Learn data analysis from scratch, to what extent can I find a job, and how to plan my study plan?

First of all, it is important to understand the purpose of numerical analysis. Usually other mathematical disciplines are studied by formulas and theorems, from the study of their definitions, properties, to proof and application. But in reality, especially engineering, physics, chemistry and other specific disciplines.

Often, all you get your hands on is the data you get through experiments. If it is a confirmatory test, it is necessary to return to the formula for analysis and verification. However, it is often more of a research or exploratory test, and there is no specific formula or theorem to replace.

It is necessary to process the data by interpolation, fitting and other calculation methods to obtain a relatively usable general formula. There are also many calculation formulas that are theoretically very complex and not practical in engineering, so they must be converted into polynomial approximations according to the actual situation. These are all tasks of numerical analysis.

When learning numerical analysis, you should not blindly memorize formulas, because business is usually long and tedious. Personally, I think that we should start from the problems that formulas face and what they are used for. For example, the interpolation method is to treat the experimental data as the solution of the formula (like the points on the function image), and from these solutions to derive an approximate formula, which can have local generality.

Another example is fitting, which considers the experimental error on the basis of interpolation, and can reduce the error as much as possible through fitting, and then the purpose is to obtain a general formula with certain conditions.

Numerical analysis is very practical, and together with mathematical modeling, it constitutes two of the most practical disciplines in mathematics, and has a wide range of uses in many neighborhoods such as engineering and economics.

If you want to master real skills and techniques, it is recommended that you master more techniques and more abilities, only in this way can you get more and better. A more comprehensive data analysis ability can help you find a job that suits you faster.

To learn data analysis from scratch, you should learn at least a junior college level, that is, be able to take a college-level exam, and be able to pass, such as a self-exam, correspondence course, etc., get a certificate, and then look for a job.

If you are learning data analysis from scratch, then you must be very proficient in this part before you can work, otherwise you are likely to be asked by others if you are not proficient. So when you can't explain some problems, you can't find a good job, and if you want to find a good job, you still have to learn to be proficient.

Start from scratch, learn data analysis, well, to what extent can you find a job? In fact, data analysis is somewhat mixed with some statistics, well, shadows, if you have the shadow of statistics, then in many cases, it will be quickly mastered.

If you want to master real skills and techniques, it is recommended that you master more techniques and more abilities, only then can you get more and better. A more comprehensive data analysis ability can help you find a job that suits you faster. Starting from scratch, you can find a job by learning data and using your proficiency skills flexibly.

Plan your own study plan, constantly exercise yourself on the job, and accumulate experience.

Starting from scratch, what level of data analysis can you find a job, how to plan a learning plan, in fact, I think that if the company is willing to give you a chance, as long as you are willing to learn to any extent, you can do it, even if you start from scratch. You can also learn and succeed. The most important thing is that you are willing to learn and specialize.

Hello. Self-study is still more difficult, it is best to apply for school, systematically study, and obtain a qualification certificate to engage in related work, data analysts need to be qualified.

If you want to learn data analysis from scratch, and want to find a job at this level, then if you want to plan a study plan, you must need to do a good job in mathematical analysis first, that is, he has a solid foundation, you can buy some ** tutorials, buy some basic books, and then solidify your foundation, and finally I will learn the key points, and then go to the interview to be able to deal with it better.

Let's start from scratch together, and learn that you can get a job with a data analysis certificate. Because this certificate is a sign of your level of learning. So to make a study plan, first of all, what are you going to do by correspondence or to participate in training at a training institution?

Once you're in the learning process, I think it's a matter of going through the program, and you can go better.

Professional functions mastered by big data technicians: Basic stage: Linux, Docker, KVM, MySQL foundation, Oracle foundation, MongoDB, and Redis.

Hadoop MapReduce HDFS Yarn:Hadoop: Hadoop concepts, versions, history, how HDFS works, introduction to YARN, and components.

Big data storage stage: HBase, Hive, and SQOOP Big Data Architecture Design Stage:

Flume distributed, zookeeper, and Kafka Real-time computing of big data: Mahout, Spark, and Storm

Big data collection stage: Python and Scala. Big Data Business Combat Stage:

Operate enterprise big data processing business scenarios, analyze requirements, implement solutions, and apply comprehensive technologies in practice.

If the unit does not require much, then generally we can find a job as long as we can master a little simple data analysis, so that we can find a job, but it should be noted that if you want to find a good job, the salary is relatively high, then try our best to analyze the data, such ability must be learned more solidly, and relatively speaking, there must be some special ways and methods of data processing and analysis, so as to find a better job, and for all the relevant aspects of knowledge, do a good job of learning programs, and plan their own work.

With the advent of the information age of knowledge, the depreciation rate of knowledge is getting higher and higher, and while the things that individuals learn are increasing, the things they have learned are also accelerating their obsolescence, which leads to more and more pressure on learning. Therefore, a good study plan will be the basis for personal competitiveness and improvement. And whether it is a student or a working person, they need to keep learning, enrich themselves, and avoid being overtaken by competitors.

The survival of the fittest effect in society allows each of us to become a lifelong learner.

In fact, it is very difficult for a student to engage in data analysis, and there is a lot of mathematical knowledge, very complex, not ordinary people can learn, so you can do mathematical analysis and linear algebra very clearly, and you can only engage in data analysis in the future, only to this extent you can, so how to plan is planned by yourself in your own slow learning.

He has worked in the front line of data analysis for many years. I have also guided the construction of the data department and trained the data analysis product manager, and I will answer this question from the perspective of business analysis.

Business data analytics is not mysterious, just like getting started in any other field of work.

On paper, I finally feel shallow, and I never know that this matter has to be done, and there is only one important thing, do more hands, and practice.

I'm against introductory learning, a lot of knowledge, theories, diagrams.

It's easy to get started with data analysis and data operations, as long as you start with a case study and go through the process to get the results, you can naturally get familiar with it.

Data analysis methods are not like being a product manager or doing programming, which involves a wide range of knowledge and requires a long time to practice.

It is not difficult to master the core method. Just do more exercise in the actual project.

There is no need to worry about mathematics, basic and advanced business analysis is generally sufficient for primary mathematics, and does not require advanced mathematical knowledge and theory.

In short, practice is the most important.

From the recent epidemic as an example, let's talk about a few basic methods, and advanced business analysis These are also basic and important analysis methods.

You can try to start with a case and do a little exercise.

Learning from scratch depends on the level of the job you are looking for, because you can continue to study while participating in the job, so as soon as you get started, you will gradually become more and more proficient according to the needs of the job.

If you want to master real skills and techniques, it is recommended that you master more skills and more abilities, only in this way can you get more and better. A more comprehensive data analysis ability can help you find a job that suits you faster. Starting from scratch, you can find a job by learning data and using your proficiency skills flexibly.

Plan your own study plan, constantly exercise yourself on the job, and accumulate experience.

Nowadays, big data is used in all aspects of life, and the demand for data analyst positions is becoming more and more popular. What are the development directions of data analysis, and a brief introduction to Gami Valley Big Data:

1. Business Analysis: Statistics, Business Analyst, Market Analyst;

2. Computing direction: data mining, modeling engineer, algorithm engineer;

3. Management direction: data product manager, project manager.

At present, the most demanded in the market is the first type of employment direction, business analysis. For example, the loss of users, automatic classification of users, etc. To become a senior data analyst, on the one hand, it is necessary to strengthen the understanding of the business, and it is best to help the company make decisions through data analysis; On the other hand, it is necessary to strengthen the professional knowledge learning of machine learning data mining and turn machine learning into a means of data analysis.

Professional functions mastered by big data technicians:

Basic stage: Linux, Docker, KVM, MySQL basics, Oracle basics, MongoDB, and Redis

Hadoop MapReduce HDFS Yarn:Hadoop: Hadoop concepts, versions, history, how HDFS works, introduction to YARN, and components.

Big data storage stage: HBase, Hive, and SQOOP

Big data architecture design: Flume distributed, ZooKeeper, and Kafka

Real-time computing of big data: Mahout, Spark, and Storm

Big data collection stage: Python and Scala.

Big data business practical stage: practice the business scenarios of enterprise big data processing, analyze requirements, implement solutions, and apply comprehensive technologies in practice.

1. To what extent can I find a job after learning data analysis?

This question depends on the company you're interviewing with, and the specifics vary widely. Therefore, I can only summarize some suggestions from my own real experience and give some of the most basic knowledge structures for the landlord's reference.

First of all, I think a good data analyst should have three qualities:

data analysis skills;

understanding of the business;

Unique analytical thinking and expression;

Of course, as long as you have basic data analysis skills, you can try to find a job, and you can gradually cultivate and improve the latter two qualities in your work.

First of all, we need to understand the conceptual difference between big data analysis and traditional data analysis, compared with traditional data analysis, big data analysis requires more skills, and the ability requirements for practitioners are improved. However, the learning threshold for big data analysis is not too high, the learning difficulty is moderate, and many people can learn big data analysis.

It is best to learn to be proficient, and then you can practice on the basis of theory.

I think that to learn data analysis, first, we must lay a solid foundation in order to stand above the profession and not be looked down upon; Second, according to the recruitment skills requirements of the enterprise, there is a target. To this end, the following suggestions are given in the hope that they can help you.

1) Basic operations of SQL database, basic data management (2) Ability to use Excel SQL for basic data analysis and display (3) Ability to use scripting language for data analysis, Python or R (4) Ability to obtain external data, such as crawlers.

5) Basic data visualization skills, able to write data reports (6) Familiar with common data mining algorithms: regression analysis, decision trees, random forests, support vector machines, etc.

The easiest: Excel is easy to use, and there will be at least one other analysis tool, master the basic data analysis methods, and be able to know that the data comes from.

Understand every knowledge point, know some simple data analysis, and then combine theory and practice in your work.

If you want to learn, you can consider the Buckle Ding Academy, the learning content is set according to the needs of the enterprise, so that you can get started quickly and find employment easily.