As more forms of data are discovered, the need to process, collect, store and analyze data continues to evolve. The term “Business Intelligence” is becoming more and more popular, and the demand for emerging software and systems for analyzing business and operational performance is rapidly increasing. Therefore, there are many positions related to data analysis. Let’s take a look today.
1. Data Scientist
Data scientists need to be able to apply mathematics, statistics, and scientific methods. Use a variety of tools and techniques to clean and prepare data; perform predictive analytics and artificial intelligence; and explain how to leverage these results to provide data-driven solutions to business problems. Data scientists require many more skills than data analysts.
2. Data Analyst
Data analysts collect, process, and perform statistical data analysis to draw meaningful conclusions for the organization. Data analysts transform and process large data sets into a usable form, such as a report or presentation. They also aid in the decision-making process by studying important patterns and gathering insights from data that are then effectively communicated to organizational leadership to aid business decisions.
3. Data Engineer
Data engineers are responsible for preparing, processing, and managing collected and stored data for analytical or operational purposes. Like traditional engineers, data engineers build and maintain data "pipes" that connect data from one system to another so that data scientists can obtain information. Because of this, data engineers are required to know several programming languages used in data science, such as Python, R, and SQL.
4. Data architect
Data architects primarily design and create blueprints for data management systems, which are then built by data engineers. Similar to traditional architects, data architects are "visionaries" in that they are responsible for visualizing and designing an organization's data management framework. In addition, data architects improve the performance of existing systems and ensure they are usable by database administrators and analysts.
5. Business Intelligence (BI) Developer
Business intelligence developers are specialized engineers who use software tools to transform data into useful insights to aid business decisions. Responsible for simplifying technical information so that others in the company can easily understand it. Simply put, they create and run reports containing the data they find using business intelligence tools and translate the information into more colloquial terms.
6. Statistician
Given that statistics is one of the main foundations of data science, many statisticians can easily transition into the field of data science. Statisticians are primarily responsible for the collection and processing of data. They decide what data is needed and how to collect it. In addition, they design experiments, analyze and interpret data, and report conclusions.
7. Machine learning engineer
Machine learning engineers are another group of professional engineers who focus on researching, building, and designing artificial intelligence and machine learning systems to automate predictive models. Basically the algorithm developed uses input data and predicts the output using statistical models while continuously updating the output as new data becomes available.
Data science is extremely popular today, and the share of statisticians and data scientists in the total workforce is small compared to other occupations, but these numbers are expected to increase in the coming years as the data science career path becomes more popular. .
Liepin Big Data Research Institute released the "2022 Future Talent Employment Trend Report"
Judging from the rankings, looking at the average annual salary of mid-to-high-end talents in various industries from January to April 2022, the average annual salary of mid-to-high-end talents in the artificial intelligence industry is the highest, at 310,400 yuan; the average annual salary of mid-to-high-end talents in the financial industry is 276,900 yuan. Second; the average annual salary of mid-to-high-end talents in the communications and big data industries is 275,100 yuan and 252,300 yuan respectively, ranking third and fourth; the average annual salary of mid-to-high-end talents in the IT/Internet industry is 230,200 yuan, ranking seventh.
Chart source: "2022 Future Talent Employment Trend Report"
What if you feel high and average like this? Then open Boss Direct Recruitment and search for big data engineers:
Let’s do some data analysis:
The salary column has a minimum salary and a maximum salary. We compared and analyzed different cities and found that Beijing has the highest salary level, with the lowest being 22k and the highest being 38k.
Working experience is also a big factor that restricts salary levels. As can be seen from the figure, even a fresh graduate can reach a salary range of 11-20k.
In terms of academic requirements, most are undergraduates, followed by junior college and master's degrees, and there are so few others that they are not shown in the figure. 
Most of the company's requirements for different positions are 3-5 years. Of course, companies need employees with certain work experience, but in actual recruitment, if you have project experience and good theoretical knowledge, companies will also relax the conditions.
Analyzing different industries, we found that the demand for big data jobs is distributed in all walks of life, mainly in computer software and the Internet. It may also be determined by this recruitment software. After all, direct recruitment by Boss is mainly in the Internet industry.
Let’s take a look at which companies are recruiting for big data-related positions. Judging from the number of more than 15, Huawei, Tencent, Alibaba, Byte, these major companies still have a huge demand for this position.
So what skills are required for these positions? Spark, Hadoop, data warehouse, Python, SQL, Mapreduce, Hbase and more
According to the domestic development situation, the future development prospects of big data will be very good. Since companies have begun digital transformation in 2018, first- and second-tier cities have a strong demand for talents in the field of big data. In the next few years, the demand for talents in third- and fourth-tier cities will also increase significantly.
In the field of big data, domestic development is relatively late. Since 2016, only more than 200 universities have opened majors related to big data. This means that the first batch of graduates in 2020 have just entered society, and our country’s market environment is at a There is an urgent need for big data talents but there is a shortage of talents, so there will be many employment opportunities in the big data field in the future.
The salary is high and the gap is large, so it has naturally become the “salary” choice for professionals!
Any learning process requires a scientific and reasonable learning route to achieve our learning goals in an orderly manner. The content required to learn Python+big data is complex and difficult. We have compiled a comprehensive Python+big data learning roadmap to help you clarify your ideas and overcome difficulties!
Detailed introduction to Python+big data learning roadmap
Introduction to the first stage of big data development
Pre-study introduction: Start with traditional relational databases, master data migration tools, BI data visualization tools, and SQL to lay a solid foundation for subsequent learning.
1. Big data data development basics 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 you write SQL well, you can find a job easily. This course comprehensively explains MySQL8.0 from zero to advanced level. After studying this course, you can have the SQL level required for basic development.
The second stage of big data core foundation
Pre-study introduction: Learn Linux, Hadoop, Hive, and master the basic technologies of big data.
The 2022 version of Big Data Hadoop Introductory Tutorial
Hadoop Offline is the core and cornerstone of the big data ecosystem. It is an introduction to the entire big data development and a course that lays a solid foundation for later Spark and Flink. After mastering the three parts of the course: Linux, Hadoop, and Hive, you can independently implement visual report development for offline data analysis based on the data warehouse.
The third stage of hundreds of billions of data warehouse technology
Pre-study introduction: This stage of the course is driven by real projects and learns 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 centrally store and process scattered business data; the purpose is from demand research, design, Version control, research and development, testing to implementation, covering the complete process of the project; mining and analyzing massive user behavior data, customizing multi-dimensional data collections, and forming a data mart for use in various scene themes.
Phase 4 PB Memory Computing
Pre-study introduction: Spark has officially adopted Python as the first language on its homepage. In the update to version 3.2, it is highlighted that Pandas is built-in and bundled; the course fully complies with the trend of the technical community and recruitment needs, and is the first company in the entire network to add Python on Spark content.
1. Python from beginner to proficient (19 days complete)
Python basic learning course, starting from setting up the environment. Judgment statements, then basic data types, then learn and master functions, become familiar with file operations, initially build object-oriented programming ideas, and finally lead students into the Python programming palace with a case.
2.Advanced python programming from scratch to building a website
After studying this course, you will master Python's advanced syntax, multi-task programming and network programming.
3.spark3.2 from basics to mastery
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 connecting theory with practice, is efficient and fast, and explains the profound things in simple terms, so that even beginners can master it quickly. Let experienced engineers also gain something.
4. Big data Hive+Spark offline data warehouse industrial project practice
Through the big data technology architecture, we solve 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 Hive data warehouse layering to store various business indicator data, and uses sparkSQL for data analysis. The core business involves operators, call centers, work orders, gas stations, and warehousing materials.