Shulou(Shulou.com)06/03 Report--

How can the American NASA predict all kinds of astronomical wonders in advance? How to choose the location of wind turbines and entrepreneurs to open stores? How can we accurately predict and warn meteorological disasters? Including in the process of urbanization in the future, how to build a smart city? And so on, behind this series of problems, in fact, the figure of big data is hidden-- not only shows the great value of big data, but also more intuitively reflects the broad application of big data in various industries. These industry applications are also more straightforward to tell people what is big data.

In fact, big data did not suddenly appear, in the past few decades, mathematical analysis has dabbled in the financial industry, Nobel laureate Harry. Markowitz, William. Sharp, Robert. Engel uses econometric knowledge and financial market data to establish a mathematical model to predict the relationship between product returns and risk fluctuations in financial markets.

The emergence of big data era is simply the result of the combination of massive data and perfect computing power, specifically, the mobile Internet and the Internet of things have produced huge amounts of data. Big data computing technology perfectly solves the problems of massive data collection, storage, calculation and analysis.

When we first talked about big data, we probably talked most about user behavior analysis, that is, the data generated by various user behaviors, including browsing records, consumption records, communication and shopping entertainment, action trajectories and other user behaviors. Because the data itself accords with the characteristics of magnanimity and heterogeneity, it is easy to match some results by analyzing the correlation between these data. That is, there are a bunch of behavior factors x and a bunch of results to form y, we find some kind of correlation, which is helpful for us to adjust the subsequent strategies.

Why can Google be big data? Have you thought about it? Because search itself is often an important entrance to user behavior, that is, search engines have the ability to collect x factors of multiple user behaviors in real time. And this ability is often a single e-commerce portal can not do. But where is the weakness of the search engine to be big data? That is, the relationship between users and users mentioned earlier is more difficult to establish, but more related to their own behavior. From this difference, we can also see that search engines are easier to do big data analysis and prediction of traffic, disease, weather and other aspects, while similar e-commerce platforms or similar Tencent are easier to do big data analysis and forecast of consumption and entertainment.

For big data's application scenario, including the application of big data processing and analysis in various industries, the core is the user demand. Next, this paper combs the challenges faced by various industries in the field of big data application, and how to find a breakthrough to show its potential big data application scene. Welcome to join big data exchange group: 658558542 blow water exchange and study together.

First, medical treatment of big data is more efficient

In addition to the Internet companies that began to take advantage of big data earlier, the medical industry is one of the traditional industries that let big data's analysis be carried forward first. The medical industry has a large number of cases, pathological reports, cure plans, drug reports and so on. If these data can be collated and applied, it will be of great help to doctors and patients. We are faced with a large number and variety of bacteria, viruses, and tumor cells, which are in the process of continuous evolution. When finding and diagnosing a disease, the diagnosis of the disease and the determination of treatment are the most difficult.

In the future, with the help of big data platform, we can collect different cases and treatment plans, as well as the basic characteristics of patients, and establish a database according to the characteristics of the disease. If the gene technology is mature in the future, it can be classified according to the characteristics of patients' gene sequence, and the patient classification database of medical industry can be established. When diagnosing patients, doctors can refer to the patient's disease characteristics, laboratory reports and test reports, and refer to the disease database to quickly help patients make a diagnosis and clearly locate the disease. When formulating treatment plans, doctors can transfer effective treatment plans with similar genes, ages, races and physical conditions according to the genetic characteristics of patients, and work out treatment plans suitable for patients to help more people to be treated in a timely manner. At the same time, these data are also helpful for the pharmaceutical industry to develop more effective drugs and medical devices.

The application of data in the medical industry has been going on, but the data has not been connected, it is all isolated data, and there is no way to carry out large-scale application. In the future, these data need to be collected and incorporated into a unified big data platform for the benefit of human health. The government and the medical industry are important drivers of this trend.

Biological big data improved gene

Since the completion of the Human Genome Project, represented by the United States, the major developed countries in the world have launched basic life science research projects, such as the International Thousand Genome Project, the DNA Encyclopedia Project, the UK Human Genome Project and so on. These plans lead to the explosive growth of biological data. at present, the total amount of biological data produced in the world every year has reached EB level, a data revolution is breaking out in the field of life science, and life science has become big data science to some extent.

Let's take a look at today's mothers-to-be. In addition to preparing diapers, bottles and baby packs, they also include genetic tests on their schedule. Genetic testing allows future parents to learn more about the health of their unborn baby. Screening for gene carriers and preimplantation diagnosis have made a big difference in the process of giving birth to a family.

At present, the biological big data technology we are talking about mainly refers to the application of big data technology in gene analysis. Through the big data platform, human beings can record and store the results of gene analysis of themselves and organisms. Use to establish a gene database based on big data technology. Big data technology will accelerate the research of gene technology and quickly help scientists to build models and simulate gene combinations. Genetic technology is an important weapon for human beings to overcome diseases in the future. with the application of big data technology, people will speed up the research process of their own genes and other biological genes. In the future, the use of biological genetic technology to improve crops, the use of genetic technology to cultivate human organs, and the use of genetic technology to eliminate pests will soon be realized.

Compared with the global upsurge of biological big data innovation and development, China's R & D and application have just begun. There are four major deficiencies in China: first, although the existing analysis ability of big data in China is not different from that in Europe and the United States, it needs to be improved in the data analysis framework, software system and advanced IT technology. Second, there are many foreign leading talents in the field of biological big data, although we also have papers and achievements published in international top journals, generally speaking, there are still few high-level domestic teams. Third, Europe and the United States pay attention to the application of results, and an endless stream of analysis software can be used in laboratory, clinical and industry. Fourth, China urgently needs to comprehensively follow up on the theoretical research, standard formulation and wide application of biological big data. Welcome to join big data exchange group: 658558542 blow water exchange and study together.

Financial big data's sharp tool for financial management

Big data of the financial industry often faces the same problem, but the situation may be better. Some credit records of similar enterprises and individuals now have a national unified database that can get some of the data. However, for individual banks, it is also impossible to get records of users' behavior in other banks. Second, when banks themselves do a lot of credit risk analysis, they do need a lot of data to do correlation analysis. But a lot of data come from various government departments, including industrial and commercial taxation, quality supervision, procuratorates and courts, etc., these data are still not available in the short term. In addition, it is more difficult to obtain all kinds of behavior data generated by enterprises or individuals on a daily basis, so the risk assessment of customers still has to use the old methods.

Big data is widely used in the financial industry. Typical cases include Citibank using IBM Watson computer to recommend products for wealth management customers; Bank of America uses customer click data sets to provide customers with special services, such as competitive credit lines. China Merchants Bank uses customer credit card, deposit and withdrawal, electronic bank transfer, Wechat comments and other behavior data for analysis, and sends targeted advertising information to customers every week, which contains products and discount information that customers may be interested in.

It can be seen that the application of big data in the financial industry can be summarized into the following five aspects:

Precision marketing: recommend according to customers' consumption habits, geographical location and consumption time

Risk control: provide credit rating or financing support based on customer consumption and cash flow, and use customer social behavior records to implement credit card anti-fraud.

Decision support: using decision tree technology to enter mortgage management, using data analysis report to implement industrial credit risk control

Efficiency improvement: use the global data of the financial industry to understand the weak points of business operations, and use big data technology to speed up internal data processing.

Product design: use big data computing technology to recommend products for wealth customers, and use customer behavior data to design financial products that meet customer needs.

Retail big data knows best about consumers

The retail industry big data application has two levels, one level is that the retail industry can understand customer consumption preferences and trends, carry out accurate marketing of goods, and reduce marketing costs. Another level is based on customer purchase products, to provide customers with other products that may be purchased, to expand sales, which also belongs to the category of precision marketing. In addition, the retail industry can grasp the future consumption trend through big data, which is conducive to the purchase management of hot-selling goods and the handling of out-of-season goods. The data of the retail industry is very valuable for product manufacturers. The data and information of retailers will contribute to the effective use of resources, reduce overcapacity, and manufacturers will produce according to the actual demand according to the information of retailers. Reduce unnecessary production waste.

The future test of retail enterprises is no longer just the quality of zero supply relationship, but also depends on the ability to tap consumer demand and efficiently integrate the supply chain to meet their demand. therefore, the level of information technology has become the key factor to gain competitive advantage. Whether they are international retail giants or local retail brands, if they want to withstand the pressure of dwindling profit margins and remain invincible in this Red Sea, they must think about how to embrace new technology and bring customers a better consumer experience.

Imagine a scene where when a customer is waiting on the subway, there is a huge digital screen advertisement from a retailer on the wall, which can freely browse product information and place an order with a mobile phone scan for goods that are interested or need to be purchased. It is scheduled to be sent home later. After the customer browses the goods and finally buys the goods, the merchant already knows the customer's preferences and personal details, distributes the goods according to the requirements and delivers them to the customer's home. In the future, even customers do not need to have any purchase action. Using big data, which was generated by the previous purchase behavior, when your bath lotion is left with the last drop, your favorite shower gel will be sent to you. Although the customer and the merchant have never met, but they already know each other as friends. Welcome to join big data exchange group: 658558542 blow water exchange and study together.

E-commerce big data accurate marketing magic weapon

E-commerce is the first industry to use big data to carry out accurate marketing. in addition to precision marketing, e-commerce can prepare goods for customers in advance according to their consumption habits, and use convenience stores as a transit point for goods. Deliver the goods to the door within 15 minutes of the customer's order to improve the customer experience. Ma Yun's Cainiao network claimed to complete the delivery in China in 24 hours, and Liu Qiangdong in Beijing publicized that JD.com would complete the door-to-door delivery in 15 minutes, all based on big data's analysis and prediction of customers' consumption habits.

E-commerce can use its transaction data and cash flow data to provide micro-loans based on cash flow for merchants in its ecosphere. E-commerce can also provide this data to banks and cooperate with banks to provide credit support for small and medium-sized enterprises. Because the data of e-commerce is more concentrated, the amount of data is large enough, and there are many kinds of data, there will be more room for imagination in the application of e-commerce data in the future, including predicting popular trends. consumption trend, regional consumption characteristics, customer consumption habits, correlation of various consumption behaviors, consumption hot spots, important factors affecting consumption and so on. Relying on big data's analysis, the consumption report of e-commerce will be conducive to the product design of brand companies, inventory management and planned production of production enterprises, resource allocation of logistics enterprises, production capacity arrangement of means of production providers, and so on. It is conducive to fine and socialized large-scale production and the emergence of a fine society. Welcome to join big data exchange group: 658558542 blow water exchange and study together.

VI. Quantitative production of Agriculture and Animal Husbandry big data

The application of big data in agriculture mainly refers to the production of agricultural and animal husbandry products based on the forecast of future commercial demand, so as to reduce the probability of cheap vegetables hurting farmers. At the same time, big data's analysis will more accurately predict the future weather and climate, and help farmers and herdsmen do a good job in the prevention of natural disasters. Big data will also help farmers decide which varieties to plant according to consumers' consumption habits, reduce the production of which varieties of crops, increase the output value per unit acreage, and help to quickly sell agricultural products and complete the return of funds. Herdsmen can arrange the range of grazing and make effective use of pastures through big data's analysis. Fishermen can use big data to arrange the fishing moratorium, locate the fishing area, and so on.

As agricultural products are not easy to preserve, it is very important to grow and breed agricultural products reasonably. If it is not well planned, it will easily lead to the tragedy that cheap vegetables will hurt the farmers. The surplus of pork, cabbage and banana in the past is due to the lack of planning for agriculture and animal husbandry. With the help of the consumption trend report and consumption habit report provided by big data, the government will provide reasonable guidance for agricultural and animal husbandry production and suggest that production should be carried out according to demand to avoid overcapacity, resulting in unnecessary waste of resources and social wealth. Agriculture is related to the national economy and people's livelihood, and scientific planning will help to improve the efficiency of society as a whole. Big data technology can help the government to achieve fine management of agriculture and achieve scientific decision-making. Driven by data, combined with UAV technology, farmers can collect information on the growth of agricultural products, diseases and insect pests. Compared with the past, the cost of hiring aircraft will be greatly reduced, and the accuracy will also be greatly improved. Welcome to join big data exchange group: 658558542 blow water exchange and study together.

Traffic big data unimpeded travel

As an important component and one of the important conditions of human behavior, traffic is also the most urgent perception of big data. In recent years, intelligent transportation in China has achieved rapid development, and many technical means have reached the international leading level. However, the problems and difficulties are also very prominent. from the development of each city, the potential value of intelligent transportation has not been effectively excavated: the perception and collection of traffic information is limited. the massive data existing in various management systems can not be shared and analyzed effectively, the research and prediction of traffic situation is weak, and the traffic information service of the public is difficult to meet the demand. Although there are differences in construction concept and investment in different places, the overall situation of intelligent transportation is that the efficiency is not high and the degree of intelligence is not enough, so that many advanced technology and equipment can not play its due role. It has also caused a waste of funds on a lot of investment. One of the most important problems is the hard wound brought by the small data era: the management ideas and technical equipment brought by the simulation era can only be analyzed in a certain range, while the relational databases of the management system can only rigidly analyze specific relationships. There is nothing we can do for massive data, especially semi-structured and unstructured data.

Although digitization has been basically achieved, digitization and digitization are not the same thing at all, but partially improve the efficiency of collection, storage and application, and in essence do not change much. The arrival of the era of big data will inevitably bring a great opportunity to solve the problem. Big data inevitably requires us to change the accurate calculation blindly under the condition of small data, but to better face the confusion and grasp the macro situation; big data inevitably requires us to no longer be keen on causality but correlation, which makes it possible to deal with massive unstructured data, and inevitably urges us to make efforts to digitize everything, and finally achieve convenient and efficient management.

At present, the application of big data in traffic is mainly in two aspects: on the one hand, we can use big data sensor data to understand vehicle traffic density and reasonably carry out road planning, including one-way route planning. On the other hand, real-time signal scheduling can be realized by using live data to improve the operation capacity of existing lines. Scientific arrangement of signal lights is a complex system engineering, and a more reasonable scheme can only be calculated by using big data computing platform. Scientific traffic light arrangement will increase the capacity of existing roads by about 30%. In the United States, the government adds traffic lights based on traffic accident information on a certain section of the road, reducing the traffic accident rate by more than 50%. Airport flight take-off and landing depends on big data will improve the efficiency of flight management, airlines can use big data to increase the occupancy rate and reduce operating costs. Railway use of big data can effectively arrange passenger and freight trains, improve efficiency and reduce costs.

VIII. Educate big data to teach students in accordance with their aptitude

With the development of technology, information technology has been more and more widely used in the field of education. Examination, classroom, teacher-student interaction, use of campus equipment, home-school relationship. As long as the technology reaches, every link is packed with data.

In the classroom, data can not only help improve education and teaching, but big data also has the opportunity to show his talents in major educational decision-making and educational reforms. The United States uses data to diagnose students who are in danger of dropping out of school, to explore the relationship between education expenditure and students' academic performance, and to explore the relationship between students' absenteeism and their grades. To give an interesting example, is the teacher's score in the college entrance examination related to the performance of the students taught? We might as well look at it with the help of data. For example, the data analysis of a public primary and secondary school in a state of the United States shows that in terms of Chinese performance, there is a significant positive correlation between teachers' college entrance examination scores and students' scores. In other words, teachers' scores in the college entrance examination are obviously related to the students' grades in the Chinese classes they teach now. the better the teachers' scores in the college entrance examination, the better the students' Chinese scores. This relationship allows us to further explore the real reasons behind it. In fact, teachers' college entrance examination scores to some extent is a certain characteristic of teachers, and it is this characteristic that plays a vital role in teaching students well. Teachers' college entrance examination scores can be used as an index for selecting teachers. If we have sufficient data, we can explore the relationship between more teacher characteristics and students' performance, so as to provide a better reference for the selection of teachers.

Big data can also help parents and teachers identify children's learning gaps and effective learning methods. McGraw-Hill Education and Publishing Group in the United States, for example, has developed a predictive assessment tool to help students assess the gap between their existing knowledge and the extent to which they need to pass the test, and then point out areas for students to improve. Assessment tools allow teachers to track students' learning so as to find out students' learning characteristics and methods. Some students are suitable for step-by-step learning, while others are more suitable for non-linear learning of schema information and integrated information. All these can be quickly identified by big data's collection and analysis, so as to provide a solid basis for education and teaching.

In China, especially in Beijing, Shanghai, Guangdong and other cities, big data has a lot of applications in the field of education, such as moo classes, online courses, flip classes and so on, in which a large number of big data tools are used.

There is no doubt that in the near future, personalized analysis reports for different applications can be obtained for education administration departments, principals, teachers, students and parents. Through big data's analysis to optimize the educational mechanism, we can also make more scientific decisions, which will bring potential educational revolution. In the near future, personalized learning terminals will be more integrated into the learning resources cloud platform, pushing cutting-edge technologies, information, resources and even future career development directions in related fields according to each student's different interests and specialties. and throughout the whole process of life-long learning.

Ninth, sports big data wins the championship spirit

From the beginning of the film "penalty kicks into Gold", people of insight in the sports world have finally found a long-awaited way, that is, how to use big data to make the team perform at its best. From football to basketball, data seems to be the golden key to winning games and even trophies.

Big data's changes to sports can be said to be in all aspects. from the athletes themselves, the data collected by wearable devices can give them a better understanding of their physical conditions. Media commentators can better explain and analyze the competition through the data provided by big data. The data have been transformed into insight through big data's analysis, which not only increases the bargaining chips for the victory in sports competitions, but also provides a personalized experience for sports enthusiasts all over the world to watch the games anytime and anywhere.

Although few professional tennis players are willing to publicly admit that they use big data to develop match planning and tactics, almost every player will use big data's service before and after the match. A coach said: "on the court, the winning or losing of the game depends on the strategy and tactics of the game, as well as the quick response and decision-making during the match, but these details are fleeting, so data analysis has become the most critical part of a game. For those players who support and use big data to make decisions, they will undoubtedly win enough competitive advantage." Welcome to join big data exchange group: 658558542 blow water exchange and study together.

Environmental Protection big data against PM2.5

Beijing suffered a torrential rain on July 21 the year before last, with an average rainfall of 164mm in one day, the heaviest rainstorm in Beijing in 61 years. The torrential rain has had a great impact on the lives of the general public because of its ferocious force. In fact, the most important thing for such a thing is for the meteorological department to make a timely and accurate early warning and to send such early warning information to Beijing citizens (including those traveling in Beijing) as soon as possible in conjunction with other operator departments. It is precisely like this that the rainstorm the year before last not only exposed loopholes in management work, but also aroused discussion in the industry about a "big data".

The influence of meteorology on society involves all aspects. Traditionally, meteorology is mainly dependent on agriculture, forestry, water transport and other industries, but now, meteorology has become a resource for social development in the 21 century, and supports customized services to meet the needs of users in various industries. With the help of big data technology, the accuracy and effectiveness of the weather forecast will be greatly improved, and the timeliness of the forecast will be greatly improved. at the same time, for major natural disasters, such as tornadoes, through the big data calculation platform, people will understand their trajectory and hazard level more accurately, which will help people improve their ability to deal with natural disasters. The improvement of the accuracy of the weather forecast and the extension of the prediction cycle will be conducive to the arrangement of agricultural production.

Especially since the beginning of autumn and winter, haze weather has broken out in many cities in China, and the air pollution is serious. As the harm of PM2.5 to human health is increasingly known by the public, people's voice for "haze fake" is getting louder and louder. Some people tease that walking on the way to work on a heavily polluted day is a "human flesh vacuum cleaner".

In view of this, there is a long way to go to rely on big data to analyze the formation and countermeasures of air pollution in Beijing or other cities. One is the source of data. Are the production scale and emissions of high energy-consuming enterprises reported layer by layer and accurate statistics? Can the department with this data be made public? What exactly are the ingredients of gasoline used by 5 million cars in Beijing, and what is the "contribution" rate of exhaust gas to the air pollution index? The second is to break through the technical barrier of data mining analysis application, of course, the premise is data disclosure.

In the United States, NOAA (National Oceanic and Atmospheric Administration) has long been using big data business. More than 3.5 billion observations are collected every day through satellites, ships, aircraft, buoys, sensors, etc. After the collection, NOAA will collect atmospheric data, ocean data, and geological data, directly determine, draw a complex high-fidelity prediction model, and provide it to the NWS (National Weather Administration) to make weather forecast reference data. At present, the amount of data managed by NOAA every year is as high as 30PB (1PB=1024TB). The final analysis results generated by NWS are presented in daily weather forecasts and warning reports.

Safety on the tip of big data's tongue

Food is the most important thing for the people, and food safety has always been the focus of the country, which is related to people's health and national security. In recent years, food safety incidents such as poison capsules, cadmium rice, clenbuterol and foreign milk powder continue to test the affordability of consumers, making consumers worried about food safety.

In recent years, foreign tourists have reduced their travel to China, and food imports have increased significantly. One of the main reasons is the problem of food safety. With the continuous improvement of science and technology and living standards, there are more and more food additives and food varieties, the traditional means are difficult to meet the current complex food regulatory needs, from the emerging food safety problems, food supervision has become a thorny issue of food safety. At the moment, large amounts of data are aggregated together through big data management, and discrete data requirements can be aggregated to form a long tail of data, so as to meet the traditional needs that are difficult to achieve. Driven by data, by collecting the reporting information provided by people on the Internet, the state can grasp the dead corner information of some villages and cities, dig out illegal processing points, improve the transparency of law enforcement and reduce the cost of law enforcement. The state can refer to the medical information provided by the hospital, analyze the information related to food safety, carry out timely supervision and inspection, and deal with it as soon as possible, so as to reduce the harm of existing unsafe food. Refer to the search information of individuals on the Internet, grasp the outbreak trend of epidemic diseases in some regions and seasons, and intervene in time to reduce their epidemic hazards. The government can provide information about unsafe food manufacturers and unsafe food to help people raise their awareness of food safety.

That's for sure, Some professionals believe that food safety involves every link from the field to the dining table, and dynamic monitoring covering the whole process is needed to ensure food safety. Take rice production as an example, producing area, variety, soil, water quality, occurrence of diseases and insect pests, type and quantity of pesticides, chemical fertilizer, harvest, storage, processing, transportation, sales and other links, all affect the safety of rice, by collecting and analyzing the data of each link. It can be predicted whether there are hidden dangers in the safety of the rice to be harvested or the rice produced in a certain area.

Big data can bring not only commercial value, but also social value. With the development of information technology, food supervision is also faced with numerous types of massive data, how to extract effective data has become the key. It can be seen that big data management is a great challenge, on the one hand, to extract data in time to meet the needs of food safety regulation; on the other hand, it is necessary to balance the potential value of data and personal privacy. It is believed that the application of big data management in food supervision can prop up a powerful umbrella for food safety.

Government Regulation and Fiscal Expenditure big data keeps it in order

By using big data technology, the government can understand the economic development of various regions, the development of various industries, consumer expenditure and product sales, and scientifically formulate macro policies to balance the development of various industries according to the results of data analysis. avoid overcapacity, make effective use of natural and social resources, and improve social production efficiency. Big data can also help the government to monitor the management of natural resources, whether it is land and resources, water resources, mineral resources, energy, etc. Big data uses a variety of sensors to improve the accuracy of its management. At the same time, big data technology can also help the government to manage expenditure, transparent and reasonable financial expenditure will help to improve public credibility and supervise fiscal expenditure.

The technology of big data and big data brings not only the improvement of efficiency, scientific decision-making and fine management to the government, but also the change of consciousness of data governance and scientific management. Big data will help the government implement efficient and meticulous management from all aspects in the future. The improvement of government operation efficiency, scientific and objective decision-making, reasonable and transparent fiscal expenditure will greatly enhance the overall strength of the country and become a national competitive advantage. Big data will have a lot of room to imagine the benefits of bringing a country and society.

13. Public opinion Monitoring, famous big data Detective Conan

"Black Cat Sheriff" is familiar to everyone. It tells the story of how "Black Cat Sheriff" is smart and capable, chasing bad guys, and ups and downs. In the context of big data's era, although it can also reflect the due diligence, intelligence and ability of the "black cat sheriff", it more often boils down to a question: why is it still so passive and inefficient? Disease can be prevented, but can't crime be prevented?

The answer is yes. Researchers at the University of Michigan have devised a way to use "supercomputers and large amounts of data" to help police locate areas that are most vulnerable to lawbreakers. Specifically, researchers use a large number of types of data (from demographic data to crime data to the types of alcohol sold in each region, security situation, floating population data, and so on). Create a hot map of Boston's crime-prone areas. At the same time, various factors such as adjacent areas are added to the data model, and the predicted data are constantly revised according to historical criminal records and location statistics.

The state is using big data technology to monitor public opinion, and the data collected can not only understand the aspirations of the people and reduce group incidents, but also can be used for crime management. A large number of social behaviors are gradually moving towards the Internet, and people are more willing to use the Internet platform to express their ideas and vent their emotions. Social media and moments are becoming platforms for tracking people's social behavior, with both positive and negative energy. Some kind-hearted people use Weibo to help others find lost relatives or provide information about people who may be trafficked. These are examples of mutual assistance among social groups. Countries can use pictures and exchange information shared by social media to collect individual emotional information and prevent individual criminal and antisocial behaviors. Recently, the police arrested a crowd of drug addicts through microphone messages and punished parents who abused their children.

The development of big data's technology has brought about the transformation of the enterprise management decision-making mode, driven the industry change, and derived new business opportunities and development opportunities. The ability to control big data has been proved to be the core competitiveness of leading enterprises, which can help enterprises break data boundaries, draw a panoramic view of business operations, and make optimal business decisions and development strategies. In fact, no matter which industry big data analysis and application scenarios, you can see a typical feature or can not leave a variety of human-centered user behavior data, user business activities and transaction records, user social data, the relevance of these core data coupled with intelligent data collection of perceptible devices constitute a complete big data ecological environment.

In order to help you make learning easier and efficient, we will share a large number of materials free of charge to help you overcome difficulties on your way to becoming big data engineers and even architects. Here to recommend a big data learning exchange circle: 658558542 welcome everyone to enter × × × stream discussion, learning exchange, common progress.

When you really start learning, it is inevitable that you do not know where to start, resulting in inefficiency that affects your confidence in continuing learning.

But the most important thing is not to know which skills need to be mastered, step on the pit frequently while learning, and eventually waste a lot of time, so it is necessary to have effective resources.

Finally, I wish all the big data programmers who encounter bottle disease and do not know what to do, and wish you all every success in the future work and interview.

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