In today's hot data analysis industry, more and more people have mastered the relevant knowledge of data analysis, and in data analysis work, we need to master some skills to better improve the efficiency of data analysis work and avoid detours . So do you know what skills data analysts should know? Below we will introduce to you the skills that data analysts should know.
choose the right role
There are many different roles in the data science industry. For example: data visualization experts, machine learning experts, data scientists, data engineers, etc. Depending on background and work experience, it may be easier to get into one role than another. For example, if you are a software developer, it is not very difficult for you to become a data engineer. So, if you don't know what role you want to be, how to start and how to improve your skills will be more difficult for you.
What if you don't know the difference or you're not sure what you could be?
Here are a few of my suggestions:
Learn from people in the industry what each role entails.
Ask others for advice. Ask them for a moment and ask relevant questions, I am sure no one will turn away a person in need.
Find out what you want and what you're good at, and choose roles that fit your field of study.
One thing to keep in mind when choosing a role: don't just jump into a role blindly. You should first have a clear understanding of what the field requires and prepare for it.
Pick a tool/language and stick with it
Generally speaking, we need to choose a suitable tool so that we can do a good job in data analysis. Facing the characteristics of high data volume, multi-dimensionality and heterogeneity, and the expansion of analysis methods, traditional statistical tools It's been hard to deal with.
Many new software analysis tools serve as an important boost for in-depth big data insight research, and have also become the knowledge and skills that data scientists must master. However, the complexity of the reality determines that there is no ultimate tool to solve all problems.
In the actual research process, it is necessary to flexibly select the most appropriate tool according to the actual situation in order to better complete research and exploration. In traditional analysis and business statistics, we have three tools, namely Excel, SPSS, and SAS. These three tools are not new to researchers. Learning these three tools well is equivalent to taking the first step towards becoming a data analyst.
study a course in its entirety
For junior data analysts, they can write SQL queries, and if necessary, write Hadoop and Hive queries, which is basically OK.
For advanced data analysts, in addition to SQL, it is necessary to learn Python, which is used to obtain and process data with twice the result with half the effort. Of course other programming languages are also possible.
For data mining engineers, they must be familiar with Hadoop, at least one of Python/Java/C++, and be able to use Shell... In short, programming language is definitely the core competence of data mining engineers.
business understanding
It is not an exaggeration to say that business understanding is the basis of all the work of data analysts. The data acquisition plan, the selection of indicators, and even the insight into the final conclusion all depend on the data analyst's understanding of the business itself.
For junior data analysts, the main job is to extract data and make some simple charts, as well as a small amount of insights and conclusions, as long as they have a basic understanding of the business.
For senior data analysts, they need to have a more in-depth understanding of the business, and be able to extract effective opinions based on data, which can be helpful to the actual business.
For data mining engineers, it is enough to have a basic understanding of the business, and the focus still needs to be on giving full play to their technical capabilities.
Business ability is a must for an excellent data analyst. If you are very familiar with a certain industry before, it is very correct to learn data analysis. Just graduated without industry experience can also be cultivated slowly, no need to worry.
fast learning
Regardless of the direction of data analysis, primary or advanced, you need to have the ability to learn quickly, learn business logic, learn industry knowledge, learn technical tools, learn analysis framework... There is endless content in the field of data analysis, and everyone is needed Have a heart that never forgets to learn.
Quick learning is very important. Only by entering this industry quickly can we seize the opportunity and gain more experience and opportunities.
As can be seen from the figure, Python is very versatile in data analysis, and Python can be used in all stages of the process. So as a data analyst, if you need to learn a programming language, Python is strongly recommended~
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