Know the Various Technologies Resulting from Data Science Implementation

Humans have an insatiable nature. This trait can certainly be a double-edged sword for humans themselves depending on how they are usen. Scientists are some humans who have the nature of never satisfied and apply these traits into positive things, they continue to create knowledge that can continue to make it easier for us to live, one of the revolutionary innovations is the science of Data Science.

Thanks to the existence of Data Science many technologies have sprung up, making it easier for humans to move. In addition to the many innovations provided by Data Science science, this science also makes it easier for humans to store data, so that the data can be used effectively and efficiently. Of course, with the development of this science, we may be one step closer to achieving what we have not achieved that is still everyone’s wishful thinking, such as flying cars, landings to Mars, and so on. !.!.!
Data Science has also often been encountered in daily activities but often we are not aware of the existence of the results of this Data Science. Here are the results of innovations developed by Data Science.

Also read Panduan Sederhana Belajar Menjadi Data Scientist untuk Pemula

1. Artificial Intelligence (AI)

Artificial intelligence (AI) is a machine that is programmed to think like a human or his actions. AI itself was created in 1956, although only 65 years old but AI is greatly developed due to data volume, advanced algorithms, and increased computing power and storage

Here are the benefits that are presented by using AI technology:

• Minimize Errors

AI is capable of working with a high level of accuracy, and is consistent. This will minimize the mistakes that are usually made by humans. AI can also study large amounts of data to provide the best decisions. Therefore, its use can take action in order to minimize the risk of loss.

• Speed up time at work

In the process of AI there are terms of learning, reasoning and self correction. These three points will make artificial intelligence has extensive knowledge. So, artificial intelligence can get the job done with a faster time span.

• Doing Human Tasks

If your home carries the concept of a smart home Google and Alexa is an important component they are assigned as your personal assistant. Simply give the Ok Google command to activate it and then give a command like “Turn off the light” and if your lights will be turned off by Google.

Although AI itself has a myriad of benefits but we also have a big challenge to develop AI itself. AI can learn only using data there is no other way to enter knowledge without data. That’s what causes data processing to be precise if improper data processing will be reflected in the answers generated by AI.

AI also has the lack that it must do a definite task, AI cannot do tasks that do not have clear regulations, such as detecting fraud or providing legal advice. Unlike the AI technology we see on the big screen, which is where AI robots can have feelings and even behave like humans.

2. Machine Learning

Machine learning is a machine that is developed to be able to learn by itself without the help of its maker. Machine learning includes other disciplines such as statistics, mathematics and data mining.

Machine learning is one of the branches of artificial intelligence (AI). In an AI, a lot of machine learning is needed to obtain existing data and learn that data in order to perform certain tasks.

Surely among you are still confused with machine learning, how they learn and develop themselves. Just like human machine learning has diverse learning techniques that are supervised learning and unsupervised.

• Supervised Learning

This technique is usually used on items that have complete information. Machine learning will give the item a label according to the same categories and also place it according to that category. This technique aims to provide a target to the output carried out by comparing experiences from the past.

• Unsupervised Learning

Conversely, by supervised, this technique is used when the information related to the item is incomplete. So machine learning does not have a reference to label goods. This technique is used to look for certain structures or patterns that do not have labels.

Of course machine learning also requires maintenance that is not easy, this is the task of a Data Scientist to make sure everything runs smoothly and properly.

Also read Siapkan Diri Menjadi Data Scientist Profesional dengan Kenali Kompetensi Ini

3. Big Data

Big data is a larger and more complex data set, especially from new data sources. These data sets are so numerous that traditional data processing software cannot manage them. However, this large amount of data can be used to address business issues that you previously couldn’t handle.

One of the easiest to understand explanations about data is the collection and use of information from a variety of sources to make better decisions. Big data is arguably a concept of our ability to collect, analyze, and understand the considerable amount of data that comes in every day.

Big data is also useful for sources captured by data-driven technologies. Without complete data AI and machine learning will fail in understanding what to do, because they act in accordance with the existing data if the data is not accurate then the resulting results will not be accurate.

4.Study Science Data with DQLab UMN

DQLab UMN is a Data Science learning center that offers online courses for those of you who want to start learning Data Science. DQLab itself has given birth to data practitioners who are proficient in their fields. With DQLab you will learn in a structured manner with case studies and data that match those in the field. DQLab also provides a forum for sharing with 95,000++ DQLab members, as well as with expert data experts.

*by Annissa Widya Davita | DQLab

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