How to learn data analysis in your spare time, how to learn data analysis in your spare time?

If you want to use your spare time to learn big data, you can choose from three learning methods: one is self-study, of course, you need to have some foundation and good learning ability and self-control. One is to enroll in a class to study, which is the fastest to learn but requires teaching fees.

Data analyst is one of the hottest professions in today's big data era, and many fresh graduates and even elderly professionals who have worked for many years want to transform into data analysis. In this way, data analysis training has quietly risen in Beijing, Shanghai, Guangzhou, Shenzhen and other places, which shows how popular data analysis is at present, after all, businessmen are driven by interests, if there is no such a strong interest channel, why is there so much data analysis training?

So, as a general audience, if we don't participate in the training, how should we learn data analysis in our spare time? In my opinion, the study of data analysis should start from two places: operation and thinking.

First of all, in terms of operation, you should learn some basic software for data analysis step by step. From the most basic Excel, to the database calling tool SQL, to the fool tool SPSS that can do data mining, and even the hot Python now. These tools, from the most basic Excel to the technical Python, require continuous learning to understand how these software cleans, processes and presents data.

Of course, in terms of data visualization, you also need to learn some tools such as echarts, tableau, and powerbi.

In addition to operation, thinking training is also an indispensable module for us. If you just stay in the basic operation of data, then you are only suitable to be a cousin, and the company will let you do more cousin work. Therefore, in addition to this operation, we must also learn a certain amount of business knowledge, know how to conduct business analysis, and understand and deepen some data business models.

This way, with a two-pronged approach, your data analysis can be taken to the next level. It's important to remember that data analytics isn't just about playing with data, it's more about generating business value with data.

Data is becoming more and more common, from everyone's social circles, consumer networks, and sports to their own devices. It is as large as the company's sales operation, planning and decision-making, product production, and transportation network. More and more people are switching careers to data analytics.

But for working people, it can be very stressful to completely give up their current job to devote themselves to learning data analysis, after all, the cost of living is there. Therefore, it is also a stopgap measure to learn data analysis in your spare time as a preparation for your future career promotion or career change. However, how to learn data analysis well in your spare time is also something worth thinking about and planning.

It's important to understand what the foundational skills of a data analyst are needed. That's how we can have a purpose. I can summarize it as follows:

1) Basic operations of SQL databases, basic data management.

2) Able to use Excel SQL for basic data analysis and display (3) Able to use scripting language for data analysis, Python or R

4) Have the ability to obtain external data, such as crawlers.

5) Basic data visualization skills and ability to write data reports.

6) Familiar with commonly used data mining algorithms: regression analysis, decision trees, random forests, support vector machines, etc.

After that, how to arrange your spare time is up to the individual. In general, the basics are learned first, then the theory, and finally the tools. Basically, every language is learned in this order.

1. Learn the basic knowledge of data analysis, including probability theory and mathematical statistics. The foundation still needs to be mastered, the foundation is not yet solid, and the knowledge edifice is very easy to collapse.

2. Relevant theoretical knowledge of your target industry. For example, in finance, you must learn various knowledge such as **, banking, finance, etc., otherwise you will be confused when you arrive at the company.

3. Learn data analysis tools, software combined with the practical application of cases, and the mainstream software for data analysis is (from easy to difficult): excel, spss, stata, r, python, sas, etc.

4. Learn how to operate these software, and then use the software to process and analyze the data step by step, and finally output the results, test and interpret the data.

I give a few pieces of advice based on my experience that I hope will be accepted.

First of all, you must read books, you must find a good book, study and read carefully, and you must lay a good foundation at the beginning! , mainly learn python. So how do you learn?

Data Analysis in Simple Terms" is to learn the most basic statistics, probability and other theoretical knowledge, these knowledge is the cornerstone of your progress, and this book continues the consistent style of the head first series, very interesting, reading will not feel boring!

The next step is to learn python as a language. Because Python really has so many uses, data analysis is just one of its functions. Therefore, lock the goal, only learn the knowledge about the data analysis part, and skip all the other web development and network programming, and be sure to concentrate on overcoming your goal, don't be distracted, sometimes you can't come back when the stall is bigger!!

Then Data Analysis with Python is definitely the best choice for you to learn Python (and I mean data analysis).

I hope the above suggestions are useful to you, please give us a lot of advice.

In general, the basics are learned first, then the theory, and finally the tools.

1. Learn the basic knowledge of data analysis, including probability and mathematical statistics.

2. Relevant theoretical knowledge of your target industry. For example, in finance, it is necessary to learn various knowledge such as **, banking, and finance.

3. Learn data analysis tools, such as SAS, SPSS, and even Excel (the function of the data analysis module is very powerful).

Remember, the first step is essential and is the foundation of data analysis.

As soon as it comes up, there are all kinds of tutorials, not to mention a batch of book lists, various ways of data processing, how to mine data with python, how to use R for data visualization, and AB testing. The tutorials they give are very good, and the way the data is processed is also very instructive. Why it is used and why it is done.

At first I was speechless, but then I thought about it, this should be a common problem, this is the biggest problem in data analysis, many people learn data analysis, more data analysis for the sake of data analysis, in fact, they don't know what they are doing or why they do it. Contemptible people are proficient in data analysis. I guess HR will smile when he sees it, and then put his resume in the folder where he won't be hired.

Data analysis, no matter what kind of tools are used, what methods are used, whether it is Excel, Python, or MySQL, are tools with only one purpose - to solve a problem, or to provide a basis for a decision. Therefore, the first step in learning data analysis is to consider what kind of problem I want to solve with data. If you don't think about this clearly, what's the use of just following the online tutorials to learn crawlers, learn data cleaning, and then make those public statistics into brilliant charts.

Open a store. For example, there is such a realistic example, your cousin-in-law told you, Xiao Ming, you like to eat the Malatang I cook the most, I want to open a Malatang restaurant, which is near the university town, and you say **Okay. Do you think of the following questions?

So, you've taken into account the quantifiable data and you've found a way to get it. This means that data analysis must be closely related to the business itself. <>

When I was in college, I also took the time to learn data analysis, and I used my spare time to read more books and do more questions to consolidate my knowledge, and I also invited classmates from other colleges to tutor me at that time.

Data analysis will be used as a general skill in more and more different jobs. After all, mastering data analysis can improve one's corresponding business capabilities on the one hand, and on the other hand, it can also allow oneself to establish a data-driven perspective to think about various problems. Keywords:

Non-professional class + spare time + data analysis Combined with my own experience, I would like to talk about it from two perspectives: basic skills + hands-on practice. Basic skills: No matter what you are learning, you still need to have a big framework first.

For learning data analysis, the required skill modules include statistical foundation + database knowledge + programming ability statistical foundation: science and engineering students have studied probability theory and mathematical statistics at the undergraduate level, and it is actually enough from the perspective of data analysis alone. <>