What Data Analysis Tools Should I Learn to Start a Career as a Data Analyst?
For data analysis, I have always emphasized that the core is the business. We associate the analysis logic of the business with the processing logic of data analysis, and data analysis tools are the means to help us achieve results. Just as we choose different vehicles according to different roads, the right tools can help us reach the end faster. We should choose different tools for different links of data analysis.
In the enterprise, data analysts are often divided into two categories: business analysts and technical analysts. And the capabilities and work content of the two are quite different, their requirements for tools are also differ accordingly.
Business analysts often work in the marketing department, sales department, etc. The daily work is more about sorting out business reports, doing special analysis for specific businesses, and measuring data and developing plans around business growth.
Technical analysts generally belong to the IT department or data center. According to different work links, they are divided into database engineers, ETL engineers, crawler engineers, algorithm engineers, and so on. In small and medium sized enterprises, these processes are often handled by one technical analyst. In large enterprises, a standard data center needs a data warehouse, special analysis, modeling analysis, and other groups to complete the data development work. The reason for this distinction is that a multi-level complex data system requires a combination of data collection, data integration, database management, data algorithm development, and report design.
The reason for this distinction is that we need a multi-level complex data system to deal with data. A data system requires a combination of data collection, data integration, database management, data algorithm development and report design. In this way, we can gather the bits and pieces of data scattered around, set common indicators, and make all kinds of cool charts. Every link here requires corresponding technical support and personnel work, so there are different positions.
When you are looking for a data analysis position, you must distinguish whether it is on the technical or business side, and whether it matches your own professional inclination.
Analysts have the distinction between technology and business, and the corresponding data analysis tools also have such attributes.
For junior data analysts, mastering Excel is a must. You must be proficient in PivotTables and formulas. And using VBA will be a plus. In addition, you must learn a statistical analysis tool. SPSS is better for beginners.
For senior data analysts, the use of analytics tools is a core competency. VBA is a basic necessity. And you have to master at least one of the three analysis tools: SPSS, SAS, and R. You can also learn other tools such as Matlab, but it depends.
For data mining engineers, R and Python are necessary, as you have to write code.
For junior data analysts, you only need to write SQL queries.You can also learn some Hadoop and Hive queries.
For senior data analysts, in addition to SQL, learning Python is necessary to get and process data with less effort. Of course, other programming languages are also alternatives.
For data mining engineers, you have to use Hadoop, Shell, Python, Java, C++, etc. In short, knowing a programming language is definitely the core competence of data mining engineers.
The following image illustrates the attributes and functions of data analysis tools.
The use of data analysis tools depends on the needs and environment of the business. Why do data analysts in small companies just use Excel for reporting, and analytics for large companies require Python and R? It depends on the data architecture of the enterprise.
From the perspective of IT, tools can be divided into two dimensions in practical applications.
