How difficult is it to be a data analyst in elementary school?

The following is an analysis of the life course and career data of a liberal arts student, Xiaobai, to share with you. I believe I can help some friends who are at the crossroads of life or people who are in a period of confusion and swing.

1, you must think twice before choosing the path of data analyst. Although this road looks glamorous (at least professional wages are not much better than other industries), it is also a difficult road, full of unknowns, thorns and confusion. Especially for me, a liberal arts student, my efforts are several times that of ordinary science and engineering men.

2. Although the industry of data analysis has a natural professional contempt chain (there are real differences in the logical thinking ability of arts and sciences, the acceptance of programming languages, and the basis of mathematical statistics, which is also an important reason why Party A trusts the background of science and engineering more, because schools specializing in social sciences or literature and art rarely make students' curriculum training plans in strict accordance with mathematical logic), it does not mean that arts students have no chance, because before college, in fact, we have never been formally exposed to programming or statistics. Therefore, friends, interests and decisions of liberal arts majors are also important factors, and we can't deny ourselves only by objective professional background.

3. If you want to choose this road firmly, you must overcome all kinds of dependencies, such as installing an R language or Python software, drawing objective conclusions from huge data, and analyzing the value of data with what you have learned. You must use your hands and brains in actual combat, instead of relying solely on the previous liberal arts thinking (paying more attention to the creation of thinking and the development of personality), rational thinking and objective science are more important. Because this study habit determines that you are bound to be left behind by like-minded people, Baidu, Google and Stack Overflow will always open their doors to you for free;

4. Hands-on practice and internship participation in projects are the beginning of good data science or data analysis. Only by learning fake tricks instead of practicing them can we see how much of what you have learned can be used to enhance business value.

5. Before applying for a job, if time permits, the three levels of R language, Python (data science related module) and SQL (you can choose a platform, such as MySQL) are a little earlier. If you don't want to work overtime every day;

6. If you are still a student at school, you should learn to prioritize all kinds of things, such as boring lectures, gift-giving marketing brainwashing classes, and various ineffective social activities. If you spend all your time on the study of data analysis, you will find that you have much more time, and naturally you can catch up with your peers earlier.

7, down-to-earth walk your own way, don't write more, read more, ask more questions (ask really valuable questions), summarize more, communicate more, and give yourself enough career change cycle (if you are a statistics, math, computer, it may be smooth, but you can't take it lightly, if not, please be sure to choose carefully and at least give yourself a specific career change buffer period of one to two years. )

8. Learn to integrate knowledge in different fields, draw inferences from others, and learn lateral transfer. Only in this way can you feel more transparent when studying, otherwise it will only increase the thickness of the notebook and only increase your troubles.

In fact, all liberal arts students are happy and entangled when studying data analysis or zero-based career change, but at any time node, if they have been stagnant and hesitant, then all the opportunities that can or may be missed. Fortunately, although I am in a muddle, I have also cut through the thorns all the way, but time waits for no one, and there will be gains in the end! I wish all liberal arts students who want to enter the data analysis industry or change careers all the best.