The basic principle of robotics and AI

-
Image
Artificial intelligence applied to robotics development requires a different set of skills from you, the robot designer or developer. You may have made robots before. You probably have a quadcopter or a 3D printer. The familiar world of  Proportional Integral Derivative  ( PID ) controllers, sensor loops, and state machines must give way to artificial neural networks, expert systems, genetic algorithms, and searching path planners. We want a robot that does not just react to its environment as a reflex action, but has goals and intent—and can learn and adapt to the environment. We want to solve problems that would be intractable or impossible otherwise. Robotics or a robotics approach to AI—that is, is the focused learning about robotics or learning about AI? about how to apply AI tools to robotics problems, and thus is primarily an AI using robotics as an example. The tools and techniques learned will have applicability even if you don’t do robotics, but just apply AI to
1 comment

Why Data Science Is Important ?

-


In a world which is increasingly becoming a digital space, organizations deal with Zetta and yottabytes of structured and unstructured information every day. Evolving technologies have enabled better cost savings and smarter storage spaces to store critical data.

The data science field employs mathematics, statistics, and computer science disciplines, and incorporates techniques like machine learning, cluster analysis, data mining, and visualization.

Data science is modern-day large scale statistical inference. It is a way to evaluate systems and to solve problems using data, using the tools of statistical analysis, machine learning, and related tools.

Data science has the potential to enable business problems of various kinds to be solved by developing applications that leverage large data sets. It can also more broadly impact society, by playing a part in society, politics, health and well being - in this sense, the true potential of statistics and mathematics in real life can be unlocked using the tools of data science.

 Future of data science 

Data science is a growing field, and it is sought out by many multinational companies. Due to the lesser number of skilled, proficient data scientists in comparison to the demand; there is a void that has been created. There is a huge demand for Data Science related jobs, and by 2020 estimate calls for 2.7 million job postings for data science and analytics roles.

There is a huge demand for Data Science related jobs, and by 2020 estimate calls for 2.7 million job postings for data science and analytics roles. Data science is a growing field, and it is sought out by many multinational companies today. Due to the lesser number of skilled, proficient data scientists in comparison to the demand; there is a space that needs to be filled and learn the best data science course online from onlineitguru website

Data Science is thus very important. The growth of self-driving cars, robots, chat-bots, etc are giving a boost to Data Scientists. Moreover, Data Science and Analytics is the most sought after industry in the world today. Simply because it is important and the pay is high.

Why it is important? 


The reason is that we collect more and more data (from mobile devices, sensors, etc.) and that the storage of data is cheap. Thus, there are a huge amount of data that can be analyzed to understand the past or predict the future, and support decision making. Analyzing data by hand is time-consuming. Hence, the field of data mining has become an important research area as it can allow analyzing a large amount of data.

Note that “data science” is not something new. Research on data mining started as early as in the late 1980s. Research on statistics also is not something new. But there are new buzzwords like “data science”, “big data” and so on...Actually, it is even hard to agree on a definition of what a “data scientist” would be. But the overall idea is to analyze data to understand the data, make predictions, and make decisions.


 this is just an example let’s move on to other points -

Industries needs data to help them and make careful decisions and data science churns raw data into meaningful insights. Therefore industries need data science.

Data science is used by almost all the industries some major sectors are healthcare, finance, banks, business, startups, etc. Because all the industries required data science for handling with a large volume of data, this increases its importance

Data Science is the career for tomorrow. Industries are becoming data-driven and new innovations are being made every day. Industries require data scientists to assist them in making a smarter decision. To predict the information everyone requires data scientists. Big Data and Data Science hold the key to the future.

Data Science is important for better marketing. Companies are using data to analyze their marketing strategies and create better advertisements. Decisions can be made by analyzing customers feedback. Therefore companies using data science to run a particular campaign.
Read more online data science certification

Comments

Post a Comment

Popular posts from this blog

iOS 13 Features

-
Image
In other words, iOS 13 should work on iPhone models from iPhone 6s onward, in addition to iPad mini 3 and later, the second-generation iPad Air and all later iPads, as well as the sixth-generation iPod touch. This was originally claimed by Israeli site The Verifier which in January said iOS 13 would drop compatibility for the iPhone 5s through iPhone 6s models. The reported removal of iOS 13 support for the iPhone SE and iPhone 6 models is very questionable because major iOS software updates are typically optimized for Apple’s processor models, not specific devices. If iOS 13 is indeed going to drop support for all the iPhone models with the Apple A8 chip, such as the iPhone 6/Plus range, then it will make no sense whatsoever to also remove compatibility for iPhone SE, which runs the newer Apple A9 chip and is still being manufactured in India. Join the Swift couse Equally puzzling is alleged support for the sixth-generation iPod touch because that device sports the same A8 pr
Read more

Data Science and Machine Learning

-
Image
Data Science and Machine Learning: Broad and Narrow Terminology First of all, data science is really a broad, overarching category of technology that encompasses many different types of projects and creations. Machine learning has been one of the biggest advancements in the history of computing, and now it is believed to be capable of taking on significant roles in the field of big data and analytics. Big data analysis is a huge challenge from the perspective of businesses. For example, activities such as making sense of huge volumes of varied data formats, data preparation for analytics and filtering redundant data can consume a lot of resources. Hiring data scientists and specialists is an expensive proposition and not within every company’s means. Experts believe that machine learning is capable of automating many tasks related to analytics – both routine and complex. Automating machine learning can free up a lot of resources that can be used in more complex and innovative job
Read more

Data Scientist Interview Question

-
Image
If you want to Become data scientist you have must know these questions. A data scientist should not only be evaluated only on his/her knowledge on machine learning, but he/she should also have good expertise in statistics. Prepare from onlineitguru is providing data science interview questions Explain The Various Benefits Of R Language? The R programming language includes a set of a software suite that is used for graphical representation, statistical computing, data manipulation, and calculation.      what is data science? Data science is a "concept to unify statistics, data analysis, machine learning, and their related methods" to "understand and analyze actual phenomena" with data. Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them.         What is selection Bias? Selection bias occurs when the sample obtained is not representative of the population intended t
Read more