Monthly Archives: January 2022

Big data is? Big data used in all industries.

Recently, through the combination of purchase history information, web log analysis, and location-based services, we have established a technology foundation for consumers to propose the services they want at the right time and place. Compared to the analog era, the scale of 데이터 data production in the digital era has been unpredictably produced, resulting in a big data society. CRM means conducting various marketing activities such as dataware houses that integrate data held by companies and customer data analysis to maintain customers and prevent deviations. In particular, text information distributed on 블로그 or SNS can be analyzed not only by the name of the person who wrote through the content, but also by the connection relationship of the other person who communicates. With the use of PCs, the Internet, and mobile devices becoming a part of daily life, the data left by people everywhere is increasing exponentially. However, the current big data environment refers to a paradigm shift in terms of quality and diversity as well as the amount of data compared to the past. Companies began customer relationship management activities in the 1990s to activate marketing activities using their customer data. ○ Master’s and Doctor’s Big Data Private Education craze ○ Small business owners, overcoming COVID-19 with big data technology ○ Government, big data with big data techniques means massive data generated in a digital environment, short generation cycle, and large-scale data including 문 text and video data. Let’s find out how to utilize big data that provides professional training so that we can utilize data that is considered a resource that has a clear outlook and does not disappear. Big data academy, which provides education to utilize, provides practical training on how to use through various education such as 데이터 data science 데이터 data mining 데이터ᄇ database SQL SQL 하 Hadup R R programming 머신 machine learning / deep learning. Data science is a technology called data science that functions the values of data and converts data according to input values to operate software. Recently, they also add value or complexity. How to Use Big Data Big Data has a lot to do with data science. In addition, using photos and video contents through PCs has already become common, and broadcasting programs are viewed on PCs or smartphones without going through TV receivers. Today, we learned about data science to utilize big data and big data, and if you want to know how to use it in more detail, please consult through the consultation application link below. The characteristics of big data are summarized in 3V. Unlike in the past, recent big data has been produced as every move of 일 due to the increased amount of 데이터의 data and the emergence of 사람 people’s consumption patterns and GPS. Such diverse and vast amounts of data are attracting attention in that they can be used as important resources that determine the advantage of future competitiveness. From this point of view, big data, like coal during the industrial revolution, is regarded as an important source for innovation, productivity improvement, and competitiveness during the IT and smart revolutions. Although UCC, which is produced by users themselves, and text messages generated by mobile phones and social network services (SNS), are showing different patterns not only in the rate of data growth, but also in form and quality. It means V Volume (amount of data) V Velocity (data generation rate) V Variety (data diversity. . Companies’ CRM activities include affiliate marketing using data from affiliates as well as their customer data. Many job seekers are looking into big data and data science courses because key technologies that will lead the 4th industrial revolution, such as AI artificial intelligence, IoT IoT IoT, Internet of Things, and drones, can utilize big data and data science technologies to implement higher-quality technologies. Attempts to analyze large-scale data to find meaningful information have existed in the past. Therefore, the more 데이터 there is, the more 할 big data and data science 사이 win-win, and 이를 the software that utilizes it is representative AI AI artificial intelligence and computerized automatic program.

U.S. unemployment data

U.S. unemployment data are quite useful indicators.
This is because the United States is relatively free to hire/discharge (Korea is at its disposal) so it quickly reflects the labor market situation.

A low unemployment rate means that there are many people working, so it means an economic boom (which means companies need a lot of manpower to make products), and a high unemployment rate means a recession.

Of course, it’s not released every day, but there’s a time difference of more than a month to announce it only, and this data can be found on FRED.

Unemployment Rate

We take data from the US Department of Labor called BLS and provide them in chart format at FRED.​

1) Recession and unemployment rate.

The gray area is a sign of the recession in the United States, and if you look at recent years, you can see the dot-com bubble in 2000, the Lehman crisis in 2009, and the economic crisis from COVID-19 in 2020.

Unemployment rises unprecedentedly during this recession. Because companies predict that consumption will decrease during recession and try to keep profits as much as possible by reducing labor costs.

2) Investment ideas.

But if you think about it backwards,
You can see that there is always a recession when the unemployment rate is low. When we hum in a booming economy, there’s always a crisis.

Judging from this, it is concluded that we should be careful of investment when the unemployment rate falls and stabilizes.

** A place to check data.

Unemployment Rate

Data vs Real estate – Predictive analytics using Big Data for the real estate.

When I first studied real estate and started investing, I was obsessed with reducing the likelihood of failure. I’ve been looking for a mysterious medicine that can eliminate risks in investment activities that necessarily involve high risks. Big data is what came to me like fate when I found a famous medicine. They use big data to invest in real estate that can rise and sell real estate that will fall. Being completely persuaded, I drank a masterpiece of medicine. Today, I’d like to talk about whether the investment was really effective and whether the real estate big data that I drank can really meet the future.
Big data and real estate.
Big data refers to the act of processing large amounts of data to derive necessary information. This is because technological advances have made it possible to collect and analyze large-scale information. In real estate, there are many attempts to collect all relevant data, process it in a good form, and further guess the future. Representatively, there are sites such as real estate magazines, Acyl, and Hogangnono.

This is Richigo’s apartment investment score analysis.

This is Darwin Jung’s reconstruction score.
Richigo, which provides future prospects and investment scores beyond information provision, and professional real estate prop-tech companies such as Darwin Brokerage, which express the possibility of reconstruction apartments with scores, are also emerging.
The beginning of big data real estate investment.
When I started studying real estate, I read a lot of REM’s books. I learned and became interested in running a real estate big data system called zip4. Then, at the bookstore, I recently bought Richigo Kim Ki-won’s big data real estate, which is famous for being a downwardist. And I paid attention to the real estate of Daejeon, Seoul, and Jeonnam, which appear as promising investment areas in this book.

Big data real estate investment.
Kim Kiwon.
Dasan Books.
200 percent of the predictions of this book were correct. Housing prices in Daejeon showed a high increase in 2018 and 2019.

This is the 2018 and 2019 sale price of Daejeon. (Korea Appraisal Board)
And I became a believer in big data investment and after that, I took a lot of big data-based investment lectures. Kim Ki-won took not only a special lecture on real estate but also a lecture by CEO Park Sang-yong. CEO Pl also has a great hit rate.

Manager Park said he invested in real estate with big data. So, how much did he earn?
Park Sangyong.
I also became a believer in big data investment and studied hard. However, as I did it, there were many advantages, but the disadvantages began to be visible.
Advantages of big data investment.
The biggest advantage of real estate investment through big data is that you can buy undervalued areas. In other words, I think safe investment is possible. If you invest in real estate using big data, it will help you choose areas with low supply, low housing prices to income, high jeonse rates, and little unsold. It is difficult to fall significantly even if it falls, and it is highly likely to rise significantly if the upward wind blows due to increased liquidity like recently. In particular, the rate of return itself is very high because it targets low jeonse rates. Compared to stocks, it’s very similar to the value investment method.

Disadvantages of investing in big data.
As it is very similar to the stock value investment method, the disadvantages are similar. Local products are undervalued more than areas that grow rapidly or want to buy. This is because local real estate has low speculative demand, so there are many areas with less bubbles or rather undervalued. The problem is that it’s not an area where you can easily reach out. Kim Ki-won’s big data real estate investment network, published in 2021, selected Wonju, Seosan, and Gunsan as promising areas. It was a huge hit, but it’s not an area that you can easily reach unless you’re a professional investor.

Big data tells you that this year’s butler is a good area.
Big data tells you about this year’s butler area [Jip Economy TV], House Interview/ Kim Ki-won, CEO of Data Nose, Part 2.
It may also be accepted as a sign for those who think of living in real life, not for the purpose of real estate investment, to continue to delay purchasing homes. This is because it is an overheating that cannot be explained in big data. Representatively, Kim Ki-won, CEO of Richigo, who insisted on overheating real estate in Gyeonggi-do, Seoul, has said that since 2020. In 2021, housing prices in Gyeonggi-do, Seoul, soared unprecedentedly. Just as Tesla soared despite experts’ ridicule of overheating, real estate is also possible.

The use of real estate big data is…
Based on the undervalued area,
Personally, I think anyone who thinks of investing in real estate should use it. In fact, everyone is smart these days, so I don’t think there’s anyone who doesn’t invest. Strategies to refrain from investing in highly valued areas and invest in undervalued areas within financial capacity can increase stability.

This is Richigo’s future prospect. Don’t trust me and just refer to it.
However, investments should not overconfidence in big data to predict the future. I think it’s dangerous to sell at a high point and buy at a low point. Big data is a data that helps judge undervalued areas, not a witch’s crystal mirror that fits the future. In fact, even undervalued areas cannot know when they will rise due to big data. Even in highly valued areas, we don’t know when it will fall. In 2020, it was said that housing prices are higher than interest rates and housing prices are higher than jeonse prices due to big data. However, interest rates were lowered in 2020 and jeonse prices were raised to the three lease laws. In the end, housing prices felt cheaper compared to interest rates, and jeonse rates rose, creating an environment favorable for gap investment and rising significantly. In other words, the future outlook is that it is barely possible to meet the current situation only when it does not change at all.
Big data is a vast collection and organization of past data. You can’t guess the front by looking at the rearview mirror. Even if there are similar cases in the past, it is not necessarily repeated. Big data should only be used to determine undervalued areas. Please remember that attempts to catch both low and high points, whether in stocks or real estate, always fail.