How do big data analysts get started? How to take the Big Data Analyst exam

At present, cloud computing and big data analysis are relatively popular, with the guidance of national policies, this industry has a huge talent gap, if you want to know more about data analysis, you can pay attention to the "Jiudaomen Community" to visit the forum, such as the National People's Congress Statistics Forum, there are many resources on it, just find a few books to start reading, the most important thing is to start. If you can't do self-control, you can also sign up for a class, learning from experienced people is always faster than self-learning, and you can avoid a lot of detours.

Data analysts need to learn statistics, programming skills, databases, data analysis methods, data analysis tools, etc., and also be proficient in using Excel, be familiar with and proficient in at least one data mining tool and language, have the ability to write reports, and have a solid SQL foundation.

1. Mathematical knowledge.

Mathematics is the basic knowledge of a data analyst. For junior data analysts, it is enough to understand some basic content related to describing statistics, have a certain ability to calculate formulas, and understand common statistical model algorithms is a plus.

2. Analytical tools.

For junior data analysts, playing with Excel is a must, pivot tables and formulas must be proficient, and VBA is a plus. In addition, it is better to learn a statistical analysis tool, SPSS as a starting point.

For senior data analysts, the use of analytical tools is a core competency, a basic must for VBA, at least one of them must be proficient in the use of SPSS SAS R, and other analysis tools (such as MATLAB) as appropriate.

3. Programming language.

For junior data analysts, they can write SQL queries, and if necessary, write Hadoop and Hive queries, which are basically OK. For senior data analysts, in addition to SQL, it is necessary to learn Python to obtain and process data with half the effort. Of course, other programming languages are also possible.

Data analysts can be engaged in: IT system analysts, data scientists, operations analysts, and data engineers.

The requirements for the big data startup analyst application are as follows:

1. Junior Data Analyst:

1) Have a college degree or above, or engage in statistical work;

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

2. Intermediate Data Analyst:

1) Have a bachelor's degree or above, or a junior data analyst certificate, or have been engaged in related work for more than one year;

2) Pass the intermediate written test and computer-based test, and pass all the results;

3) Pass the intermediate practical application ability assessment.

3. Senior Data Analyst:

1) Master's degree or above, or engaged in related work for more than five years;

2) Obtain an Intermediate Data Analyst Certificate.

3) After passing the senior written examination and report assessment, obtain the quasi-senior data analyst certificate;

4) After obtaining the quasi-Gaoqing side letter certificate, the candidate will work in the professional field for five years, and write a professional data analysis **, and obtain the senior data analyst certificate after passing the defense.

The Big Data Analyst exam needs to be registered at an institution authorized by the Education and Examination Center of the Ministry of Industry and Information Technology.

Big Data Analyst Profile:

Big data analyst refers to the process of scientific analysis, mining, display and use for decision support of big data based on various analysis methods.

Application conditions for data analyst at the beginning level:

College degree or above in statistics, mathematics, economics, management or related majors; Have more than one year of work experience; Be of good character; Be physically and mentally healthy; Discipline. Pass the primary written test, computer-based test, report assessment, and all the results are qualified. You need to ask He Zhi to prepare the following materials:

An electronic photograph of the applicant; I have both sides of the front; My academic certificate; Fill in 1 copy of the training registration form; Prepare the above information and send it to the relevant big data analyst admissions teacher of the registration unit; At the same time, the relevant registration fee shall be paid.

The Role of the Big Data Analyst:

Big data analysts can make enterprises have a clear understanding of the current situation and competitive environment of the enterprise, risk evaluation and decision support, and can make full use of the value brought by big data. Therefore, big data analysts are no longer simple IT staff, but core people who can participate in the development of enterprise decision-making.

Data analysis can be said to have a long history, and Mr. Bookkeeper can also be called a data analyst in a sense, analyzing current accounts, receivables, expenses, etc., but this is not big data analysis, just statistics based on their own data, so to understand the responsibilities of big data analysts, we must understand the difference between data analysis and big data analysts.

The knowledge that big data analysts should learn includes, the theoretical basis of statistical probability, the practical application of software operation combined with analysis models, the directional selection of data mining or data analysis, and the business application of data analysis.

1. Theoretical basis of statistical probability

This is the most important thing, the platform of a thousand miles, starting from the soil, and the most important thing is the bottom layers. Statistical thinking, statistical methods, here is first of all the acquisition and sorting of market research data, then the simplest descriptive analysis, followed by the commonly used inferential analysis, analysis of variance, to advanced correlation, regression and other multivariate statistical analysis, master these principles, in order to proceed to the next step.

2. The software operation is combined with the analysis model for practical application

The mainstream software for data analysis is (from easy to difficult): Excel, SPSS, STATA, R, SAS, etc. The first step is to learn how to operate the software, and then to use the software to process and analyze the data step by step, and finally output the results, verify and interpret the data.

3. Directional selection of data mining or data analysis

In fact, data analysis also includes data mining, but in the work will be subdivided into the direction of analysis and mining, the two have been different, and data mining also involves many model algorithms, such as: association law, neural network, decision tree, genetic algorithm, visual technology, etc.

4. Data analysis business application

This step is also the most difficult step to learn, because the industry is different, the business is different, the analysis methods used in the business are also different, and the actual work is to solve the business problem, so the insight into the business is very important.

Data analysis is simply divided into these major parts:

Data collection, data cleaning, data analysis, data visualization

1. Data collection.

The so-called data collection is not a data crawler as we understand it, especially the data we encounter in our work is from the data in the system, the data from the database, and the data from the log. The commonly used means of data collection are: SQL Python, where SQL is a necessary skill for data analysis, and Python is a plus.

2. Data cleaning.

The collected data is generally irregular, the fields are missing or there are errors, and the analysis results will be abnormal. Data cleaning requires some simple statistical foundations.

3. Data analysis.

In terms of business data analysis, the most important thing in data analysis is industry knowledge and logical thinking ability, and industry knowledge is often obtained through work experience in the industry. Logical thinking ability requires continuous exercise.

4. Data visualization.

To make the conclusion easier to understand, there are many data visualization products at home and abroad, and I commonly use Tableau Excel Python and so on.

What is a Data Analyst Certificate?

1. Mathematics knowledge background at the undergraduate or master's level of applied mathematics, statistics, and quantitative economics is required.

2. Proficient in at least one of SPSS, Statistic, eViews, SAS and other data analysis software.

3. At least be able to use acess for database development;

4. Master at least one mathematical software: matalab, mathmatics for the construction of new models.

5. Master at least one programming language;

6. Of course, knowledge of other application fields, such as marketing, economic statistics, etc., because this is the main application field of data analysis.

Big data analyst refers to the process of scientific analysis, mining, display and use for decision support of big data based on various analysis methods.

The responsibility of the operation and maintenance position is to complete the construction of the big data platform, the deployment of components, testing, control, maintenance, etc., which do not require very strong logical thinking ability, and can also be engaged in for people with poor logical thinking ability.

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