data experts and Data Science experts. Many people confuse the two terms, but these are different aspects of data that has a different approach. 

What Are the Differences Between Data Science & Big Data

How is big data different from data science?

If you are planning to learn more about data science or big data, it is important that you must know how they are different.

Data Science– It is a field that involves the use of different statistical tools like statistics, data cleansing, mining, and programming to prepare and along with big data for better analysis such that meaningful insight can be deciphered from it.

Big Data–  It is the collection of data that comes from different sources in different formats. You can use it for analyzing the insights which can help in better decision making.

Tabular representation of the difference between Big Data and Data Science: 

FieldBig Data Data Science
Uses Financial Services, Retail, and CommunicationDigital advertisements, internet searches
SkillsAnalytical skills, statistics, mathematics, Computer science, business skillsIn-depth knowledge of SAS, Python, Hadoop, SQL, working on unstructured data
Salary$622,066 per year$113, 436 per year

Nowadays, companies are looking for specialized skills in different fields so that they can use this data to derive a conclusion, which can eventually help in the growth of their business. If you are also willing to learn about Big Data and Data Science, then Global Tech Council is offering a certification program in Big Data certification and Data Science certification. After successful completion of this program, you will become eligible to get a good-paying job across different industries.

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