Python heat has been stubbornly high, in addition to the easy to learn, landing to the application level, there are many direction, operation and maintenance, automated testing, back-end development, machine learning ... more ground gas is Python performance in the field of data analysis:
Python data analysis using real case studies
"Life is short, I used Python", all sectors will have a lot of data to be processed, Python has a unique advantage in the field of data processing, call the library matplotlib quickly organize data in a few lines of code and a map:
Matplotlib call the library to quickly organize data in a few lines of code and a map
Write dozens of lines of code can achieve expression package crawling
Said before, time is money, in this day and age, information is money , huge amounts of data are hidden opportunities. Therefore, well-paid data analysts on the market demand is also increasing.
Practitioners accumulated more than 5 years, 30k is more properly the
Python tool that allows us to better focus on the "analysis", if you want to become a very professional data analysts, in fact, is the need for long-term self-driven learning, both have some technical knowledge, but also requires a certain business experience in order to better play value of the data for the actual business empowerment. Life-long learning, you will not progress backward, it is inevitable!
Reproduced in: https: //juejin.im/post/5d087dbb51882570da22072b