What are the main jobs of data analysts? What are the development prospects? What relevant knowledge do you need to master?

The position of data analyst has distinct characteristics of the times and huge demand. It is of great research value and practical significance to actively explore effective countermeasures to cultivate the data analysis ability of undergraduate students majoring in statistics, and then provide qualified data analysts for the society.

First, the significance of training data analysts

(a) Training of data analysts is in line with national strategies

In order to adapt to the process of world economic integration and completely change the shortage of professional and technical personnel for "project data analysis" in China, the first national data analysis office was established in Shaanxi in April 2005. Up to now, there are about 80 professional organizations for project data analysis in Beijing, Shaanxi, Jiangsu, Xinjiang, Gansu, Shandong, Zhejiang, Shanghai and Heilongjiang 14 provinces, municipalities and autonomous regions. With the advent of the era of big data, building a big data research platform, integrating innovative resources and implementing "special plans" have become one of the priorities of provinces and cities.

(B) Data analysts have bright employment prospects.

Nowadays, as the "first year of data", data analysts are internationally renowned for their generous treatment and respected status, and are praised as "the five hottest emerging industries in 2 1 century" by Time magazine. Nowadays, the number of professionals in the domestic data analysis industry is growing at a high speed of more than thousands of digits every year. In the same period, the number of vacancies in various industries has reached nearly 200,000, and the demand for data analysts in China will increase explosively in the future.

In the training of data analysts, foreign countries have taken data analysts as a national strategy. According to statistics, more than 90% of Fortune 500 companies have established data analysis departments. The huge demand for data analysts in the era of big data has also greatly stimulated the enthusiasm of colleges and universities.

Second, the cultivation of professional quality of data analysts.

Through the collection and in-depth analysis of the recruitment information of data analysts and market research analysts on major recruitment websites, the specific requirements of social employers for the knowledge, skills and moral quality of data analysts are as follows:

(A) the professional connotation of data analysts

Data analyst refers to a professional who specializes in data collection, collation and analysis of different industries and makes industry or market research, evaluation and prediction based on the data; Is based on the actual data, project status and long-term statistical data, analysis, prediction and into decision-making information professionals. Data analysts can use a large number of industry data and scientific calculation tools, combined with economic principles and mathematical models, to make scientific and reasonable quantitative analysis. Data analysts can predict the future profits and risks of enterprises and provide scientific and quantitative analysis basis for enterprise management decisions.

At present, there are two main types of data analyst certification: one is Certified Data Analyst (CDA), which is a professional abbreviation initiated by CDA's Certified Data Analyst Association under the trend of big data and cloud computing; 2. The Project Data Analyst (CPDA) is certified by the Data Analysis Professional Committee of China Business Federation and the Education Examination Center of the Ministry of Industry and Information Technology. Certificate is one of the necessary conditions for applying to establish a project data analysis firm.

(B) the knowledge requirements of data analysts

Master statistical modeling methods such as multivariate statistical analysis, applied regression analysis, time series analysis, econometrics and economic forecasting research, and understand the new progress of statistical methods in this industry; Master the methods of data sorting, query and extraction in SQL/oracle database; Skillfully use relevant statistical software to accurately interpret the running results of the software; Understand the business knowledge and data composition of related industries.

(C) the ability requirements of data analysts

Sensitive to information and data, strong writing ability, able to write research reports independently; Proficient in using statistical analysis software such as SPSS/SAS/Eviews, with comprehensive ability of data analysis or data mining; Master database architecture and data architecture, have the skills and knowledge of using query statements in Excel/SQL or Access, and have good data processing and statistical model building capabilities.

(d) Job responsibilities of data analysts

To undertake the investigation, collection, collation, analysis and release of information and data related to industries and enterprises; Participate in special research, research and consulting projects, and write industry analysis articles and research reports; Dig deep into big data, establish relevant models for prediction and analysis, find out relevant relationships, reveal internal laws, and provide basis for industry and enterprise decision-making.

Third, the training plan for data analysts.

The training scheme is the concentrated embodiment of the idea of running a higher education. In order to highlight the training characteristics of data analysis, statistics major should make a curriculum system that meets the training requirements of data analysts on the premise of in-depth analysis of the needs of data analysis major.

(A) training objectives

In order to make students become professionals in the field of data analysis in various industries after graduation, the training objectives of statistics specialty in undergraduate education are determined: first, to have good basic literacy in economics, management and financial management; The second is to understand relevant industry knowledge and company business processes; Third, master the basic theories and methods of statistics, and have the comprehensive ability to skillfully use statistical analysis software such as SPSS/SAS for data analysis or data mining; Fourth, master the database architecture and data architecture, have the skills and knowledge of using query statements in Excel/SQL or Access, and have good data processing and statistical modeling capabilities; Fifth, I have strong writing skills and can independently write data analysis and research reports.

(B) the principle of establishing a curriculum system

In the undergraduate education stage, the curriculum of training data analysts should implement the principle of "three combinations".

1. Multidisciplinary integration. Data analysis is a comprehensive application of multi-discipline and multi-specialty in enterprise decision-making. To be an excellent data analyst, you need to be proficient in many disciplines. Must be familiar with or understand the relevant knowledge of mathematics, statistics, economics, finance, management, marketing and other disciplines.

2. Combining theoretical research with practical application. Colleges and universities generally have relatively mature teaching practice bases and practice bases. After the theoretical study, students can go to enterprises and institutions or finance, finance, insurance and other industries to carry out targeted practice, understand the business knowledge and data composition of related industries, use the knowledge they have learned to conduct data analysis, and independently or cooperatively complete data analysis and research reports.

3. Combination of vocational education and technical qualification education. Through study, students can obtain a bachelor of science degree in statistics or a bachelor of economics degree; By taking the social technical qualification examination, you can obtain professional technical qualification certificates such as data analysis, statistician and survey analyst. The combination of the two is more conducive to students moving from a closed campus to an open society, increasing their skills and better integrating and adapting to society.

(C) the basic framework of the curriculum system

In the whole teaching process, each semester can be divided into two short semesters: long and short. Set up some short-term concentrated practical teaching links related to employment and aimed at skill training in a short term, mainly on-the-job training courses. Long-term courses are divided into four series: basic courses, orientation courses, comprehensive practice courses and career development courses. By integrating relevant knowledge, optimizing the curriculum structure, strengthening practical skills, highlighting job skills training and other means, the curriculum system is constructed, so as to achieve the purpose of cultivating students' basic skills and literacy of data analysts.

Fourthly, the strategy in the process of training data analysts.

(A) teaching content integration strategy

Under the overall construction of curriculum system and curriculum setting, according to the idea of curriculum modularization, the teaching content, teaching progress and depth are reorganized, obsolete and repetitive content is eliminated, the combination of theory and practice is strengthened, and the content of cultivating comprehensive application ability is increased to realize the integration and optimization of teaching content. For example, if the contents of applied regression analysis and econometrics overlap, then applied regression analysis can be merged into econometrics. For another example, some courses, such as descriptive statistics, mathematical statistics, econometrics, statistical prediction and decision-making, are repetitive, so it is necessary to integrate the corresponding teaching contents and re-formulate teaching documents on the basis of careful combing of knowledge.

(B) experimental link setting strategy

Identify the combination of professional knowledge and practical problems, analyze and study the current hot and difficult issues, enrich and enrich the content of practical teaching, compile experimental instructions and counseling materials with application background and practical exercise effect, and clarify the specific links, objectives and requirements of the experiment. Each experimental project should include experimental nature, experimental purpose, experimental requirements, experimental content, experimental steps and result analysis. The experimental contents of all courses are from shallow to deep, step by step, and the practice teaching is standardized.

(C) Software teaching arrangement strategy

In order to make students fully master the relevant statistical software and skillfully use the appropriate software for data sorting and analysis, the teaching of statistical software is divided into three levels: one is to set up SPSS and SQL Server database courses respectively; The second is to set up Lingo, Eviews, SAS and other software experiments in the classroom; The third is to offer software courses such as Latex and R for a short time, and conduct comprehensive training to realize layered software teaching.

(D) Practice course operation strategy

In order to strengthen students' practical ability and employment competitiveness, we will set up workplace etiquette and communication practice, PPT production, statistical model, statistical investigation method and practical training, office automation training and other projects in a short time; Each semester's comprehensive training includes statistical process and analysis writing, accounting practice software, statistical analysis cases and other projects to realize the specialization of comprehensive practice.

(E) Expanding curriculum design strategies

Hire statisticians, survey analysts and entrepreneurs with rich practical experience as part-time professors or off-campus tutors to strengthen off-campus practice; Combined with the second classroom, combined with professional teaching to carry out a variety of extracurricular activities; At the same time, college students' comprehensive ability is exercised by using college students' statistical modeling contest, college students' market survey and analysis contest and college students' mathematical modeling contest to realize the diversification of career development.

Verb (abbreviation of verb) safeguards for training data analysts

(A) the integration of various educational resources to improve teaching efficiency.

Without the guarantee of funds, the training of data analysts can only be an armchair strategist. Therefore, schools and secondary colleges should set up and increase capital investment, and give strong support from both software and hardware to ensure the implementation of funds. Make use of the existing resources of the school, build open laboratories and practice bases, and create a good environment for training data analysts.

The training of data analysts must be based on the combination of production and learning, open education and joint training with enterprises. Establish a new mechanism for joint training of talents between universities and industrial enterprises, and change the current phenomenon that the training of talents in universities is out of line with the needs of industrial enterprises. Using social resources, we will establish practice and experimental bases through joint education and co-construction.

(2) Establish a tutorial system and strengthen the guiding role of teachers.

In order to improve students' data analysis level, secondary colleges should implement undergraduate tutorial system after entering sophomore year; In junior year, undergraduates who have certain scientific research ability can participate in various professional competitions and innovative practice activities related to data analysis under the guidance of their tutors, experience the whole process of data analysis activities personally, and improve their basic skills and innovative consciousness of data analysis; Under the full participation and guidance of the tutor, the comprehensive training in the school, the graduation practice outside the school and the graduation thesis writing of the senior are completed, so that the whole training process can be effectively monitored and the teaching quality can be guaranteed.

(3) Make full use of mass organizations at all levels,

Carry out the second classroom activities. The second classroom is the extension and supplement of classroom teaching. Under the planning and deployment of mass organizations at all levels, we should increase the input of manpower and material resources, systematically and comprehensively consider and design the second classroom and the first classroom, implement standardized management and organized operation, formulate a series of activity plans, and train data analysts through more ways and means.

(D) Reform the evaluation mechanism to stimulate students' interest in learning.

Evaluation is the baton to guide teachers and students. Under the guidance of the current evaluation system, most students and teachers will always seek "good results". To train future data analysts, we must enhance students' learning initiative and improve their practical ability. Strengthening the cultivation of students' ability through various activities and ways must be measured by a scientific evaluation system. Therefore, the evaluation system of "N+2" process is established to track and investigate the whole process of students' ability cultivation and training. The results of the test and information feedback reflect the effect of education and training, and evaluate the changes of students' creativity.

In a word, the project data analysis (division) firm is growing rapidly in China, providing more and more important reference information for the decision-making of the government, financial institutions and enterprises, with good growth and development space. How to find effective information in the information ocean and how to make scientific decisions through effective data has become particularly important, so the prospect of data analysts is bound to be brilliant.