"What skills do you need to master in basic warehousing data analysis?"
"How can I find a job in data analysis as soon as possible?"
"What is the future development direction of data analysts?"
What is data analysis?
Data analysis is the general term for "data" work. People engaged in these jobs can find business problems and gain insight into business opportunities by analyzing data, and provide reasonable suggestions and references for business activities, business growth and enterprise development.
Data analysis is mainly about processing data, but data analysis is about analyzing big data. Don't be afraid of this position. Zero-based data analysis is feasible. Three months is enough time to start.
It should be pointed out that:
1. If you are not sensitive to data, or get dizzy and have a headache when you see complicated data, it means that you may not be suitable for this position.
2. At present, data analysis is no longer a full-time skill, but a necessary general skill for people in the workplace. I suggest that everyone in the workplace can learn it, which will give you an advantage in the workplace competition. As for whether you are engaged in data analysis, it depends on your sensitivity to data and your love for this position.
Data analysis post direction and work content
Data analysis can be simply divided into two directions: business and technology:
Business direction-data operation, data analyst, business analysis, user research, growth hacker, data product manager, etc.
Technical direction-data development engineer, data mining engineer, data warehouse engineer, etc.
Most data analysts in business positions are in business departments, whose main tasks are data extraction, supporting relevant reports of various departments, monitoring data anomalies and fluctuations, finding problems and outputting special analysis reports.
In daily work, business departments are often more concerned about why an indicator will fall or rise, what is the user attribute of the product, and how to better complete their KPI.
Taking active indicators as an example, data analysts usually have to solve the following problems:
How much did the index fall? Is it a reasonable range of data fluctuation or sudden? (what)
When did the decline begin? (when)
Is the overall user decline, or some users? (WHO)
What is the reason for the decline? Product update? Or should it be promoted through channels? (why)
How to solve the problem of fading (how)
After a series of steps such as data extraction, data cleaning, multidimensional analysis and cross analysis, you find that the activity of a certain area has decreased, but this cannot be used as the conclusion of analysis. Because the active decline in a certain field is only a phenomenon, not the root cause.
So what data analysts have to solve is, why is the activity in this field declining? Is it a policy factor? Still a competitor? Or the channel problem, these are the places that need in-depth analysis.
After finding the reason, the data analyst also needs to predict the future development trend, output executable improvement strategies according to the current analysis results, and finally push the business department to land, and finally form a closed-loop analysis path.
For data analysts, solving problems is only one aspect. On the other hand, the responsibility of data analysts is to systematize business data and form a set of indicator framework. For example, the active decline is essentially an indicator problem, such as "daily activity" and other indicators.
Some technical positions, such as data mining/algorithm experts, belong to the R&D department, while others set up separate data departments. Compared with data analysts in business direction, data mining engineers have higher requirements for statistical ability and programming skills. Because data mining engineers have higher requirements for tools, the average salary of data mining will be higher than that of data analysts.
Job skill requirements of data analysts
For data analysts in the business direction, mastering tools is only the foundation, but also requires a deep understanding of the business and strong data analysis capabilities.
In the use of tools, data analysts need to master Excel, SQL, PPT, Python and other tools.
Excel is the most commonly used tool in daily work, and you should learn common functions and pivot tables.
SQL is the core tool of data analysis, mainly learning selection, aggregation function and conditional query.
Python focuses on Pandas data structure, Matplotlib library, Pyecharts library and Numpy array.
Regarding tools, it should be noted that different industries have different requirements for tools. For example, the financial industry will need tools such as SAS. Generally speaking, Excel, SQL, PPT and Python can handle most data analysis.
In addition to the use of tools, data analysts should also understand basic statistical knowledge and data analysis methods.
Statistical knowledge: ring comparison, year-on-year comparison, probability distribution, variables, sampling, etc.
Data analysis methods: hypothesis testing, regression analysis, funnel analysis, multidimensional analysis, comparative analysis, etc.
For 0-based partners, I suggest you focus on the thinking and training of data analysis first, read more business data models and data analysis cases, and finally form your own analysis ideas. Don't chew Python from the beginning. You can start with two simple data analysis tools, Exce+SQL. It will be relatively easy to learn Python after you have the SQL foundation.
The growth route of data analysts
Data analysts in the business direction have two development paths.
One is to specialize in business and be promoted to business analyst, strategic analyst or management position. The advantage of business expansion lies in its insight into business networks, which direct data mining does not have.
The other is to improve technical ability and grow into an algorithm expert or data scientist.
How to quickly get started with data analysis
Freshmen want to enter data analysis, so it is suggested to make a study plan first:
Make it clear whether you want to take the business direction or the technical direction.
Fully investigate the industry knowledge in the target field, and understand the industry background and related indicators (in the choice of industry, the best industry field is what you are good at, love and have development prospects).
Understand the commonly used data processing tools, data production processes and data applications in the target industry. Systematic learning of data tools.
0 Basic occupational data analysis, it is suggested to list personal advantages and industry background first to find the best breakthrough:
If you have relevant operation experience and master basic tools, you can learn SQL first, and then start from the data operation post.
If you have product experience and a deep understanding of interaction design and user experience, you can choose a data product manager.
If you have working experience in finance, logistics and other industries, you can borrow the advantages of the industry and transfer to the data analysis position in related industries.
In other words, there is not only one path to switch to data analysis. What we need to do is to find the one that suits us best according to our own background and advantages.
Summary:
As a qualified data analyst, you need to have at least the following three skills:
Necessary SQL, Excel+python\R skills;
Understand the business correctly;
Basic data use awareness and learning ability.
It is important to hone your necessary skills in the process of changing careers, but maintaining a good and positive attitude is also one of the essential elements for successful career change.