How to transform from a zero-based novice to a professional data analyst in three months?
With the development of the digital economy and the era of big data, data has become the most important profit resource in the current era, allowing enterprises to be more targeted and based when making decisions and planning plans, and can predict the direction of market development in advance and make a good layout. The resulting data analysis positions are gradually being valued by more companies, especially medium and large companies have a great demand for talents in this area.
The popularity of data analysis in China has only started in recent years, and it has been introduced to the mainland for a shorter period of time. At present, there is a great demand for data analysis talents in Chengdu, but there is still relatively little training in this area. If you are very interested in big data and data analysis, you can seize the opportunity to learn to enter the industry.
But if you want to master the technology of data analysis within three months, you must make a study plan. Do not start learning with a textbook. Learning requires methods and skills, especially for those who have no contact with data analysis. noob. At the same time, time and energy are also necessary investment costs. The learning content and process must be systematic. Don't expect fragmented learning arrangements to master data analysis well. The following are some preparations before the start of learning and sharing of learning skills, I hope it will be helpful to you!
1. Understand clearly what technical content data analysts need to master?
Compared with other IT majors, data analysis is relatively easier to get started and the whole learning process. It does not have as many code requirements as the back-end, and it does not have a wide range of technologies involved in cloud computing. Therefore, data analysis is an IT major that is very friendly to zero-based beginners . You need to master two core contents: data analysis tools + data analysis thinking.
1. Data analysis tools : The first lesson of data analysis is to learn various data analysis tools, such as Excel , which can basically solve most data analysis problems, and the number of data processing can reach millions; you need to master common Excel functions , Data Pivot and the production of visual reports.
Power BI builds business intelligence reports, aiming at the amount of data that Excel cannot handle, including AIPL model crowd visualization, advanced intelligent visualization, bubble map, shape map...
SQL database , fast data processing, including SQL single-table query, multi-table query, joint query, query operators and sub-queries, three-step SQL to solve business problems, multi-condition business analysis and other technical content.
Python programming , Python is a must-learn programming language for data analysis. It plays an important role in massive data processing and lays the foundation for big data mining and analysis. The content includes common technical content such as tuples, lists, dictionaries, sets, conditions, file loops, reading, Python data visualization, missing data processing, data type conversion, data sorting, and outlier processing.
2. Data analysis thinking and methods : After the tools are mastered, they need to be used in actual project work to be valuable; and the thinking and methods of data analysis that can reflect the value of our data analysts. Many people mistakenly think that as long as they master data analysis tools, they can be qualified for this position. In fact, data analysis itself cannot directly create value; therefore, more accurate data is needed to indirectly create value and provide data support for leaders and business departments. and solutions.
Then, if you want to master this skill well, you need to learn some analytical thinking and methods: the ten common data analysis methods ( multi-dimensional disassembly analysis method, comparative analysis method, hypothesis testing analysis method, correlation analysis method, group analysis method) , RRM analysis, AARRR model, funnel analysis, regression analysis, logic tree analysis );
Construction of indicator body ( including understanding data, user data indicators, indicator selection, establishment of indicator system, etc. );
Make data analysis report ( type and purpose of data analysis report , 5W2H method, pyramid principle, SCQA method, etc. ).
At this point, the basic skills course of data analysis is completed, and you can do most of the data analysis work, whether it is the existing data or the work content that needs to be obtained in the database, you can also crawl data through Python. However, data analysis faces different industries and businesses, and the requirements for job functions will also be different. Therefore, in the learning process, we must focus on project practice and accumulate different analysis methods and skills. If you have a zero foundation and want to master the above skills within three months, you need to find professional courses to study, and do projects with technical experts to achieve the results you want.
2. Precautions before learning
1. About education background and age
The basic requirements for academic qualifications are college or above, and non-computer or mathematics related majors are acceptable . The salary division of data analysis is still relatively affected by academic qualifications. After all, there are not many technical things in the profession itself, and it mainly depends on personal thinking and problem-solving solutions. The salary of undergraduates is generally 8k-10k, and the salary of junior colleges will be less, around 6k-8k.
In fact, age, data analysis does not have a big age limit for students, and 30+ can also learn. Data analysis can also be regarded as a position that becomes more popular the longer you work. It has similarities with cloud computing. The more experience you have accumulated in work projects, the more ideas and methods to solve problems. There is basically no overtime work in the position, and the development is relatively stable. There are many industries that you can work in. You can choose the industry you like to apply for a job.
2. Cultivate data analysis thinking , don't be preconceived, everything speaks with data, and remember to imagine yourself. This is also a taboo for data analysts. Data with personal subjective color is not objective and correct for enterprises. Data analysis has its own measurement and screening standards and systems, and only quality data can meet the needs of enterprises. As a qualified data analyst, you must abide by the company's standards, and at the same time, you must have a deep understanding of the business , rather than behind closed doors; cultivate data sensitivity , see the principles behind the data and the reasons for it, and be able to discover the company's business problems; Closed-loop thinking thinking about problems , status analysis, attribution analysis , competing product analysis, strategic recommendations, etc., not only to find problems, but also to solve them.
3. Basic tools must be used proficiently , and data analysis tools must be proficient. Although many students will think that these tools are not important after learning, how to make your data reports more intuitive for leaders and colleagues to understand is also an important skill . After all, we will feel happy when we see reports with good objectivity. Of course, the premise is that the data is good. If there is a shortfall in the business, there needs to be a practical remedy.
4. Do more projects to accumulate work experience . To learn data analysis, you must do more projects to accumulate work experience and ideas and methods for solving problems. After joining the job, you can get started with work better and integrate into the team as soon as possible. I have also written a few articles about some directions for daily work during data analysis. Interested friends can go to the homepage to search and find out, so that they can have a more comprehensive cognition and understanding of this position. Employment is also more targeted to find a direction that suits you.
3. Sharing about learning skills
1. The focus of the data analysis course is actually on the analysis. The data and tools are only auxiliary, because these machines can also complete it, such as the recently popular Chat GPT, and the collection and cleaning results of data may be better. If you don't want to be replaced by machines, you need to be able to do skills that machines cannot replace, such as the ability to analyze and think. AI can only obtain data for analysis based on existing data analysis ideas and methods, and has not yet reached the stage of thinking like a human.
2. Practice makes perfect. You need to do more practice and operation during the tool learning stage, and you can basically master it well. It is also necessary to practice in combination with actual projects to deepen understanding, learn to use different analytical methods to infer backwards, and cultivate analytical thinking.
3. Asking if you don't understand is an excellent study habit. Although data analysis is easy to learn, it is still a big problem for many students in the later stage of analysis methods and thinking learning. They will unconsciously bring in subjective thinking, and they are not aware of it. If you encounter a difficult problem, don't feel embarrassed to ask your teacher or classmates because it is easy, everyone will inevitably encounter a "brain strike".
4. Pay attention to the cultivation of soft power. Although data analysis skills look relatively simple, the job function is not just to do a good job of analysis and submit reports. It also needs more leadership reports and communication with colleagues. For example, if you want to make your data more quality and valuable, you need to understand the specific business situation, activity progress, feedback, etc. with the business department in the early stage, and only when you analyze it can you get the report needed by the business. During this period, your communication skills, understanding skills and teamwork skills will be tested.
Summarize
Seeing this, I wonder if you have a certain understanding of the data analysis major and the learning process. Are you confident that you can master this technology within three months? If you find a reliable training class for offline learning, and learn and do project exercises with professional teachers, you can still easily reach your goal within three months. However, during this period, you will definitely need to put in a certain amount of effort and patience. The learning process will definitely be boring or you will encounter technical difficulties that you cannot overcome. It can be solved easily.
So, if you are interested in IT and data analysis, seize the opportunity to join us! If you still want to know more about data analysis, you can take a private class ~~~