10 Key Skills That Data Analysts Need to Master | 7wData

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Every single day, we answer questions about data analyst skills. Some people ask me, “I only know how to use Excel and make analysis charts since I started. To me, data analyst seems to be an analyst of business data, and I don’t know how to improve myself.”

This is because he did not fully dig out the value of big data analysis. Data analysis is to optimize products, marketing strategies, and operation strategies through discovering the data. Only knowing the business is not enough, the more important is to master various skills of data analysis. Based on my years of experience, I summarized ten skills that a qualified and senior data analyst must master.

As we all know, statistics is the cornerstone of data analysis. Statistics, of course, is also a core skill of data analysts. After you study statistics, you will find that in many cases, the analysis is not so accurate. For example, many people like to use the average to analyze the results, but this is often rough. Statistics can help us look at the data more scientifically and gradually get closer to the “truth” behind the data.

Systematic learning is the key point to learn statistics well. Pure machine learning emphasizes the predictive ability and implementation of algorithms, but statistics have always emphasized interpretability. You need to understand the principles behind the data. The following statistical methods are all you can learn.

Data visualization is mainly realized through two types of tools: programming and non-programming. For data analysts in the general industry, it is not necessary to master the visual tools of programming. I will recommend the following tools:

Excel is a common data display tool. For data analysts, skills need to master in Excel includes being proficient in presenting data in Excel charts and knowing how to set formats for a series of generated charts.

Learning excel is a gradual process. For example, beautify series format, enhance 3d format, set the axis and grid.

Charts can be used in conjunction with functions or macros to produce analog charts or advanced charts with interactive effects, such as population distribution of cities and counties on a map, for better data analysis and viewing.

The data analysis function in Excel can largely complete the data analysis of professional statistical software (R, SPSS, SAS, Matlab), including descriptive statistics, correlation coefficient, probability distribution, mean estimation, linear, nonlinear regression, multiple regression analysis, time series, and other contents. 

Familiarity with the various features of Excel is essential for a good data analyst.

Data visualization tools like D3.js, HighCharts, Tableau and PowerBI all have their advantages. You must be proficient in at least one visualization tool. If you want to know which tools you can learn, you can read this article 9 Data VisualizationToolsThat You Cannot Miss in 2019or  Compare 6 Types and 14 Data Visualization Tools

In my case, I often use FineReport at work. As a reporting and data visualization software, FineReport has two core functions: data entry and data display. However, I think it is quite surprising that it has a large number of built-in charts and visualized dynamic effects. It can make a variety of dashboards in various formats, and even a large screen like TV dashboards.

Those who have learned Python data analysis know that there are many excellent third-party libraries in visual tools, such as matplotlib, seaborn, plotly, Boken, pyecharts, etc. These visual libraries have their advantages and are widely used in practical applications. Mastering Python will be the right choice as data analyst skills.