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Anonymous users2024-02-07Everyone knows that there are many people who want to become data analysts nowadays, and data analysts need to learn a lot of knowledge, which is beyond doubt, but they don't know much about the courses that data analysts need to learn, and in general, data analysts need to learn a lot of knowledge. For the courses to be studied by data analysts, they need to be divided into three levels: technical learning, statistical theory, and presentation ability, which are the general content of data analysis.
The technical learning of data analysis involves a lot of work. First of all, we need to get the data from the database or other sources. Many people still rely on a lot of people for data acquisition, and now they can only rely on themselves for data acquisition, SQL tools are needed for data acquisition, and SQL tools are tools born for statistical data acquisition, and SQL tools are generally to solve medium-sized data, Excel can deal with the analysis of small data.
Of course, you also need to learn R language, Python, SPSS and other data, so as to be able to provide data mining capabilities. Of course, you also need to learn the content of the database, and the ability to incorporate data into the database also needs to be mastered, and only after learning these can you do a good job in data analysis. Therefore, we must pay attention to the use of data analysis tools.
Statistics is also the most important work in data analysis, statistics is a crucial course in data analysis, whether it is in the business development or in the development of technology, we need to pay attention to data analysis, we must learn the data analysis thinking framework when learning statistical knowledge, so as to be able to have a good help for future data analysis work.
Finally, let's talk about the ability to express yourself, in fact, no matter what kind of job you are, the ability to express is an important skill, if you have a lot of things in your stomach, but you can't express it, you are not a good data analyst, so a data analyst must be confident, so that you can make it easy for others to understand your ideas. It is very important to have a good expression ability, after analyzing the data, you need to explain the results of data analysis to the customer, not only have strong language skills, but also be able to make ppt, you need to have strict logic when telling and making ppt, so as to be convincing, and you also need to organize the language when doing ppt, and strive to do it well, so as to be able to convince people of your data analysis results.
If you want to enter the data analysis industry, you must understand these contents in advance, which is conducive to designing your own learning plan and learning knowledge efficiently. Of course, if you want to know more about data analysis, please stay along.
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Anonymous users2024-02-06What is a Data Analyst Certificate?
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Anonymous users2024-02-05Data 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.
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Anonymous users2024-02-04Data analysts need to take courses in the following areas:
1) Data management.
a. Data acquisition.
Enterprise requirements: database access, external data file read.
Case Study: Using a Product Information File to Demonstrate Data Read-In Synergy with SPSS.
b. Data management.
Enterprise needs: Encode, clean, and transform large data.
Case Study: The Process of Using the Bank Credit Default Information File SPSS.
1) Selecting, merging and splitting data, and checking for outliers.
2) New variable generation, spss function.
3) Transform data structures using SPSS – transpose and reorganize.
4) Commonly used descriptive statistical analysis functions. Frequency process, description process, exploration process.
c. Data exploration and report presentation.
Enterprise needs: Exploration of enterprise-level data, primarily involving the use of graphs. SPSS report output.
Case study: Business performance documents, how to generate beautiful and clear reports.
1) Check the variables before making the report.
2) Make reports for different types of data processing.
3) The difference between the report generation feature and other options.
2) Data Processing.
a. Correlation and difference analysis.
b. Linear**.
Business Needs: Explore the factors that affect business efficiency and further improve business efficiency.
Case Study: Analysis of the influencing factors of product qualification rate and its ** analysis.
c. Factor analysis.
Enterprise needs: It is necessary to extract the main factors that affect the efficiency of enterprises, and conduct key investment case analysis: customer purchasing power information research.
d. Cluster analysis.
Business needs: Need to know the information of the customers who are buying the product.
Case Study: Customer Purchasing Power Information Research.
e、bootstrap。
Case Study: Bootstrap Sampling.
3）spss**。
SPSS application.
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Anonymous users2024-02-03Here are a few things for data analysts to learn:
1. Statistics.
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 ability.
Learning a programming language will greatly improve the efficiency of processing data. If you can only copy and paste on Excel, you can't get your hands on it fast.
3. Databases.
Data analysts often work with databases, and it's not good to use them without mastering them. Learning how to build tables and use SQL for data processing is an essential skill.
4. Data warehouse.
Many people can't tell the difference between a database and a data warehouse, but in simple terms, a data warehouse records all historical data and is designed to be used efficiently by data analysts.
5. Data analysis methods.
For 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.
6. Data analysis tools.
SAS, MATLAB, and SPSS are often recommended.
The CDA industry standards are jointly formulated by industry experts, scholars and well-known enterprises in the field of data on an international scale and revised and updated every year to ensure that the standards are public, authoritative and cutting-edge. Those who pass the CDA certification exam can obtain the CDA certification certificate in both Chinese and English.
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Anonymous users2024-02-02To answer the question, a data analyst needs to learn statistics, programming skills, databases, data analysis methods, data analysis tools, etc., be proficient in 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.
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Anonymous users2024-02-01Data analysis requires statistics, programming skills, databases, data analysis methods, and data analysis tools; A data analyst is data.
1. Business. The premise of engaging in data analysis will be to understand the business, that is, to be familiar with industry knowledge, the company's business and processes, and it is best to have your own unique insights. >>>More
First, the basic tools.
As the saying goes, if you want to do a good job, you must first sharpen your tools, so SQL, Python, Excel, etc. are the most basic tools for data analysis, but it is not necessary to learn these to be data analysts, the work of data analysts not only needs to master some basic operations of Python and SQL, but more importantly, business knowledge architecture and data can be combined, and business problems in the process of enterprise operation can be found through various data of the enterprise, and can help enterprises solve problems. >>>More
Step 1: Reading according to the outline analysis of the official website, the first reading, let me understand what foundation I am not right, targeted adjustment, the second reading, sorted out the mind map, the third reading, is combined with the two mock volumes, and at the same time make notes in the notebook. >>>More
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.
If you want to engage in the data analysis industry, you still need to learn systematically, and generally cooperate with many project cases to learn in the learning process, which is easy to learn and understand, and you can also accumulate experience.