What you need to know about data analysis

If you want to become a data analyst, basically you need to master statistics related knowledge, **language: pyton, sql and the like, and you are familiar with all kinds of data analysis**, and the most important point is to be a data analyst, you must be proficient in data analysis software such as spss, which is very important, because the use of good tools will increase your efficiency several times.

A good data analyst needs to have the following qualities: have a solid SQL foundation, be proficient in Excel, have a statistical foundation, master at least one data mining language (R, SAS, PYTHON, SPSS), have good communication and presentation skills, be ready for continuous learning, have strong data sensitivity and logical thinking ability, have a deep understanding of the business, have a manager's thinking, and be able to consider problems from the perspective of a manager.

Data analysis requires learning the following:

1. Statistics. 2. Programming ability. 3. Databases. 4. Data warehouse.

5. Data analysis methods. 6. Data analysis tools.

Want to become a data analyst.

There are two things that should be focused on:

Language. These are the most basic tools, Python is the best language to get started with data, while R language tends to be statistical analysis, graphing, etc., and SQL is the database. Since it is data analysis, I usually spend more time dealing with data analysis and data collection.

A series of data analysis tasks such as data cleaning and data visualization need to be completed by the above tools.

2.Business capabilities.

The meaning of the existence of data analysts is to help enterprises achieve business growth through data analysis, so business capabilities are also a must. The company's products, users, market environment and employees are all content that must be mastered, and through the establishment of these contents, it helps enterprises establish specific business indicators and assist enterprises in making operational decisions.

Of course, these are the most basic things that data analysts need to focus on learning if they want to change careers, and if they want to have a better development in the future, they also need to learn more skills, such as business management, artificial intelligence, etc.

For more information about data analysts, you can go to the CDA Data Analytics Certification Center. Adhering to the new concept of advanced business data analysis and the new norms of the CDA Code of Professional Ethics and Conduct, global CDA licensees give full play to their data professional capabilities, promote scientific and technological innovation and progress, and contribute to sustainable economic development.

Statistics, mathematics, and logic are the foundation of data analysis, and they are the internal skills of data analysts.

Only by mastering statistics can we know what kind of input and what kind of output and what kind of role each data analysis model, and we don't necessarily have to understand every algorithm at the beginning.

If we want to be data miners, data capabilities are our bread bowl.

If you don't have the ability to do math, you can do it with ready-made models or modules, but it will definitely affect your technical improvement, and of course, your promotion.

If you plan to become a data analyst, you need to have basic knowledge in all three fields: statistics, databases, and economics; CET-4 or above, familiar with the English name of the indicator; Knowledge of Internet product design.

What data analysts need to learn 1. StatisticsI see that some people have recommended a lot of professional books on statistics, and many people read "Probability Theory and Mathematical Statistics", and they haven't read much of other statistics-related content. For Internet data analysis, it is not necessary to master too complex statistical theories. Therefore, it is enough to learn statistics according to the undergraduate textbook.

2. Programming abilityLearning a programming language will greatly improve your efficiency in processing data. If you can only copy and paste on Excel, the hands-on ability is impossible to be fast. I recommend Python, it's quicker to get started and easier to write.

3. Database data analysts often deal with databases, and it is not possible to use databases without mastering them. Learning how to build tables and use SQL language for data processing can be said to be an essential skill. 4. Many people in the data warehouse do not know the difference between the database and the data warehouse, in short, the data warehouse records all the historical data and is specially designed to facilitate the efficient use of data analysts.

5. Data analysis methodsFor Internet data analysts, you can take a look at "Lean Startup" and "Lean Data Analysis" to master the commonly used data analysis methods, and then adjust and flexibly combine them according to your company's products.

It is recommended to choose Shifang Ronghai for data analysis.

Shifang Ronghai independently developed an interactive intelligent teaching system with exclusive patented technology. The knowledge required to learn data analysis is as follows:

1. Excel: Professional analysts will use Excel to process aggregated data.

2. SQL language: SQL (Structured Query Language) is a computer language used to process and retrieve data stored in relational databases, and the most important thing is to master the query function.

3. Visualization tools: Visualizing data can make people understand data better.

4. Python: If you want to explore more deeply, you need to learn Python for data mining. Python is an object-oriented, high-level programming language that is primarily used for the web as well as for application development.

Python has graphical and visualization tools, as well as an extended analysis toolkit for better data analysis.

5. SAS: SAS (statistical analysis software) is a set of modular large-scale integrated application software system. SAS is capable of deep mining and analysis of data.

Shifang Ronghai has always adhered to the development concept of "people-oriented innovative education", firmly runs scientific and technological innovation through the entire vocational education training process, and constantly introduces novel teaching models and teaching tools to enhance students' learning interest and efficiency, and cultivate more compound talents for the society.

First of all, you should be proficient in the use of office software, proficient in the use of various mathematical statistics, data analysis, data mining tool software, familiar with the application of various analysis software. Familiar with Linux operating system, with good industry analysis, judgment ability and writing skills.

1) Have business sensitivity, quick response, and be able to communicate well; 2) Project practical experience in data analysis and data warehouse modeling; 3) At least 3 years of experience in data analysis, with experience in Internet product and operation analysis; 4) Familiar with R, SAS, SPSS and other statistical analysis software, proficient in the use of Python, proficient in use.

SQL, Hive, etc.; 5) Bachelor degree or above, major in mathematics, statistics, computer science, operations research and other related majors;

Data analysis refers to the process of analyzing a large amount of data collected with appropriate statistical analysis methods, extracting useful information and forming conclusions, and then making detailed research and summarizing the data. This process is also a support process for the quality management system. In practice, data analytics helps people make judgments so that appropriate actions can be taken.

It is the process of collecting data in an organized and purposeful manner, analyzing it, and turning it into information. Data analysis classification. Data analysis is divided into descriptive statistical analysis, exploratory data analysis and confirmatory data analysis. Among them, exploratory data analysis focuses on discovering new features in the data, while confirmatory data analysis focuses on the confirmation or falsification of existing hypotheses.

Common methods for data analysis. PEST Analysis:. It is a model that uses environmental scanning to analyze four factors in the overall environment: political, economic, social, and technological.

This is also part of the external analysis when doing market research, which gives the company an overview of the different factors in the overall environment. This strategic tool is also an effective way to understand the growth or decline of the market, the situation of the company, its potential and the direction of its operations. It is generally used for macro analysis.

SWOT Analysis:. Also known as the advantages and disadvantages analysis method or Dawes matrix, it is a kind of enterprise competitive situation analysis method, is one of the basic analysis methods of marketing, through the evaluation of their own strengths, weaknesses, opportunities and threats in external competition, in order to formulate a development strategy before the in-depth and comprehensive analysis of their own and competitive advantage positioning.

Most of the data analysis will use the following knowledge in statistics, which can be focused on:

Basic Statistics: Mean, Median, Mode, Variance, Standard Deviation, Percentile, etc. Probability Distributions: Geometric Distributions, Binomial Distributions, Poisson Distributions, Normal Distributions, etc. Populations and Samples: Understand the basic concepts, the concept of sampling.

Confidence Intervals and Hypothesis Testing: How to Perform Validation Analysis.

First of all, if we want to learn data analysis, we need to learn Excel, data visualization, database knowledge, Python and R language, statistical knowledge, analytical thinking, business knowledge, and learn these knowledge to be able to do a good job in data analysis.

First of all, let's talk about the excel in data analysis knowledge, this excel has been touched by many people, the focus is to understand various functions, such as sum, count, sumif, countif, find, if, left right, time conversion, etc.; But you don't need to learn all the functions, master some of the most commonly used ones, and use others as you search when you use them. In addition, vlookup and pivot table are two cost-effective tricks, after these two are done, there is basically no difficulty in the statistics of data within 100,000 pieces. However, this data analysis tool has a certain limitation, that is, Excel can only handle and analyze small data, and cannot deal with large data.

If you need to deal with large amounts of data, you still need tools that use databases.

And then let's talk about data visualization. What is data visualization? It is that we present the data to others in the form of charts.

Why do you present the data in the form of charts? There are two reasons for this, the first is that charts can represent data more intuitively. The second is that many people can't understand the data, so they need a simple and easy-to-understand way of data expression, there is a classic saying in the data analysis industry, words are not as good as tables, and tables are not as good as pictures.

The best way to explain the best way is to make a PPT with clear views and detailed data to customers or leaders. Although Excel can also do a lot of data visualization functions, if you want to get a more professional visualization effect, it is recommended to learn some programming knowledge.

There are also many people who have used BI to process data, and in general, the difference between BI and charts is that BI is good at interaction and reporting, and is suitable for interpreting data that has happened and is happening. Power BI is suitable for individual learning, and FineBI is suitable for enterprise-level applications.

For data analysis, the most important knowledge to master should be Excel, first of all, you must know Excel above Word is the most basic.

Analysts need the following skills: Excel, SQL, Statistics, SPSS, Python R, etc.

It is recommended to start with Excel, because Excel is the most used and most powerful data analysis tool, and it is easy to get started, because most people have been exposed to Excel.

A good data analyst needs to have the following qualities: have a solid SQL foundation, be proficient in Excel, have a statistical foundation, master at least one data mining language (R, SAS, PYTHON, SPSS), have good communication and presentation skills, be ready for continuous learning, have strong data sensitivity and logical thinking ability, have a deep understanding of the business, have a manager's thinking, and be able to consider problems from the perspective of a manager.

Air conditioning analysis this quality to master a lot of knowledge to prove that you can get into the study should be able to do a close relatives for a game, so I think this should be relatively good, can you tell these critical speeds.

Python and SQL are also important to understand, and when there is a lot of data, these two tools can help us solve a lot of problems.

Solid basic skills, such as statistics, modeling learning.

Through some practice, solve some of the company's business problems.

10 must-haves**.

For data analysis, you must first be able to use a computer, and you will be able to use relevant programs and software.

Mastery of basic tools.

Learning of theoretical knowledge.

Cultivation of analytical thinking.

First, the mastery of basic tools.

Data analysts: Excel, SQL, and Python are the three basic tools that data analysts must know.

2. Theoretical knowledge.

Statistics and machine learning should be grasped with both hands, and both hands should be hard.

In terms of theoretical knowledge, the most important knowledge that data analysts need to master is statistics and machine learning, but there are still some ways to master it and which fields to master.

3. Data analysis thinking.

There is no more important ability than this.

Analytical thinking is the core competitiveness of data analysts, and the python, sql, and machine learning knowledge learned above are all at the tool level, and if you want to use them well, you also need to control analytical thinking. In the interview, the examination of analytical thinking is also quite important.

For the learning of analytical thinking, I suggest that newcomers also start by reading: data analysis, product thinking, and logical thinking.