For friends who are new to data analysis or preparing to enter the data industry, some of the most frequently mentioned questions are: what skills should be mastered for changing careers/in-depth data analysis, and whether there are some data-based learning resources Demos, etc.
Regarding skills, we will not discuss soft aspects such as communication and coordination and self-learning for the time being. Mainly talk about the general abilities required by the data person, roughly as follows:
Familiar with visualization and BI tools such as Excel, PPT, and Tableau.
Familiar with common data analysis thinking methods, and have a certain business sense.
Understand common statistical methods and machine learning models.
Proficient in SQL (HIve) query, master a kind of Python/R.
After you understand the skills you should master, you can find relevant learning videos, cases, books and other introductory data analysis.
I used to teach people how to fish. Some friends found it troublesome. Today, I cooked the fish for everyone. Here are some data learning resources ( freely available at the end of the article ).
Not much to say, just deliver the dry goods directly. First, all data catalogs are compressed:
For example, the Python resources organized therein, from Python basics to regular expressions, JSON, functional programming, crawlers, and Pythonic programming are all available, as follows:
Of course, there are also actual data analysis projects combined with third-party modules (Numpy, Pandas, etc.):
As mentioned earlier, you need to be familiar with SQL (HIve), and you can also look at this part of the catalog for everyone.
Okay, space limitation. The statistics, Tableau, data analysis thinking/methods and other sections in the learning catalog will not be introduced one by one.
We have put all the data code, case PPT presentation, learning videos have been packaged better.
The method of obtaining is as follows :
1. Scan the QR code below to add friends to receive learning materials
Note: If you are prompted to add frequently, please try again later.