Data analyst conditions?

I majored in data analysis. What are the requirements for entering the data analyst exam?

Also pay attention before the exam: simulation exercises, imagine the que

I majored in data analysis. What are the requirements for entering the data analyst exam?

Also pay attention before the exam: simulation exercises, imagine the questions that the teacher may ask, and try to answer them by yourself from archaeological questions or self-evaluation questions and keywords. I believe that with continuous practice, you will know which parts need to be strengthened.

What are the entry requirements for project data analysts?

The organizer of the professional technical training program for data analysts of talent certification institutions is the Data Analysis Professional Committee of China Returned Business Federation and the Education Examination Center of the Ministry of Industry and Information Technology. The competent authorities of all provinces and municipalities directly under the Central Government shall establish a professional certification system and carry out training and continuing education.

Ⅲ How to Test Big Data Analysts

The application requirements of big data analysts are as follows:

1, main data analyst:

(1) Persons with college education or engaged in statistical work;

(2) Pass the primary written test, computer test and report assessment, and all the results are qualified.

2. Intermediate data analyst:

(1) Bachelor degree or above, or junior data analyst certificate, or engaged in related work for more than one year;

(2) Pass the intermediate written test and computer test, and all the results are qualified;

(3) Pass the intermediate practical application ability assessment.

3. Senior Data Analyst:

(1) graduate degree or above, or engaged in related work for more than five years;

(2) Obtain the certificate of intermediate data analyst.

(3) After passing the advanced written test and report assessment, obtain the certificate of quasi-senior data analyst;

(4) Candidates have worked in the professional field for five years after obtaining the quasi-advanced certificate, and have written professional data analysis papers. After passing the defense, they obtained the certificate of senior data analyst.

(3) Conditional extended reading of data analysts

Skill requirements

1, understand business

The premise of engaging in data analysis is to understand the business, that is, to be familiar with the industry knowledge and the company's business and processes, and it is best to have your own unique opinions. If it is divorced from the industry cognition and the company's business background, the analysis result will only be an off-line kite with little use value.

2. Understand management

On the one hand, it is the requirement of building a data analysis framework. For example, the theoretical knowledge of marketing and management is needed to guide the determination of analysis ideas. If you are not familiar with management theory, it is difficult to build a data analysis framework, and subsequent data analysis is also difficult. On the other hand, the function is to put forward guiding analysis suggestions for the conclusion of data analysis.

Ⅳ 2016 Conditions for Data Analyst to Enter the Examination

20 16 sub-registration conditions for data analysts (meet one of the following conditions):

1. College degree or above, with more than half a year's continuous practice and internship experience in related industries (provide the original and photocopy of academic certificate, proof of original unit).

2. Those with technical secondary school education have graduated from related majors (e-commerce, computer and its application, communication engineering, electronic information engineering, etc.). ), and engaged in related industries 1 years of practice and internship experience. Non-above majors must have worked in related industries for more than 3 years (provide academic qualifications, original and photocopy of unit certificates).

3. Students (including self-taught students) above the relevant professional college (the same below) must study related majors for more than 2 years; Other students must carry out more than 80 hours of systematic training according to the syllabus (certificate of completion of training school or certificate of completion).

4. Persons who hold relevant vocational and technical certificates (provide the original and photocopy of the certificate) can declare.

Data analyst exam related knowledge:

Examination arrangement:

Data analysts are assessed by the Education Examination Center of the Ministry of Industry and Information Technology and the Data Analysis Professional Committee of China Business Federation. As of August, 20 14, I passed the exam * * * Fundamentals of Data Analysis, Quantitative Management and Quantitative Investment, each with a score of 100 and a score of 60.

Examination time:

There are four exams every year. Please pay attention to the examination notice of CPDA data analyst official website, which is roughly in mid-March, mid-June, mid-September and 65438+February every year.

Issue certificates:

After passing the examination, the students have obtained the professional technical certificate of project data analyst issued by the Education Examination Center of the Ministry of Industry and Information Technology and the certificate of data analyst issued by the Data Analysis Professional Committee of China Business Federation, which can be inquired. See below.

I hope I can help you.

Ⅳ What does it take to be an excellent data analyst?

1. To the superior: understand the data requirements. The core is to find out the satisfaction/dissatisfaction of leaders with data work. Write down in a notebook how many things have been arranged and how urgent they are. In this way, report how much has been completed every week. Doing it slowly does not mean doing it silently. The more effective the work, the more progress will be made in stages and daily. Otherwise, if the leaders fail to see progress, they will think that the recruits have not made progress and will resent it. Most tragedies begin here.

2. Flat business department: understand the business background. Naturally, business processes should be gradually familiar with, and what major business actions have happened before should also be gradually understood. These have a lot to do with the construction of analytical thinking and answering questions. Secretly observe the attitudes of different departments to data, and follow-up cooperation can be targeted.

3. Horizontal technical department: Understand data flow. Data collection-cleaning-storage -BI development-maintenance, who is doing each link and what is the situation. We need to know them one by one. In the future, everyone will often work together, and the relationship will naturally last.

4. For subordinates (if any): Don't show off your official power in a hurry, first understand the types and uses of existing data requirements (reports/topics /BI) and what puzzles subordinates have in their daily work. People who have eaten cakes know the taste of cakes best. Don't be fooled by the pie drawn by the boss. Listen to the real situation at the grassroots level, so that you can better understand the situation.

The above ~ ~ sounds unintelligent, but it is a relatively safe way to stand. There are also some guys who are very * * *, and when they enter the door, they are pregnant with "I brought you an alpha dog!" This idea, counting on a company to do super awesome algorithm Conan the Destroyer, oh no, another day. This radical approach is often easy to get into trouble. Deal with interpersonal relationships first, find out the situation and then be targeted.

ⅵ What skills do you need to be a data analyst?

Next, let's talk about what each part should learn and how to learn.

Data collection: open data, Python crawler

If you only touch the data in the enterprise database, you don't need to get external data, and this part can be ignored.

There are two main ways to obtain external data.

The first is to obtain external public data sets. Some scientific research institutions, enterprises and * * * will release some data, and you need to go to a specific website to download these data. These data sets are usually relatively complete and of relatively high quality.

Another way to get external data is a crawler.

For example, you can get the recruitment information of a position on the recruitment website, the rental information of a city on the rental website, the list of movies with the highest douban rating, the likes of Zhihu and the list of comments on Netease Cloud Music through the crawler. Based on the data captured on the network, we can analyze a certain industry and a certain population.

Before crawling, you need to know some basic knowledge of Python: elements (list, dictionary, tuple, etc. ), variables, loops, functions (the linked novice tutorial is very good) ... and how to realize a web crawler with a mature Python library (URL, BeautifulSoup, requests, scrapy). If you are a beginner, it is recommended to start with urllib and BeautifulSoup. (PS: Python knowledge is also needed for subsequent data analysis, and problems encountered in the future can also be viewed in this tutorial)

Don't have too many online crawler tutorials. Crawlers can recommend Douban's web pages to crawl. On the one hand, the web page structure is relatively simple, on the other hand, watercress is relatively friendly to reptiles.

After mastering the basic crawler, you need some advanced skills, such as regular expression, simulating user login, using proxy, setting crawling frequency, using cookie information and so on. To deal with the anti-crawler restrictions of different websites.

In addition, the data of commonly used e-commerce websites, question and answer websites, comment websites, second-hand trading websites, marriage websites and recruitment websites are all good practice methods. These websites can get very analytical data, and the most important thing is that there are many mature codes for reference.

Data access: SQL language

You may have a question why you didn't talk about Excel. When dealing with data within 10 thousand, Excel generally has no problem in analysis. Once the amount of data is large, it will be insufficient, and the database can solve this problem well. Moreover, most enterprises will store data in the form of SQL. If you are an analyst, you also need to know the operation of SQL and be able to query and extract data.

As the most classic database tool, SQL makes it possible to store and manage massive data and greatly improves the efficiency of data extraction. You need to master the following skills:

Extracting data under certain circumstances: The data in the enterprise database must be large and complicated, so you need to extract the parts you need. For example, you can extract all the sales data of 20 18, the top 50 products sold this year, the consumption data of users in Shanghai and Guangdong ... SQL can help you complete these tasks with simple commands.

Database addition, deletion, query and modification: these are the most basic operations of the database, but they can be realized with simple commands, so you just need to remember the commands.

Grouping and aggregation of data, how to establish the relationship between multiple tables: this part is an advanced operation of SQL, and the relationship between multiple tables is very useful when you deal with multidimensional and multi-data sets, which also allows you to deal with more complex data.

Data preprocessing: Python (Panda)

Many times, the data we get are unclean, with repeated data, missing data, abnormal values and so on. At this time, it is necessary to clean up the data and deal with the data of these impact analysis well in order to get more accurate analysis results.

For example, air quality data, many days of data are not monitored due to equipment reasons, some data are repeatedly recorded, and some data are invalid when equipment fails. For example, there are many invalid operations in user behavior data that are meaningless for analysis and need to be deleted.

Then we need to use corresponding methods to deal with it, such as incomplete data, whether we directly remove this data or use adjacent values to complete it. These are all issues that need to be considered.

For data preprocessing, learn the usage of panda and deal with general data cleaning. The knowledge points to be mastered are as follows:

Selection: data access (label, specific value, Boolean index, etc. )

Missing value processing: delete or fill missing data rows.

Duplicate value processing: judgment and deletion of duplicate values

Handling of spaces and abnormal values: Clear unnecessary spaces and extreme abnormal data.

Related operations: descriptive statistics, applications, histograms, etc.

Merge: a merge operation that conforms to various logical relationships.

Grouping: data division, independent function execution and data reorganization.

Refresh: Quickly Generate PivotTables

Probability theory and statistical knowledge

What is the overall distribution of data? What are population and sample? How to apply basic statistics such as median, mode, mean and variance? If there is a time dimension, how does it change with time? How to do hypothesis testing in different scenarios? Data analysis methods mostly come from the concept of statistics, so statistical knowledge is also essential. The knowledge points to be mastered are as follows:

Basic statistics: mean, median, mode, percentile, extreme value, etc.

Other descriptive statistics: skewness, variance, standard deviation, significance, etc

Other statistical knowledge: population and sample, parameter and statistics, error line.

Probability distribution and hypothesis testing: various distribution and hypothesis testing processes

Other knowledge of probability theory: conditional probability, Bayes, etc.

With the basic knowledge of statistics, you can use these statistical data for basic analysis. By describing the indicators of data in a visual way, we can actually draw many conclusions, such as 100, the average level and the changing trend in recent years. ...

You can use the python package Seaborn(python) to do these visual analysis, and you can easily draw various visual graphics and get instructive results. After understanding the hypothesis test, we can judge whether there are differences between the sample indicators and the assumed overall indicators, and whether the verification results are within the acceptable range.

Python data analysis

If you have some knowledge, you will know that there are actually many books about Python data analysis on the market at present, but each one is very thick and has great learning resistance. But in fact, the most useful information is only a small part of these books. For example, testing hypotheses in different situations with Python can actually verify the data well.

For example, mastering the method of regression analysis, through linear regression and logical regression, we can actually conduct regression analysis on most data and draw relatively accurate conclusions. For example, DataCastle's training competition "house price forecast" and "position forecast" can be realized through regression analysis. The knowledge points that need to be mastered in this part are as follows:

Regression analysis: linear regression and logical regression.

Basic classification algorithms: decision tree, random forest ...

Basic clustering algorithm: k-means ...

Basis of feature engineering: how to optimize the model through feature selection

Parameter adjustment method: how to adjust the parameter optimization model

Python data analysis packages: scipy, numpy, scikit-learn, etc.

At this stage of data analysis, most problems can be solved by focusing on regression analysis. Using descriptive statistical analysis and regression analysis, you can get a good analysis conclusion.

Of course, with the increase of your practice, you may encounter some complicated problems, so you may need to know some more advanced algorithms: classification and clustering, and then you will know which algorithm model is more suitable for different types of problems. For model optimization, you need to learn how to improve the prediction accuracy through feature extraction and parameter adjustment. It's a bit like data mining and machine learning. In fact, a good data analyst should be regarded as a junior data mining engineer.

Systematic actual combat

At this time, you already have the basic data analysis ability. However, it is necessary to conduct actual combat according to different cases and different business scenarios. If you can complete the analysis task independently, then you have defeated most data analysts in the market.

How to carry out actual combat?

The open data set mentioned above, you can find some data in the direction you are interested in, try to analyze it from different angles and see what valuable conclusions you can draw.

Another angle is that you can find some problems that can be analyzed from your life and work. For example, there are many problems to be discussed in the direction of e-commerce, recruitment, social networking and other platforms mentioned above.

At the beginning, you may not consider all the problems thoroughly, but with the accumulation of your experience, you will gradually find the direction of analysis, and what are the approximate dimensions of analysis, such as ranking, average level, regional distribution, age distribution, correlation analysis, future trend prediction and so on. With the increase of experience, you will have some feelings about data, which is what we usually call data thinking.

You can also look at the industry analysis report, look at the perspective of excellent analysts, and analyze the dimensions of the problem. In fact, this is not a difficult thing.

After mastering the primary analysis methods, you can also try to do some data analysis competitions, such as three competitions customized by DataCastle for data analysts, and you can get scores and rankings by submitting answers:

Employee turnover prediction training competition

Jinxian County Housing Price Forecasting Training Competition, USA

Beijing PM2.5 concentration analysis training competition

The best time to plant a tree is ten years ago, followed by now. Go find a data set now and get started! !

ⅶ What are the entry requirements for data analysts?

Requirements:

1. College degree or above, with more than half a year's statistical work experience;

2. Skillfully use office software, master and use excel spreadsheet functions, and have strong data statistics and analysis capabilities;

3. Conscientious and enterprising, with strong sense of responsibility and dedication, strong sense of collective identity and teamwork spirit.

ⅷ What does a data analyst do, and what are the conditions for entering the exam?

The purpose of data analysis is to adapt to the requirements of the era of big data sources, strengthen the standardization, specialization and professionalization of data analysts, and further improve the professional quality and ability level of data analysts in China. It has been promulgated and implemented by relevant national ministries and commissions, aiming at expressing economic principles with mathematical models by mastering a large number of industry data and scientific calculation tools, scientifically and reasonably analyzing the future benefits and risks of investment and operation projects, and providing a basis for scientific and rational decision-making.

There are no restrictions on the application conditions.

ⅸ What do data analysts mainly do?

Specializing in the collection, collation and analysis of industry data, and conducting industry research, evaluation and prediction according to the data.

Internet itself has the characteristics of digitalization and interactivity, which brings a revolutionary breakthrough to data collection, collation and research. In the past, data analysts in the "atomic world" spent a lot of money (funds, resources and time) to obtain data supporting research and analysis, and the richness, comprehensiveness, continuity and timeliness of data were much worse than those in the Internet era.

Compared with traditional data analysts, data analysts in the Internet era are facing not data shortage, but data surplus. Therefore, data analysts in the Internet era must learn to use technical means to process data efficiently. More importantly, data analysts in the Internet era should constantly innovate and break through the methodology of data research.

As far as the industry is concerned, the value of data analysts is similar. As far as the press and publication industry is concerned, it is the key to the success of the media whether the media operators can accurately, detailedly and timely understand the audience and the changing trend.

(9) Conditional extended reading of data analysts

The definition of the profession of data scientist is a bit broad. Also known as data scientists, the work done by different companies in different industries may vary greatly.

Some are partial to machine learning and modeling, and some are partial to data analysis. Some people are called data scientists. They do many things similar to software engineers (SWE). Some products are short, flat and fast. Some long-term studies look at the effect for a year or two or even longer.

The ultimate goal of data analysis is the ability to guide product improvement through data analysis. In the final analysis, any skill needs to serve this purpose.

ⅹ What are the application conditions for project data analysts?

College degree or above, more than 2 years working experience in data analysis.

Bachelor degree or above, you can apply directly.

Project data analyst integrates examination and training, and needs to attend training to take the examination.

Now, the project data analyst has changed its name to data analyst.