Cell phones lead our new generation to be more social and get updated. Our children can get any news as per their interest. Also, meet with new people who share a similar view. It cannot be possible offline. Presently, you meet your friend online and get involved. The situation is the same in the classroom. If you are not interested in mixing up with the new student for completing a particular task, you can have the option to choose someone else. In today’s digital world, many children have become disconnected from their families and face-to-face friends and now find someone to communicate with within the digital world.
At first, you may think that children don't require studying data utilization due to their efficiency in using smartphones. However, parents and teachers need to understand that using a phone and data utilization are two different things.
In Switzerland 2016, the world economic forum in Davos discussed the Fourth Industrial Revolution became a hot topic in Korea. One of the main themes among the discussion topic was the diminishing job opportunities due to the fourth industrial revolution.
In March of 2016, Lee Se-dol, one of the best professional Go players in the world was defeated by the self-taught AlphaGo system. The consequence was shocking to game fans. Hence, big data and artificial intelligence (AI) became familiar words to all of us. While big data and AI are certainly symbols of humankind's advancement, they also make us question the meaning of human existence. And many now believe our future depends on big data and AI.
As per the current situation, a big data or AI expert is inevitably considered the most promising occupation in the 21st century. For future societies, the residents must have data literacy. Even if you are not an expert still, basic knowledge would be required for your literate eligibility.
Big data experts have become more dominant in future societies when a large amount of data is required to process. The data importance has increased at that time. Let’s take a look at Google. This company successfully predicted the prevalence of influenza about 10 years ago. Influenza kills many people every winter, it is crucial to identify how many cases are found in specific regions. But to our surprise, data experts who had no relevant knowledge accurately predicted the area and timing of the influenza epidemic. Like this, if you know how to analyze data, you can forecast the weather, traffic, and even the team to win a game. Data and algorithms can outdo expert knowledge and experience.
Hence, Big data experts, one of the most promising future job options for your children. If you can accurately predict the future, you can surely make adequate preparations to mitigate the consequences. This future prediction is quite tedious due to the involvement of the multiple variables that occur. The job of Big data expert requires the skills needed to predict the future by analyzing data, the determination to continue examining a variety of data, and many practices to sharpen those skills.
The discipline is attracting a lot of colleges and teachers. Every college opens courses about theories and algorithms required for big data analysis. Many teachers have taught these and related courses for several years. However, they started to have doubts that ‘teaching a theory is the right way in this Fourth Industrial Revolution era.’ If ‘what’ data to analyze is more important than ‘how to examine, wouldn't they teach how to think broadly rather than how to analyze data? After the contemplation, they opened a course to read books to think about data, instead of theories and analysis methods.
In the book, Factfulness by Hans Rosling. His content in his book was nothing new. It is the data that the general public can easily access, such as populations, gross domestic product, birth rate, and morbidity of various countries. His method of analyzing the data and statement of his case is nothing new, either. He divides countries into several groups by income and draws the average life expectancy by country and continent in a teardrop shape on a world map. No high-level analysis methods you learn in the Big data analysis or a machine-learning classroom appeared. Then why is the world paying attention to this book? The answer lies in the viewpoint to analyze the data.
When you read the book, you looked up the map of Africa. Before that, you may never consider any data from Africa meaningful because you perceived Africa as one big lump.
However, when you read the data from Africa and became inquisitive, you will realize that Africa had more than 50 countries and as much population as China. You could deduce that by using its population as a weapon, Africa would achieve economic development and become a continent of people welcomed as tourists, not refugees, in every country in the world in 50 years.
The above example represents the importance of data rather than simply reading the data is to accept it without prejudice and find meaningful stories from it. More than ever before, it is principal to identify the correlation of the uniquely human trait of empathy that machines cannot fulfill and data and understand why it exists.
Greta Thunberg, the youngest environmental activist in Sweden, realized the crisis in the Earth's environment on her native land. In Stockholm, in front of the National Assembly building, she held a one-man protest every Friday. Then something surprising happened; millions of students who learned about her movement joined the revolt from all over the world.
In this era, where we can find the data virtually everywhere, we can analyze extensively to search for the desired information. However, making decisions based on that data and taking action is not easy. Even though AI like AlphaGo from Google and Watson from IBM surprised us, it was Thunberg who touched our hearts by taking matters into her own hands. It is time for schools and societies to think about new data literacy education. Hence, there will be more Thunbergs who are brave enough to state their case on the world stage.