1. What the hell is data analysis?
In fact, data analysis is not that mysterious, because everyone experiences data analysis every day in their lives. For example, if you wake up late in the morning because the alarm clock does not ring, you need to analyze how to go to the company so that you will not be late. Of course, in addition to this case, there are many other cases in life that also show that data analysis has always run through our lives. For example, students who have bookkeeping behaviors will use their previous bookkeeping records at regular intervals. Analyze where you spent your money last month, and then you will exercise restraint in certain areas next month and so on.
So what exactly is data analysis? In fact, we can see some common points from the above. First, we need to have some data, whether it is historical, current, or future; second, we will compare or reason based on the analysis of these data, and then draw some in conclusion.
Therefore, data analysis needs to collect data, extract useful data from it, and then use reasonable methods to compare or reason, and summarize the corresponding conclusions.
Having said so much, it is obvious that data analysis is definitely not for fun, and obviously not for showing off skills. After all, mere data analysis is not a skill to show off. What is the purpose of data analysis, combined with some of the above cases, it can be roughly seen that data analysis is for us to draw some conclusions, and then to help us make corresponding decisions.
Just like the case above, whether it is the conclusion that we are late for work based on historical experience, or that we reasonably control the consumption of this month based on the consumption situation of the previous month, all are the conclusions drawn after data analysis. To help us make decisions.
In the actual product work, the purpose of data analysis is similar in the final analysis, which is to draw certain conclusions through analysis to help make subsequent decisions in the work.
Due to the different roles and data time dimensions of data analysis and the different data indicators of interest, there will be some different objectives for measurement. From the perspective of roles, there can be products, operations, markets, etc. From the time dimension of data, there can be history Data, current data, and future data. From the perspective of data indicators, there can be daily activity, monthly activity, order conversion rate, access duration, etc., and according to the permutation and combination of different roles and data time dimensions, some different data analysis can be obtained Purposes, such as e-commerce products to analyze future sales trends based on historical sales data, and to analyze the reasons for data changes through past data trends and try to find solutions, and so on.
2. Skills to be mastered for entering the bank data analysis
At present, the data analysis industry is under fire. The domestic demand for data analysis talents is constantly expanding, and the salary of data analysts is also considerable. Many people want to seize the opportunity to successfully enter the industry and raise their salary.
If you want to enter the data analysis and find a high-paying job, you can refer to the recruitment requirements of Zhongyi company on the recruitment website and improve the corresponding ability according to the requirements. After referring to the recruitment requirements of several recruitment websites for data analysts, the editor summed up the following abilities that must be possessed in data analysis for entry into the industry. Small partners who want to enter the industry can refer to it.
1. Business understanding ability. As a data analyst of an enterprise, you must deeply understand the positioning of business and products, as well as business logic and business dynamics, clarify the purpose of data analysis, and use which data to display the results to drive business growth in the next step Foreshadowing;
2. Data analysis tools
Excellent personal skills, understand data collection, data cleaning, and data analysis. Don't think that you can only use Excel. Commonly used data query and visualization tools such as SQL, Python, PowerBI, and Tableau are all necessary weapons. To improve your ability to deal with complex business scenarios, you can always use one more.
3. Master data analysis methods and models.
User data operation model, user churn prediction model, hybrid matrix model, Kano model, funnel model, index system construction, exploratory analysis... These are the green channels from mediocrity to excellence, and you have cleared the customs ?
4. Have data awareness.
Use all kinds of advanced visualization charts to explain business scenarios and data logic, can easily tell stories through data, and catch the boss's eyeballs in order to gain greater voice and promotion opportunities in the company.
5. Thinking ability. Although data analysts are accustomed to using data to prove, they must not be understood as just a simple data display. You need to think more about why the analysis and analysis are correct, whether there are other data, and the problem of data display is What, what is the logic behind the problem, etc.
6. Communication skills. As a data analyst, you should not only deal with data, but also have excellent communication skills with people and other departments.
In addition to the above six points, the editor believes that the most important thing for entering data analysis is to be truly interested in data analysis, rather than just blindly following the trend, seeing that data analysis is a hot industry and blindly entering the industry.
3. How do zero-based scholars get started?
(1) Statistics related knowledge
Statistics is the basis of data analysis, because data analysis requires statistical analysis of a large amount of data. You can cultivate some of the most basic logical thinking of data analysis through the study of statistics.
(2) Excel is
the first-level data analysis tool. When the amount of data processed is not very large, Excel is completely competent. The focus should be to strengthen the learning of various functions and Excel data visualization.
(3) Programming language
data analysis requires a lot of tools, such as python, SQL, etc., which require strong code knowledge to support. Friends who want to learn data analysis can have a preliminary understanding of the code before learning, so as not to Really learn at a loss.
4. Talk about data analysis positions
First of all, I have to admit that in fact, most data analysts are migrant workers, and many jobs only need to understand Excel and SQL to start working.
And now many off-the-shelf analysis tools are already very useful, and many companies use Power BI, Tableau, and Qlik Sense (visualization) for data analysis.
Isn't that popular with data analysts?
Fragrant data analyst, so sweet! Even companies large and small are hiring data analysts, and the salaries given are higher than one by one.
So if you want to increase your salary, there is still a great opportunity!
But in these high-paying job recruitments, without exception, two words are emphasized-business, which tells us a truth for data analysts:
The so-called data analysis capabilities should not be just about obtaining data, cleaning data, data modeling, data statistics and other operation methods.
Proficiency in the use of tools is only a basic skill. The hard part is knowing what to use these tools to analyze; or how to solve business problems after analysis, and how to quickly obtain commercial value.
Therefore, for every data analyst, understanding business has become one of the most promising and most potential abilities for contemporary data analysis posts.
For example, one day after get off work, the boss said to you: "I want to see the sales of the stores in this city this week, and give me the results in a week."
[Most data analysts]:
Retrieve data from modules such as store sales records and cost management, export the data, and use Excel or python tools to create data visualization charts;
add two sentences to the report to interpret the data: 100000 products sold Pieces, revenue 7 million, net profit 1.8 million.
[Data analyst who understands business]:
First study the company's development strategy this year and the boss's recent business adjustment strategy, and learn that the boss recently wants to reduce the operating cost of the store.
Retrieve employee attendance, cargo storage records and other data, use comparative analysis to find outliers, and match specific business scenarios, and draw the conclusion:
Store A is in the off-season in the second quarter and should reduce personnel input by 30%, and the use of cold storage in B storage The rate is lower than the average, and the cold storage storage structure should be adjusted...Through these measures, the overall cost is expected to be reduced by 15%.
We all know that making management think that data analysis is useful is actually a very difficult thing. They think you just hold a few pages of data analysis reports. Why do you challenge the acumen that they have accumulated over the years?
But data analysts who understand business are different. Only by understanding the business can you take the initiative and make yourself no longer passive.
In fact, as early as 2017, Jack Ma once said: "In the future, all businesses will be digitized and all data will be commercialized, and enterprises will have a better way out."
Today, 4 years later, the data has covered every scene in our work.
In the digital age, data analysts who understand business will gain high salaries, voice, irreplaceability, and sense of accomplishment at the same time.
If you don't want to get to the edge of being optimized, if you don't want to enter a bottleneck period before you play your strengths, if you don't want to stop in your comfort zone, start improving your business capabilities as soon as possible.
Finally, the editor wants to add that although data analysis is an essential skill for future professionals, all those who want to enter the industry still need to consider their future development direction. Generally speaking, there are two directions in the data analysis industry. The first is the technical direction. Specific occupations include data mining engineer, data modeling engineer, data algorithm engineer, and so on. The second direction is the business direction. Specific occupations include data product managers, project managers, etc., through data analysis to assist the company in decision-making and achieve business growth. If you are a small partner who chooses data analysis in a technical direction, you will have more technical knowledge to learn. In terms of business, you need to have an in-depth understanding of related industries.