How to get started with data analysis?
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.
So, what is data analysis? Data analysis is the product of the combination of data and computer technology, and it can be promoted and used in the growth of computers. Encyclopedia definition : Data analysis refers to the use of appropriate statistical analysis methods to analyze a large amount of collected data, summarize, understand and digest them, in order to maximize the development of data functions and play the role of data.
Getting started with data analysis is actually not difficult. Once you have mastered common analysis tools and methods, you can find a job, and then accumulate analysis experience and methods in actual projects to further improve your professional ability. So if you want to learn data analysis with zero foundation, it is also possible. After about 3 months of systematic study, you can basically meet the entry requirements ; of course, this time is for class registration. If you choose to learn by yourself, the time cost must be more , If you want to avoid some detours, you can sign up for a class, which is faster and more effective.
1. The learning route of data analysis
If you want to start data analysis with zero foundation, it is best to start learning from the following technical routes:
First of all, you need to be sensitive and interested in data, preferably with a foundation in mathematics or statistics . Nowadays, many occupations such as operations, finance, personnel, and products require data analysis skills. If you master this skill and then apply for a job, promotion and salary increase are inevitable. For example, operations often come into contact with user basic data, behavior data, and business data, etc., and you need to find important clues from these data to provide direction guidance for your operation strategy: which type of people is more likely to place an order through which video at what time high.
Secondly, master the necessary analysis tools, Excel , powerBI , MySQL , Python . If it is a position that only requires analytical capabilities (operations, products, finance, etc.), mastering basic Excel and BI tools can solve most of the problems; the emergence of BI makes analysis easier and more efficient, suitable for handling more complex tasks Problem, can make a more advanced visualization chart than Excel.
But if you want to engage in a professional data analysis career, you must be proficient in MySQL and Python, which have direct value in enhancing your competitiveness. When you want to process more than one million pieces of data, SQL and Python are your plug-ins: get the underlying data through SQL, and get the data without asking for help; you can also realize office automation and say goodbye to meaningless and tedious repetitive work. Credit to Python.
Mastering data analysis tools is a basic skill for data analysis positions. Some friends may find it a bit difficult when they see this, because it involves code, and they are worried that they will not be able to learn it. The small class wants to tell everyone that there is no need to worry about not being able to learn it. The codes learned for data analysis are very few, and they appear in the form of tools. Basically, you can master them with more operations.
For example, the Python skills required for introductory data analysis: tuples, lists, dictionaries, sets, conditions, and loop files, etc.; index slicing, data cleaning, Pandas, missing data processing; data visualization, NumPy, automated office, data type conversion.
If you don't know how to get started and don't have clear learning ideas and skills, the safest and most effective way is to find a reliable technical teacher and follow along to learn. For example, Mr. Zhang, a senior data analyst at Peking University Jade Bird Tianfu Campus, has more than ten years of corporate work + teaching experience, and has trained many data analysis talents for various industries, which is trustworthy.
Finally, it is the mastery of data analysis methods . Commonly used data analysis methods must be proficient: AIPL model crowd visualization, RFM e-commerce user behavior crowd structure analysis, funnel analysis method (locating business problems and predicting data), AIPL model, AARRR model : Quickly realize user growth, RFM analysis method (user classification, refined operation), comparative analysis method, logic tree analysis method (simplifies complex problems), regression analysis method (proposes effective suggestions and predictions), multi-dimensional disassembly analysis method, Hypothesis testing analysis, correlation analysis, group analysis, etc.
For the learning of data analysis methods and the cultivation of analytical thinking, you can also pay attention to the data analysis course of Peking University Jade Bird Tianfu Campus. All analytical tools, methods, and the cultivation of analytical thinking are available, and there are supporting projects, and they are all real project cases. ; It will also involve the extended learning of R language, etc. If you are interested, you can find out.
If you want to take the direction of big data analysis, you can continue to learn deeper technical content such as machine learning and big data mining.
2. The importance of data analysis
In the past, when bosses made business plans, they made decisions with their brains, or brainstormed, and made business plans based on experience. However, with the development of the Internet and the penetration of big data, users have more choices about information and products, their self-awareness increases, and their behavior habits change rapidly. Then enterprises also need to keep up with the footsteps of users, use data analysis tools to collect and organize user data in a timely manner, understand the real needs and trends of users through the analysis of data analysts, and provide more effective guidance for enterprise decision-making, so as to fundamentally Changed the way enterprises make business decisions.
3. Development prospect of data analysis
As an IT technology job that has become popular in recent years, data analysis has rapidly fermented and expanded under the tide of the big data era, sweeping many Internet companies, spreading to traditional industries such as finance, education, medical care, and consumption, and also playing an important role in the new economic field , such as artificial intelligence, new energy, electronic chips, enterprise digital services and so on.
At present, there are only about 500,000 data analysis talents, and the market demand will reach about 1.8 million in the next three to five years. In the face of the upcoming blowout demand for talents, if you like working with data and the profession of data analysis, then now is the best time to learn it. There is market demand and development.
All in all, getting started with data analysis is not difficult, but it requires systematic learning and accumulation of project experience in order to become a qualified data analyst. In the future, data analysis still has more room for growth and development potential: employment is in demand, salaries are high, and development prospects are good. As an IT technology of choice in 2023 , data analysis is simply not too good! If you want to know more about data analysis, you can take a private class~
