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What this article shares with you is about how to analyze the basic concepts of big data analysis. The editor thinks it is very practical, so I share it with you. I hope you can get something after reading this article. Let's take a look at it with the editor.

With the further convergence of the Internet, the analysis of big data is bound to become the work of a key strategic department.

Just as many things first exist and then become reasonable, data analysts will exist because of the actual needs of some companies, and then the jobs and skills they are engaged in will continue to be enriched and improved.

When it comes to data analysis, Xiao Cheng will think of Sherlock Home. To solve a case, you need to analyze the data.

But as ordinary technicians, readers do not need to be as "smart" as TV characters, they only need to master general knowledge and skills to be competent for the job, and then continue to improve their ability.

Some organizations have defined the skills that data analysts should master according to their own understanding, such as the following picture from the Internet:

This diagram is reasonable, and readers who aspire to be data analysts can refer to the skill requirements mentioned in it.

As the beginning of data analysis, this paper introduces several concepts that are often mentioned in data analysis.

Readers may find the concepts introduced below boring, so it is recommended to skip them.

(1) average

The average refers to the arithmetic mean, that is, the sum of the total divided by the number (or the sum of other units). Average is a frequently used concept, such as "average 2 iPhones per student", "average download speed is 1MB/s", "average monthly cost is 4, 000 yuan".

One drawback of the mean is that when the extreme situation exists, that is, when both the maximum and the minimum are outrageous, the average value becomes unreasonable, which is why the highest and lowest scores may be removed and averaged when voting for the average score.

For an example of this defect, take a look at the following picture from the Internet:

The recruiter tells the reader that the average salary for a job is 1800, but when the reader is an employee, the salary is only 800.

This is also an example of an average fallacy.

Look at another picture:

The income gap of different levels is very big, if collect the income of a number of families, and take the average to represent the general household income, it is unreliable, the rich average the poor.

For this kind of statistics, you can remove the extreme values and then count, or take the proportion of each interval, or use the median or mode described below.

(2) median

The median is the separated value of the size, and the occurrence of a maximum or minimum does not affect the median, so in this extreme case, the median is an available reference value.

For a numerical sequence of odd numbers (sorted), the median is the middle value. For even numbers, the median is the sum of the middle two values divided by 2.

For example: 1, 2, 3, 4, 5, the median is 3.

For example, the median of 1, 2, 3, 4, 5, 6 is (3-4) / 2-3.5.

(3) Mode number

The number is the value that appears the most times. There may not be a single number, or there may be more than one number.

For example: 1, 1, 2, 5, 3, 5, 1 mode is 1.

For example: the modes of 5, 4, 6, 2, 5, 6 are 5 and 6.

The number of people is "everyone is like this", which is of certain reference significance.

(4) absolute number and relative number

The absolute number is a number without comparison, such as the weather is 27 degrees, there are 50 students in a class, the monthly salary is 50,000 yuan, and so on.

The relative number is a ratio, such as a 10% gain, less than half of someone's weight, a ratio of 1:3, and so on.

Simply put, an absolute number is a natural number, while a relative number is generally a percentage (or can be converted into a percentage).

(5) percentage and percentage point

The cost has gone up by 80% and the speed has dropped by 30%. These are all percentages, which is a frequent occurrence.

A point, or a percentage point, is 1%.

Generally, when the range of percentage changes, use percentage points, for example, from 3% to 5%, an increase of 2 percentage points.

(6) proportion and ratio

The proportion of the part in the total is the proportion. For example, the failure rate is 0.01% (accounting for the sum of failure and success), male colleagues account for 70% of all colleagues, and so on.

The ratio is the ratio of various parts, for example, the ratio of female students to male students is 1:3, and so on.

(7) multiple and number

In general, in a rising scenario, use multiples, such as a twofold increase. Percentage is used in declining scenarios, such as a 30% reduction in income, and of course, when it goes up, for example, the number of participants has increased by 300%.

The number, which represents the N power of 2.

A doubling of net income means that it has doubled (to the power of 2, that is, twice as much as before).

Four times means four times (to the second power of 2); three times means eight times, and so on.

(8) compared with the same period of last year

Year-on-year, for comparison, for example, it is now May, compared with May last year, when the number of major failures fell by 30%.

A month-on-month comparison, used of a trend, such as what it is like in the previous week, the previous month, this week or this month.

The above is how to analyze the basic concepts of big data's analysis. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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