A. Who is the lecturer of e-commerce marketing data analysis course?

The lecturer of e-commerce marketing data analysis is Webtrekk (the largest German website data analysis service provider Webtrekk), the head of technology and consulting in China, a blogger of data research and commercial application, and a senior expert in data analysis. Rich experience in data projects.

B. What are the training courses related to data analysis?

According to the course of analyst, there are two levels of content. Only by studying and applying these courses of data analysts can you become a top big data analyst.

First, the curriculum level.

The first level: the content of data analysis course is mainly from theory-practice-case application step by step, which enables students to fully grasp the basic theory of probability and statistics, skillfully use a set of professional analysis software such as Excel, SPSS, SAS, etc., have good business understanding ability, and can use common data analysis methods to process and analyze data according to business problem indicators, and get logical business reports.

The second level: On the basis of the first level, the second level includes modeling analysts and big data analysts, which provides timely, effective, easy to implement and reliable data support for enterprise decision-making. Modeling analyst refers to people who specialize in data analysis and data mining in ZF, finance, telecommunications, retail, Internet, e-commerce, medicine and other industries. This course aims at the whole process of data mining, and deeply teaches the main algorithms of data mining with the case background of finance, telecommunications, e-commerce and retail. The effective combination of SAS Enterprise Miner, SPSS Moderler, SAS programming and SQL will enable students to be competent for all-round data mining application scenarios. Big data analyst, this course is aimed at big data analysis. Starting with the introduction of data analysis, JAVA language and linux operating system, this paper systematically introduces the theoretical knowledge of hadoop, HDFS, MapRece and Hbase, as well as the ecological environment of hadoop, and demonstrates the installation and configuration of three modes of Hadoop in detail. In the form of cases, this paper focuses on the clustering, classification and topic recommendation of big data analysis based on mahout project. By showing the actual big data analysis cases, students can understand the real value of big data analysis in a short time and master how to use hadoop architecture in the process of big data analysis, so that students can quickly become big data analysts who pay equal attention to theory and actual combat, thus better adapting to the employment situation with strong demand for big data analysts under the current Internet economy background.

Second, the knowledge structure of data analysts

C.0 basic business data analysis

Basically not. Business analysis mainly comes from three sources: 1. The depth of the industry needs relevant common sense such as economics and the actual accumulation of deep cultivation in this field; 2. Data mining analysis foundation, statistical foundation, general methods of data mining, programming statistical skills; 3. Presentation skills, PPT and report writing skills.

Basically, it requires basic skills+years of experience and ability to do a good job. So a course can only be an introduction.

D. I want to study data analyst. Please recommend a reliable study place!

At present, there are two mainstream.

Project Data Analyst (CPDA) sponsored by the Data Analysis Committee of the Business Federation of the Ministry of Industry and Information Technology and the Education Examination Center.

Data analyst sponsored by Renmin University Economic Forum: CDA.

About CPDA

The full name of CPDA is Project Data Analyst. The earliest data analysis training in China was originally organized by the Ministry of Information Industry. At present, CPDA is led by the Data Analysis Committee of China Federation of Industry and Commerce and the Education Examination Center of the Ministry of Industry and Information Technology. The content mainly focuses on enterprise analysis in investment, operation, management and other fields, similar to MBA courses.

Courses include data analysis, strategy management, quantitative investment, quantitative management, etc. , covering every link of enterprise operation, and analyzing management, operation and investment with data analysis method. It should be said that enterprise management is suitable for learning CPDA to analyze and guide at the management level.

At present, many courses have no practical operation mode, but CPDA does, which introduces many cases and models of enterprise production, management, operation, investment analysis and decision-making The purpose is also to let the students have theoretical support and practical models in management positions, so that everyone can have practical and operational practical models to analyze.

About CDA

The full name of CDA is Data Analyst, sponsored by Economic Forum of Renmin University of China. Mainly talking about data analysis methods, techniques and software operations.

Courses include: 1, statistical probability basis; 2. Data analysis model method; 3. Application of software and tools. If these technologies are not available, it is impossible to play data analysis. Therefore, CDA is mainly aimed at the necessary technical training for data analysts, which is a whole process from data collection, storage, sorting, cleaning, analysis, inspection to result report, as well as the operation of data analysis for some software.

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So I think everyone should have a clear understanding of the difference between the two. If you already have the basic technology of data analysis and want to be a management, you can choose CPDA；; .

If you are a beginner, switch to zero foundation, have a weak foundation, or just want to do technical work, the first step is to master the methods and skills of data analysis, then you can choose CDA.

In addition, if you are a senior analyst, a senior mining engineer or a big data analyst who studies algorithms, you can refer to other related teacher training.

Sc-cpda data analysis public exchange platform

E. Why do you want to learn business data analysis?

There is a famous paradox in Greece. "If 1 grain of millet falls on the ground, it can't form a grain heap, two grains of millet fall on the ground and three grains can't form a grain heap, and so on, no matter how many grains fall on the ground, it can't form a grain heap. However, this is not the case. "

This paradox tells us that quantitative change leads to qualitative change and needs an obvious dividing line. If quantity is quantitative data, quality is the conclusion. Then, what data analysis does is to analyze quantity, which produces "qualitative" and "qualitative". Quantitative understanding of the law of history ("quality"), so as to predict the future.

About understanding historical laws, common data analysis ideas, as shown above, probably introduce four kinds. Group comparison, trend analysis, anomaly analysis and ranking analysis; There are three main purposes:

1) Find the periodic law.

2) Find the characteristics of each classification.

3) finding anomalies and extreme values

Understanding history is to better predict the future.

If we find the periodic law, we can know which fluctuations are normal and which ones need attention.

Knowing the characteristics, we can sum up some transactions with the same classification, which may also have this characteristic;

Knowing the anomaly and extreme value, we can analyze it deeply, find out the reason for the solution, or take measures to carry forward the extreme value.

F. Analysis of e-commerce marketing data What are the knowledge points of how to analyze marketing data in Module 3 of this course?

The knowledge points of e-commerce marketing data analysis in module 3 of this course are guided by modules, what is included in unit 1 e-commerce marketing analysis, the construction of unit 2 marketing analysis system, unit 3 marketing analysis dimensions, and unit 4 how to analyze advertising effects.

G. Business analysis is very popular in recent years. What does this major study?

The birth of BA originated from the Internet and big data. Since the emergence of the mobile Internet, the data of business operations has greatly increased, and big data has become an important basis for business operations and decision-making. In the past, it was enough for enterprises to do financial, market and operational analysis with Excel and Word. Now enterprises need to analyze a lot of data, so who can analyze so much data? There is no doubt that the era of simple Office analyzing data is gone forever. With the rise of "Internet of Things", big data will play a key role in the future economy. However, universities with traditional majors have no corresponding majors and are closer to statistics, computer science and business. But none of these majors can fully meet the needs of the big data industry.

Statistics majors don't know the laws of business operation and market, and they don't have computer-related knowledge. When faced with data storage systems and open source software for analyzing data, their knowledge reserves are insufficient. Computer majors are good at writing codes, but they don't know the knowledge of business and statistics, and even many people lack the ability of business communication. Business students, however, have no background in science and engineering and have no excellent statistical and programming skills. Therefore, facing the demand of industry and market and the existence of compound talent gap, BA major came into being.

Students will learn about statistical modeling, data management, visualization and optimization, information security, decision-making and so on. At the same time, students will learn the following programming tools to analyze a large number of unstructured data sets, turn the analysis results into decisions that can improve business performance, and effectively show complex data to senior decision makers.

H. What is the difference between a business analyst and a data analyst?

Business analyst:

Generally speaking, business analysts need to have a certain MBA background, have a strong insight into the market, upstream and downstream, and specialized business, have systematic data collection, market research, and sorting capabilities, and have good word processing skills, strong logical thinking skills, keen observation skills, and independent analysis skills. Many business analysts need to complete an industry analysis report independently, and look at the relationship, advantages and disadvantages of the company, all competitive companies and upstream and downstream from the perspective of the whole industry.

You need to know all kinds of strategic models and methodologies, such as SCP, RFM, Boston matrix, golden pagoda principle, 5W2H, MECE analysis, SWOT analysis, etc.

Data analyst:

Data analysts are more inclined to analyze and model a company's products to promote growth.

Strong landing ability and communication skills with various business departments are required.

You need to know the relevant knowledge of statistics, find the hidden rules of user behavior in big data, and master basic statistical models and knowledge: regression analysis, cluster analysis, time series, multivariate statistics, Bayesian and so on. If you study products online, you need to know: funnel analysis, product transformation and so on.

1. What is the content of e-commerce marketing data analysis course?

In the era of big data, e-commerce pays attention to product outsourcing, and needs to pay more attention to the problems reflected behind the data. For example, in addition to the financial data and industry competition environment data that all enterprises are concerned about, e-commerce enterprises should pay more attention to: 1. Website operation data: PV, UV, number of comments, bounce rate, new user registration purchase rate, advertising conversion rate, average acquisition cost per user, etc. , SEM flow ratio; 2. User data: the age of website users, the main shopping time of users, the geographical distribution of users, the use of browsers by users, the occupation of users and other related crowd attribute data. This course aims at the talents and skills of data analysis positions in e-commerce enterprises, and provides learners with complete data analysis knowledge of data collection, mining and analysis, reporting and application covered by e-commerce marketing data analysis through online learning. In addition to teaching related to data analysis, this course also covers the necessary knowledge of e-commerce enterprise organizational structure, workflow, working methods, job positioning and so on, which is of positive significance for students to understand the industry, go deep into the industry and apply the industry. Finally, for every teaching link in the course, almost all personal knowledge and skills and real e-commerce analysis and application cases are covered, which can help students quickly enter the role and apply what they have learned.