Why there is more scope for Data scientist?

Job Opportunities in Big Data. 

You can never get away from statistics in your life. Banks, private companies, government agencies, administrations and many other places need a lot of information and statistics. Of course it is a kind of data. Data needs to be stored and saved. So i. In T., the work on Big Data is in full swing. The process of improving business, making decisions and backing up information on competitors, storing statistics and other practical information, all fall into Big Data.

Income. T. Bigdata is a great option from a career point of view after engineering. This is because the demand for analytics professionals, data scientists is increasing in many multinational and Indian companies. Many tools have to be used while working in data analytics. For this you need to have his skills. You can work in data analytics using many tools like Table Public, OpenRefine, RapidMinor, Google Fusion Tables. Hadoop is an open source distributed Big Data framework that handles data processing, complex data management and its storage for large data applications running in clustered systems. It mainly involves information processing.

BigData's work also includes robotics technology, such as machine learning and Amazon webservices. Concepts such as Artificial Intelligence, Machine Learning have no alternative but big data for information management. And as you know, artificial intelligence technology is gaining momentum today, which is why the demand for BigData is growing.

So this is an open way to build a career in BigData. For this, it is necessary to acquire the skills of the above mentioned tools. Courses are also available at private institutes in cities like Pune, Chennai, Hyderabad, Bangalore. As soon as you become a BigData expert, you get a job as a BigData Analytics Consultant, Analytics Associate, Business Intelligence Analytics Consultant, while experienced people get a job as a BigData Analytics Architect, Metrics or Analytics Specialist. Companies are also offering good packages for this.

Do you use Social media only for enjoyment ?

We always look to spread our thoughts to all our followers in society. Our Constitution gives everyone the right to read and write. The internet is being used as a medium for this. Needless to say, this is making today's youth talkative. This situation will continue to escalate. Social media has tremendous power. Social media provides a platform for everyone's freedom of expression, allowing people to share their thoughts. Suppose we want to ask some social and cultural questions, in the past, we had to advertise in newspapers, but now we are expressing ourselves through social media, of course, everyone who does that is a journalist.

Social networking sites have reduced the distance between each person and the number of contacts is increasing. Similarly, corporate access to social media has made it easier for companies to interact directly with customers. Advertising through social media is becoming more effective and widespread than traditional advertising.

In addition, we can provide employment for ourselves today. Due to the popularity and expansion of social networking sites, many commercial companies and businesses use these websites for advertising. E.g. - Advertisements of other websites, advertisements of new products in the market, advertisements of jobs, etc. Websites make a lot of money from these advertisers. There are many options for this such as YouTube channel, news portal, blogging, advertising promotion.

This is where the concept of social media marketing comes into play. The process of getting attention from social media sites is called social media sales. At the heart of this algorithm is trying to create eye-catching text and reach out to as many people/readers as possible. An event, goods, services, organization, etc. Texts about the Internet via e.g. Websites, social networks, instant messaging, news, etc. Can reach millions of customers in a jiffy.

Increase followers by creating official pages on Facebook, advertising your product, increasing subscribers on YouTube, writing blogs, making it viral on the Internet, sharing information about your product with people, and paying on other social media sites such as Twitter, WhatsApp, Instagram. Comes. Companies that provide services or manufacture products for such tasks have a specialized social media team. There are jobs like social media sales executive, social media sales manager or you can run your own business as social media sales. All its pieces of training and tricks are available online. The use of social media is gaining momentum in all areas e.g. Political, cultural, religious business, etc. So the demand for social media marketing is increasing.

What is Deep Learning ?

 What Is Deep Learning?

It's Nothing but AI and some kind of machine learning.

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. NLP has a major role in DL. (NLP - Natural language processing)

How Deep Learning Works?

Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. Deep Learning gives more accuracy and efficiency in AI.  This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.

However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information, to provide more efficiency. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to AI systems for automated support.

Deep learning learns from vast amounts of unstructured data that could normally take humans decades to understand and process.

Deep Learning Versus Machine Learning

One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data.

In case, If a digital payments company wanted to detect the occurrence or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set and point out any anomaly detected by the pattern.

Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis i.e. understanding with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a nonlinear approach.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning nonlinear technique would include time, geographic location, IP address, type of retailer, and any other feature that is likely to point to fraudulent activity with more accuracy. The first layer of the neural network processes a raw data input like the amount of the transaction and passes it on to the next layer as output. The second layer processes the previous layer’s information by including additional information like the user's IP address and passes on its result.

The next layer takes the second layer’s information and includes raw data like geographic location & makes the machine’s pattern even better. This continues across all levels of the neuron network. DL is a very depth level of a machine learning concept. 

We will keep posting in upcoming blogs.. stay tuned with the site.

 

What is Machine Learning ?

Now a day, Robotics and Data science growing in the world and there are several sub-branches are come up like Big Data, Machine Learning, Cognitive intelligence, etc. In this article, we are going to speak about Machine Learning.


What is Machine Learning ? 

Machine learning is the deep scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without explicit instructions, relying on patterns and inference. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", to make predictions or decisions without being explicitly programmed to perform the task. Without Math ML can not be completed, where it is infeasible to develop an algorithm of specific instructions for performing the task. The study of mathematical optimization delivers methods, theory, and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. Domain knowledge is the most important part in ML to get accuracy.

How machine learn itself?

The concept here is, Machine is learning itself from the experience. Machine learning can have many tasks, and they are classified into several broad categories. In supervised learning, the algorithm builds a mathematical model from a different set of data that contains both the inputs and the desired outputs. For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with and without that object (the input), and each image would have a label (the output) designating whether it contained the object.n special cases, the input may be only partially available or restricted to special feedback. Semi-supervised learning algorithms develop mathematical models from incomplete training data, where a portion of the sample input doesn't have labels.

Classification algorithms and Regression algorithms are types of supervised learning.

• Classification algorithms are used when the outputs are restricted to a limited set of values. e.g. Email Filtration

• Regression algorithms are named for their continuous outputs, meaning they may have any value within a range. Examples of a continuous value are the temperature, length, or price of an object.

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining (Not Data warehousing) uses many machine learning methods, but with different targets; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a pre-processing step to improve learner accuracy.

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). 

Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a pre-processing step to improve learner accuracy.

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.

Machine learning (ML) and statistics are closely related fields. According to Michael I. Jordan, machine learning ideas have had a long pre-history in statistics from methodological principles to theoretical tools. He also suggested the term data science as a placeholder to call the overall field. Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.