Nowadays, the competition in the workplace is getting more and more intense, do not learn a new skill or two, to keep their own knowledge up to date, it is very easy to be surpassed by the younger generation. Some people choose to learn a foreign language, others choose to learn the ability to deal with people in the workplace.
If your job requires you to work with data, trust me, Python will definitely be the knock-out brick for your promotion and salary increase. Why? Because of the efficiency. Let's take a look at a job posting for a senior data analyst with an annual salary of 24w-48w, the following 4 competencies are more important to employers:
Combine them more carefully and you'll find that even if you're not a data analyst, you'll be able to score points for yourself by having these 4 competencies in the workplace. Imagine, an e-commerce promotion end review, others spend a lot of time combing data, and you have more energy to analyze and locate the problem, but also to make a better looking interaction chart. Business analysis, you pull a lot of data, manually tagging to do charts, are not as efficient as a few lines of Python code. Let's analyze it line by line.
1, business insight and execution business insight and execution, to put it plainly, is how to get effective information from the massive amount of information.
Python can use MySQLdb library to connect to the database, you can use pandas and matplotlib for cleaning and analysis, you can use pyecharts for interactive visualization, you can use numpy and sklearn for modeling, and even pyinstaller can be used to package the workflow to a colleague. ***The same efficiency ......
Call matplotlib library with a few lines of code to quickly organize the data and come up with graphs
When the tool is more efficient, there is more time to y understand and analyze the business.
2. Communication power
Python can also improve communication power?
Data analysts belong to the business end of the work, long-term exposure to the company's projects and customer needs. On the technical side, they generally only care about the implementation of product features. An analyst who masters Python will have a better understanding of where the pain points are on both the business and technical sides.
3, Python and SQL
and huge data to deal with, only Excel is not economical, so most of the data analyst jobs require SQL skills.
SQL is easy to get started with, and after mastering accessing data and basic data cleaning functions, you can get down to business. Junior analysts may fetch numbers locally and then analyze them, while efficient data analysts use Python to connect to databases for analysis, making the workflow more efficient.
Using the Python tool library pymongo for database document query
4. Initiative and logicInitiative and logic is a metaphysics, people in the workplace will say that they have initiative, but the question is how can the boss feel your initiative? For example ......
In the conversion rate data is low when the data is quickly accessed to find the reason, and even use Python to write an automatic early warning script, accurately expressed to the front-line business staff, rather than in the boss asked you only when you say "I think "; in the company's new business has not yet been formed when the use of Python to collect and organize effective data, to establish a visual indicator system, to guide the business, rather than when the boss asked you to say "I think"; initiative to learn, and take the initiative to find new ways to improve efficiency in the solidified data workflow, such as finding that colleagues are still copying and pasting data to find the cause. For example, if you find that your colleagues are still copying and pasting and repeating their work, you can use Python to help them write a script to merge files. This detail, although the boss will not ask, but initiative and logic is because a person has a strong ability to show.
Using Python to write a small tool, a few minutes to complete the merger of 912 Excel tables
Summary, in order to be a "senior" data analyst, it is impossible to eat the old money all the time. The only way to get to the top is to keep learning and thinking.