I don't know when we have entered the era of "everyone must know a little data analysis".

With the gradual application of big data technology, mastering data analysis has become a necessary skill for the contemporary workplace!

So as a liberal arts business student, how should I learn data analytics well and arm myself with data analytics skills to achieve a bend in the career development?

Q: Hello teacher, I am a liberal arts major, what do you think is the help of data analysis for liberal arts students?

A: In fact, we often encounter this problem, and now I also found that more and more university majors are in the liberal arts.

Students who used to do positions like administration and human resources are also learning data analysis, and many of them even have 8 to 10 years of working experience in their positions, and they are interested in this matter of data analysis.

First of all, it is still the same, I have been talking about.

The essence of data analysis is an analytical ability, and the analytical ability is actually a kind of workplace underlying ability.

Analytical ability, frankly speaking, is that you analyze a problem, and then to come up with a result, to provide a solution.

This thing actually and your occupation, professional, gender, etc. are not necessarily related. This is an underlying ability that I think all of us should master.

Q: But as a liberal arts student, sometimes I feel that data analysis involves a lot of knowledge, so how should I learn it?

A: This is also a problem that I often encounter.

Yesterday, I actually shared the answer to a former executive mom. Here also share it with you.

In fact, we can think of the learning of data analysis for liberal arts students as the process of making burgers.

For liberal arts students, the feedback I've heard is that technical skills and data cleaning are the two more challenging aspects.

Because not many liberal arts students have learned these two aspects, and frankly speaking, to compete with students in science and technology, you will definitely not be able to compete, so we have to make some trade-offs.

Just like making burgers, whether a burger is delicious depends more on its burger filling or the top and bottom buns?

It's definitely the burger filling.

So like the example I just gave, the two aspects of technology and data cleaning, in fact, is particularly like the top and bottom two layers of bread in the process of making a burger, they are definitely needed, and you will find that these two parts of making a burger may sometimes be ready-made.

So you don't have to put an inordinate amount of effort into making these two slices of bread as perfect as you can, but rather have the form. You can have a basic mastery of the technique, a basic mastery of the ability to clean the data and that's it.

Q: In this example of making burgers, what do you think is the filling of burgers from the perspective of data analysis?

I think the most important thing is 4 words, which we call "Begin with the end in mind". Specifically, these 6 words are called "find the goal and push the path".

To be more specific, when you learn data analysis, you must think of a problem, what is my goal? To turn this kind of thing into a habit for you.

And then from the goal to work backwards in order to accomplish this goal, I need what data, how to clean these data to organize, summarize, and then to consider other factors, such as variables and so on.

For liberal arts students you must remember that the key to learning to do data analysis of this burger is to find the goal to push the path of this idea and method. Because the filling is the key to determine whether the burger is delicious!