Host: Hello everyone, today we have invited Shouming Jiang, a CDA licensee, who is currently working as a consultant and solution provider for a fintech company. Welcome Shouming, say hello to everyone!


Shouming Jiang: Hello, my name is Shouming Jiang and I am currently working in a FinTech company as a consultant and solution provider. I studied information and computing science in my undergraduate program, half math and half computer courses, so I have gone through some elementary and systematic learning and training in statistics, database and software programming, and I studied management science and engineering in my graduate program.



Question 1.

I'm curious about the consulting and solutions piece. Will it utilize data analytics?



Shouming Jiang: Yes, there are quite a lot of application scenarios.

Moderator: Can you give us a few business examples and tell us more about them?

Guest: No problem. I will give two types of application scenarios.

The first category is the business management scenario.

With the development of market economy, the scale and complexity of the market and customers of medium and large enterprises are increasing, and the process of collecting and summarizing operation data is becoming more and more time-consuming. At this time, the reporting time for management is also increasing, because at this time, it is no longer possible to draw conclusions from the basic data, and each question raised by management needs to be answered by combining information from many aspects.

In this case, it is necessary to build a data platform for the enterprise, connect the data of various IT systems in the front and back office, and use the methodology and tools of data analysis to process and analyze the presentation of a large amount of basic data. So now many medium and large enterprises have opened the digital transformation, and started to build data boards for management analysis, management cockpit, etc. We can also obviously feel that in recent years, the recruitment market for mastering digital transformation, data analysis and other aspects of skills of talent demand is also growing.

The second category is project management analysis scenario.

Take my current company as an example, as a large scale financial technology enterprise, responsible for building a large scale of IT applications to serve all kinds of complex business needs, but with the continuous development of technology, application systems need to continue to iterative upgrades, each involving technical architecture upgrades often require hundreds of projects in parallel implementation and effective project group management, this situation using traditional In this case, it is difficult to clearly show the whole picture and key information of the project by using traditional and simple data reports. In this case, it is also necessary to use data analysis methods and tools to carry out multi-dimensional perspective analysis, global progress tracking and timely risk warning based on the basic data of the project group as a whole, and present them in an automated and visual way to assist project management and promotion.

These two types of scenarios are the ones I have actually experienced and participated in as a consulting and solution consultant, and there are actually many other types of scenarios.



Question 2.

Okay, I see that during your study period, half of the math courses were taken, and one of the more common questions asked by many friends around you who want to engage in data analytics is, is it necessary to be particularly good at math to engage in the financial data industry?



Shouming Jiang: No, not really. The financial data industry is a relatively generalized concept, perhaps securities analysts, fund managers, financial big data analytics engineers and other professional posts more closely related to the need to model market transaction data to predict, optimize investment models, or design and develop related algorithms and software to serve the financial scene business decisions, these jobs require a certain degree of mathematical skills.

Other job types, including my type of consulting solutions, more is required to understand the business, understand the data, the use of data analysis methodology and tools, more efficient, more intuitive, comprehensive organization of data and present conclusions, does not need to master the complex mathematical theory or algorithms. Of course, if the mathematical skills are good is definitely a plus, in learning and mastering the relevant theory and tools, can be faster to understand the principles and hands-on use.



Question 3: As a data analyst in the financial industry, what business knowledge do you think is essential to learn? How to better advance yourself?



Jiang Shuming: I think the business knowledge depends on the nature of the company, business type and job type. For example, if you are in a bank, an insurance company or a securities company, the business knowledge required is different. You should at least understand some terminology, common business logic and rules of the corresponding business in order to communicate effectively with business personnel, understand business objectives and carry out data analysis. The former may require mastering some procurement, sales, logistics, inventory and finance and other related knowledge, with certain company business planning and analysis capabilities, while the latter requires an understanding of Internet-based data operation methods; the job type is more specific, for example, if you do investment analysis, you need to master systematic finance, economic theory and investment analysis methods.



Question 4: As a CDA holder, do you have any strategy to share with us for the preparation of the certificate?



Jiang Shouming: There are two main preparation strategies.

First, the exam syllabus and mock questions must be well read and well done, the certificate involves a wide range of relevant knowledge, the exam syllabus can help us focus on some key knowledge modules focus on learning, the preparation stage also need to review the knowledge points to fill in the gaps; mock questions are best done after the completion of the overall study, after all, the number of sets is limited, if each set of questions can be scored in the seventy to eighty points or more, then Directly register for the exam is still more certain to pass, after doing the topic should also focus on the analysis of the wrong questions, identify blind areas of knowledge, and migration to fill in the gaps.

The second is to take notes or very necessary, notes can help us record the main points, deepen the impression, in the systematic learning, before and after the knowledge points are related or based on each other, often need to review the knowledge behind the learning front, if there are notes in the words, will save a lot of time; In addition, in the study, the preparation line is relatively long, you need to review the notes repeatedly to combat forgetfulness, as well as the last concentrated preparation review that. In addition, when the study and preparation line is long, you need to review your notes repeatedly to combat forgetfulness, and you need to have enough ammunition for the last few days of concentrated preparation.



Question 5: Do you have any experience to share with people who are about to enter financial data analysis?



Jiang Shouming: I would like to share two points of my personal understanding.

The first point is that we need to keep learning to master complex tools, and the tools here are in a broad sense, which can be theories, algorithms, models, software and so on, to improve efficiency and effectiveness through the mastery of complex tools, and often the more complex the tools, the more obvious the degree of improvement, because the part of efficiency improvement is the part that the tools automatically help us to complete; cost reduction and efficiency is the constant theme of company operation, and we continue to The use of tools to improve personal efficiency and organizational efficiency will bring more profits to the company and highlight the value of our position.

Secondly, I think that the data analysis position should be advanced, and must be deeply integrated with the business objectives, through long-term focus on a certain or certain business areas and data, precipitate and summarize some common entry points and unique perspectives for analyzing problems, cultivate their own data sensitivity, and finally form business diagnostic ability, and become a user growth expert, customer marketing expert, or enterprise risk control expert. This is the goal and direction that should be set early as a data analysis position.

 

Conclusion.

Well, thanks to Shou Ming's sharing, no thanks to the solutions, every question answered is like giving a solution, Shou Ming combined with his work business to share the type of financial business scenarios, specific jobs also need to master the knowledge of professional areas, but also in the CDA certification preparation, as well as to engage in financial data analyst partners put forward their own advice, thanks again! Shou Ming received our interview, we will see you next time! Bye bye!