The best time to plant a tree was ten years ago, and the next best time is now. If you want to learn, then you must come in time.
Python+ has become a weapon
Shine big in the field of big data
Python has become a sharp tool for people in the workplace to pursue efficiency, because no matter what kind of work, data will be part of the work. Where there is data, there is Python!
We know that with the development of the Internet, online data is increasing in magnitude, and there is a lot of valuable information hidden in huge data, and Python can be used to discover and refine these valuable information, in data mining, data Play a key role in analysis, data visualization, and more!
Now, driven by digital transformation, more and more companies realize the charm of big data and continue to invest in this field. Talents related to Python+ big data development are also favored!
According to the "New Occupation—Analysis Report on the Employment Prosperity of Big Data Engineering and Technical Personnel", the demand for big data talents will reach 2.5 million in 2025!
Under this gap, the salaries of big data talents have been soaring. Not only can high-paying jobs be found in first-tier cities, but employment in new first-tier cities and provincial capital cities is also very good!
There are more and more demands for big data, and as long as technology is in hand, there is no need to worry about finding a job.
What language foundation do you need to master to learn big data?
1. Java foundation
More than 90% of big data frameworks use the Java development language, so if you want to learn big data technology, you must first master the basic Java grammar and the relevant knowledge of JavaEE direction.
2. MySQL database
This is one of the knowledge that must be mastered in learning big data. The language of data manipulation is SQL, so the development goal of many tools is to be able to use SQL on Hadoop.
3. Linux system
The framework of big data is installed on the Linux operating system, so proficiency in Linux-related knowledge is also the basic knowledge for learning big data.
The learning of big data can’t just stay at the theoretical level. The direction of big data is all-round. The learning of basic language is only a small aspect. Programming is implemented in the end to programming ideas. With the guiding ideology, it is easy to learn. Can be much more convenient.
As the tide of the Internet goes to the bottom, and traditional enterprises are undergoing digital transformation one after another, basically every company is considering how to further tap the value of data and improve the operational efficiency of enterprises. In this trend, big data technology is becoming more and more important. Therefore, in the future, big data is one of the necessary skills for our workers.
1. What is big data?
Regarding the interpretation of big data, the more official definition refers to a collection of data that cannot be captured, managed, and processed by conventional software tools within a certain period of time. It requires a new processing model to have stronger decision-making, insight, and processes. Massive, high-growth, and diverse information assets to optimize capabilities. Simply put, big data is structured traditional data plus unstructured new data. So what are traditional data and new data? Traditional data is the data in the IT business system, such as customer information, financial data, etc. These data are structured, and the amount is not particularly large, generally only terabytes. Compared with traditional data, there is also a kind of "new data", which comes from social networks, the Internet and other channels, including text, pictures, audio, video and other unstructured data. At present, more than 75% of the world is unstructured data, and it has been showing explosive growth.
2. What are the characteristics of big data?
(1) Large capacity
The volume of data is large, and the size of the data determines the value and potential information of the considered data.
(2) Various types
The diversity of data types, including traditional databases, images, files, and other complex records, is worthless if there is only a single piece of data, such as a single personal data, or a single user-submitted data, which is still It cannot be called big data, so big data still needs to be diverse. For example, among current Internet users, everyone has different characteristics such as age, education, hobbies, personality, etc. This is the diversity of big data.
(3) fast
The speed of data means that the logical processing speed of data through algorithms is very fast, and the 1-second rule can quickly obtain high-value information from various types of data, which is also fundamentally different from traditional data mining technology.
(4) Great value
If you have more than 1PB of online data of all 20-35 young people in the country, then it will naturally have commercial value. For example, by analyzing these data, we can know the hobbies of these people, and then guide the development direction of products, etc. . If we have the data of millions of patients across the country, we can predict the occurrence of diseases based on the analysis of these data, which is the value of big data.
4. Application Scenarios of Big Data
(1) Finance: Big data plays a major role in the three major financial innovation areas of high-frequency trading, social sentiment analysis, and credit risk analysis.
(2) Urban management: Big data can be used to realize intelligent transportation, environmental protection monitoring, urban planning and intelligent security.
(3) Medical care: When a disease is discovered and diagnosed, the diagnosis of the disease and the determination of the treatment plan are the most difficult. With the help of the big data platform, we can collect different cases and treatment plans, as well as the basic characteristics of patients, and build a database for disease characteristics.
(4) Retail: The retail industry can use big data technology to understand customer consumption preferences and trends, carry out precise marketing of products, and reduce marketing costs. In addition, it can also provide customers with other products that may be purchased according to the products purchased by customers to expand sales.
(5) Meteorology: With the help of big data technology, the accuracy and effectiveness of weather forecasts will be greatly improved, and the timeliness of forecasts will be greatly improved. At the same time, for major natural disasters, such as tornadoes, through the big data computing platform, people will It will be more accurate to understand its trajectory and hazard level, which will help people improve their ability to deal with natural disasters.
The Python language is simple and easy to understand, suitable for zero-based entry, and has the fastest rise in the ranking of programming languages. It can complete various big data integration tasks including data mining, machine learning, and real-time computing.
Detailed introduction to Python+big data learning roadmap (all free video tutorials)
Getting Started with Big Data Development in Phase 1
Pre-study guide: Start with traditional relational databases, master data migration tools, BI data visualization tools, and SQL, and lay a solid foundation for subsequent learning.
1. Big data data development foundation MySQL8.0 from entry to proficiency
MySQL is the entire IT basic course, and SQL runs through the entire IT life. As the saying goes, if SQL is well written, you can find a job easily. This course fully explains MySQL8.0 from zero to advanced level. After studying this course, you can have the SQL level required for basic development.
The core foundation of big data in the second stage
Pre-study guide: learn Linux, Hadoop, Hive, and master the basic technology of big data.
2022 Big Data Hadoop Introductory Tutorial
Hadoop offline is the core and cornerstone of the big data ecosystem, an introduction to the entire big data development, and a course that lays a solid foundation for the later Spark and Flink. After mastering the three parts of the course: Linux, Hadoop, and Hive, you can independently realize the development of visual reports for offline data analysis based on the data warehouse.
The third stage of hundreds of billions of data warehouse technology
Pre-study guide: The course at this stage is driven by real projects, learning offline data warehouse technology.
Data offline data warehouse, enterprise-level online education project practice (complete process of Hive data warehouse project)
This course will establish a group data warehouse, unify the group data center, and centralize the storage and processing of scattered business data; the purpose is from demand research, design, Version control, R&D, testing, and launch, covering the complete process of the project; digging and analyzing massive user behavior data, customizing multi-dimensional data sets, and forming a data mart for use in various scene themes.
The fourth stage PB memory computing
Pre-study guide: Spark has officially adopted Python as the first language on its homepage. In the update of version 3.2, it highlights the built-in bundled Pandas; Spark content.
1. From entry to mastery of python (19 days)
Python basic learning courses, from building the environment. Judgment statements, and then to basic data types, and then learn and master functions, familiarize yourself with file operations, initially build object-oriented programming ideas, and finally lead students into the palace of python programming with a case.
2. Python programming advanced from zero to website building
After completing this course, you will master advanced Python syntax, multi-tasking programming, and network programming.
3.spark3.2 from basic to proficient
Spark is the star product of the big data system. It is a high-performance distributed memory iterative computing framework that can handle massive amounts of data. This course is developed based on Python language learning Spark3.2. The explanation of the course focuses on integrating theory with practice, which is efficient, fast, and easy to understand, so that beginners can quickly master it. Let experienced engineers also gain something.
4. Big data Hive+Spark offline data warehouse industrial project actual combat
Through the big data technology architecture, it solves the data storage and analysis, visualization, and personalized recommendation problems in the industrial Internet of Things manufacturing industry. The one-stop manufacturing project is mainly based on the Hive data warehouse layer to store the data of various business indicators, and based on sparkSQL for data analysis. The core business involves operators, call centers, work orders, gas stations, and warehousing materials.