DEV Community: Anushya

An overview of IR 4.0 course and its importance

Anushya — Thu, 26 Dec 2019 06:06:21 +0000

The industrial revolution has changed everything that we know. So many things have changed and the whole definition of IR has been jumbled up. The principles and philosophies that were introduced by the technological revolution founders have become obsolete and new things have come to light. In the past, it was about minimizing workers' movement and instead, moving the job. Today, we are seeing a lot of robotics and automation and this is why the above concept is being abandoned. The introduction of IR 4.0 was met with resistance because people did not have awareness and were ignorant of the benefits that were in store.
What the course offers
The program covers different IR 4.0 branches. It helps the learners understand how these branches can be applied to businesses to get the kind of value that will propel them forward.
Most people who take the course are decision-makers in organizations or those who form the decision-making teams in different organizations. It helps them realize the areas that can be used to bring about favorable changes.
The road has been long. It all started with mechanization when we entered the nineteen century. In the 70s, we witnessed automation. In the centuries that followed, the internet, and information have been a big thing. By adding connected intelligence to social fabrics and businesses, we are advancing at great speeds and conducting business is not the same as before.
What the course is about
Since it is obvious that things never remain the same, it is important to try to make predictions on how the future will be. The IR 4.0 course aims at discussing predictions and changes that may happen in the future. This includes things like the fading out of credit cards, using bitcoin as currency, how mobile internet will help narrow down wealth gap, extinction of e-business models, and the end of mobile phones and so on.
The course aims at explaining the kind of changes that could possibly occur because of using digital intelligence extensively. Internet intelligence will be availed through the internet, mobile, and all-pervasive computing like factories, offices, and homes being knitted together on a cyber-physical kind of system.
There are enabling tools like cyber-physical systems, internet of things, big data, and cloud computing that are also explained in the course. Also, collaborations, intelligence, and automation are other things that are discussed in the course while referencing smart services and products, smart cities, smarty manufacturing, and all the challenges and opportunities that they have to offer.
IR 4 cannot be considered to be a technical course. Focus is usually on the organizational readiness, competencies, and skill gaps that have to be imparted on workers so as to leverage industry 4.0 power.
This course is ideal for all learners taking part in all disciplines as long as they are interested to learn more about the big changes that are happening to the society today, and what the future could possibly bring.
Resource
Learners need to take this course to have a better understanding of the different stages that are involved in industrial revolutions. This can help them understand and plan for the future based on the skills that will be needed and the kind of work that will be in high demand.

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COMPONENTS OF THE DATA SCIENCE TECHNOLOGY

Anushya — Wed, 04 Dec 2019 03:53:37 +0000

INTRODUCTION
We all hear about data science technology frequently. No doubt, data science technology is the most overwhelming technology for the present time. But data science technology is the blender of different technologies. Some of the most common technologies are artificial intelligence, machine learning, IoT, data analytics, big data, etc. These technologies contribute to data science technology in different ways. Moreover, there are many sub-processes and components in data science technology.
COLLECTION OF THE DATA
Data science is also known as the data-driven approach. As it is very clear from its name, the most important requirement is the data. The companies have to gather the data of the customers from different sources. Generally, the data is gathered from the database of the customers. As mentioned above, the big data is a part of the data science technology. Then, what does big data stands for? Many people think that big data is the kind of data with huge volume. But this is not the exact definition of the big data. Big data is identified by the following properties.
Velocity
Veracity
Volume
CLEANING THE DATA
Cleaning the data is the most important process. In this process, the data is clarified. In such process missing values are filled, duplicate data is removed, improper values are removed and the data is arranged in the correct way. All these tasks take place under this process. From this process, all the processes are considered under the data analytics until and unless the useful information is squeezed out from the data. Data analytics is also a part of the data science technology. The processes taking place under data analytics are extraction, transformation, visualization and modeling of the data. All these processes are done to extract useful information from an ample amount of the data.
ANALYSIS OF THE DATA
The data analysis is performed by the methodology mentioned above. There are different types of data analysis and the type of analysis to be used is based on the type of problem you are working on. Data analysis includes some methodologies like prescriptive analysis, descriptive analysis, and predictive analysis. For this reason, a better understanding of the problem is necessary so that the type of analysis can be decided. Data cleansing operations are performed to get data structured. Moreover, the data mining process is applied to the data to discover the hidden patterns from huge amount of the data.
BUSINESS INTELLIGENCE REPORTS
The results can be obtained after analyzing the data. Then the necessary actions can be taken accordingly. The results are based on the data mining procedures applied. Moreover, the results also depend on mathematical models. These mathematical models accept the data as an input and provide predicted values accordingly. The technology, which comes into light at this stage is machine learning.
CONCLUSION
Data science is a very effective technology. This technology has a great demand. Students can choose this technology as a career option and can take a course on it.
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COMPONENTS OF THE DATA SCIENCE TECHNOLOGY

Anushya — Fri, 29 Nov 2019 03:57:41 +0000

INTRODUCTION
We all hear about data science technology frequently. No doubt, data science technology is the most overwhelming technology for the present time. But data science technology is the blender of different technologies. Some of the most common technologies are artificial intelligence, machine learning, IoT, data analytics, big data, etc. These technologies contribute to data science technology in different ways. Moreover, there are many sub-processes and components in data science technology.
COLLECTION OF THE DATA
Data science is also known as the data-driven approach. As it is very clear from its name, the most important requirement is the data. The companies have to gather the data of the customers from different sources. Generally, the data is gathered from the database of the customers. As mentioned above, the big data is a part of the data science technology. Then, what does big data stands for? Many people think that big data is the kind of data with huge volume. But this is not the exact definition of the big data. Big data is identified by the following properties.
Velocity
Veracity
Volume
CLEANING THE DATA
Cleaning the data is the most important process. In this process, the data is clarified. In such process missing values are filled, duplicate data is removed, improper values are removed and the data is arranged in the correct way. All these tasks take place under this process. From this process, all the processes are considered under the data analytics until and unless the useful information is squeezed out from the data. Data analytics is also a part of the data science technology. The processes taking place under data analytics are extraction, transformation, visualization and modeling of the data. All these processes are done to extract useful information from an ample amount of the data.
ANALYSIS OF THE DATA
The data analysis is performed by the methodology mentioned above. There are different types of data analysis and the type of analysis to be used is based on the type of problem you are working on. Data analysis includes some methodologies like prescriptive analysis, descriptive analysis, and predictive analysis. For this reason, a better understanding of the problem is necessary so that the type of analysis can be decided. Data cleansing operations are performed to get data structured. Moreover, the data mining process is applied to the data to discover the hidden patterns from huge amount of the data at 360DigiTMG
BUSINESS INTELLIGENCE REPORTS
The results can be obtained after analyzing the data. Then the necessary actions can be taken accordingly. The results are based on the data mining procedures applied. Moreover, the results also depend on mathematical models. These mathematical models accept the data as an input and provide predicted values accordingly. The technology, which comes into light at this stage is machine learning.
CONCLUSION
Data science is a very effective technology. This technology has a great demand. Students can choose this technology as a career option and can take a course on it.