In today's world where big data is widely used, data has become one of the very important competition points between companies. And as the profession that deals most closely with data - data analyst - will also usher in a golden employment period. According to Avery Research, the shortage of data science jobs has eased considerably in the past two years thanks to the hiring of 800,000 new data scientists across a wide range of industries; but there are still thousands of openings in the market today, most of which are in the U.S. and China.

Figure 1: Data Analytics Concept Map

I. Common Analytical Skills:

Data analytics and data processing is a very large field in its own right, with quite a few analytical methods and techniques. Here are a few of the more commonly used data analysis skills.

Basics of mathematical statistics:

Before data analysis, it is necessary to master certain methods and principles of mathematical statistics. For example, understanding the types of data and the characteristics of various data types. In the process of data processing, do not pass the data type applicable methods are different. Because the purpose of data analysis is to draw certain conclusions with the help of analyzing the data, we also need to understand the meaning behind some of the statistical characteristics of the data. For example, when using IBM SPSS Statistics for correlation analysis of data, we need to understand the meaning of the significance indicators in the analysis results.

Database Principles and Related Knowledge:

When analyzing data, in the vast majority of cases, the amount of data that needs to be processed will not be very small. Especially today, when big data technology is widely used, the use of databases is an indispensable part of the data analysis process. A lot of data analysts do the work from the collation of raw data, to a variety of split summary, and then after the analysis, and finally form a complete analysis report. Even if you don't need to organize the raw data yourself, having knowledge of database principles and related topics, as well as a certain level of coding ability, will definitely add to your work.

Skilled use of Excel, IBM SPSS Statistics and other data analysis software:

In the data analysis, we often need to use computer software to assist, after all, the human brain's data processing ability is far less than the computer program. Excel is the Microsoft Office software Microsoft

Office components. Office is one of the components. As one of the most common office software, we use Excel's function is just the tip of the iceberg.Excel in addition to making ordinary spreadsheets, there are many advanced application scenarios. For example, the pivot function, a set of data through a pivot show to change a lot of different analysis needs to meet the form, so that you can analyze the data from multiple perspectives. But we need to carry out professional statistical analysis, we need to use more professional software to assist, such as IBM

SPSS Statistics.

Second, commonly used analytical software

In addition to the above several commonly used analytical techniques, a good and powerful analytical software also allows data analysts to get twice the result with half the effort. Although Excel is commonly used and powerful, but when we want to analyze a large amount of data for statistical analysis, Excel will seem overwhelmed. That's when we need to use a more professional statistical software - IBM

SPSS Statistics? SPSS is self-evident in the field of data analysis.

With SPSS Statistics, you can:

Analyze and better understand your data through a user-friendly interface to solve complex business and research problems.

Understand large, complex data sets faster with advanced statistical programs that help ensure high-precision and high-quality decision making.

Use extended, Python?and R?programming language code that integrates with open source software.

Select and manage your software more easily with flexible deployment options.

Summary

Mastering data analysis skills and proficiency in using data analysis software are two core competencies of a data analyst. To become a good data analyst, you can start by mastering common data analysis methods as well as SPSS software.