I am currently self-taught Statistics, JAVA, R, SQL, SPSS. But they are all at the academic level. Now I am a bit confused. There are so many tools for data analysis. There are two main issues I'm thinking about right now: In the short term, I hope to find a related job (the current career is messed up). The long-term plan is to be a data scientist and don't want to go astray. text: Also switched to data science because of love. After graduation, I have been paying attention to the news of artificial intelligence in my spare time, and started to learn related theoretical knowledge and tools such as statistics and python in the evening out of interest. Suddenly one day, on a whim, I suddenly figured out why I didn’t devote myself to the industry I love, or else I just have a passion.
So I started to learn Python non-stop, and decided to start with the general basic ability in the era of artificial intelligence - data analysis. However, because it is a zero-based career change, many of them are not good at the beginning of the job. The mobile number list most scolded is that the plans made do not come true, and the proposed plans are out of business scenarios... After going through many projects and accumulating experience, I have concluded that the following can help to your experience. 1. Tools - Hard Power Having learned so many tools from the subject, I must be a "tool control" like me, and I can't help but learn when I come into contact with interesting and powerful tools. Now, as a data analyst, I am using all the tools mentioned by the topic.
The application scenarios of some related tools I am currently using: 1. Python A commonly used data analysis tool, a star product in the data science community. It is an almost universal tool, especially in solving repetitive tasks, big data analysis and other scenarios. In the days when Python was included in the elementary school curriculum, it was definitely a programming language that was worth the price of admission. 2. SQL The general database language, for data analysts, can complete the work of fetching and analyzing. The so-called clever woman is hard to cook without rice, SQL can solve the problem of no data from the source, otherwise you can't imagine how IT refuses or delays your request for data withdrawal. 3. SPSS The "fool-style" data analysis graphics software can easily complete complex data analysis tasks, such as correlation analysis, regression modeling, etc., just like operating Excel.