Graphics + System + Compilation, the joy of creation.
Tag: #Data Analysis
Multiple ways to achieve data visualization.
A guide to help beginners quickly get started with data analysis!
Using Pandas in Python to complete data cleaning, improve your efficiency in data analysis.
Today, let us put aside these personal feelings. And I am trying to talk objectively with you about my personal views on data analysis tools on the market, for your reference.
I have chosen a total of 6 tools in three types. Let me introduce them one by one.
Many people who are just getting started with finance often feel it a headache to deal with financial statements. In fact, to do financial statement analysis, you only need to master the formula of “idea + content + tool”.
Still looking for the next place to travel? Let big data tell you the answer.
The essence of data analysis is actually to do data comparison analysis. Without data comparison, it is often difficult to use single indicator statistics to play the value of data. This post uses the Internet industry as the background for data business analysis to share some experience in traffic analysis.
Data maps are very intuitive and visual expressions in the process of business analysis. You can use maps to show sales and profits in various regions, or to display the distribution of warehouses all over the country to optimize transportation networks. As a professional application, the data map has a wide range of analytical uses.
If you want to learn more aboout how to achieve lean management, and as a leader of company, you want to realize the intelligence production using lean management, you must read it.
From the perspective of data analysis, this article makes a modular analysis of the marketing decision, including six steps: indicator decomposition, management cockpit, marketing KPI system, dealer and store management, competing product analysis and predictive analysis, which has a good guiding significance for the planning and construction of marketing work.
This article compares the four most popular data analysis tools: Excel, R, Python and BI. If you want to enter the field of data analysts, you can start with these four tools.
Data analysts are often divided into two categories: business analysts and technical analysts. And their requirements for tools are quite different. This post summarizes the data analysis tools necessary for data analysts.
How to improve business conversion rate is usually a challenge. There is a modeling tool in data analysis that can help you quickly find the problem and take optimized measures to improve conversions. This is the funnel analysis model.
Data analysis thinking is a framework-based guide in data analysis. This post will share five common data analysis methods: the formula method, the contrast method, the quadrant method, the 80/20 rule and the funnel method, which are often used in combination.
Doing a report to make a form, many people think of Excel in the first place. Excel as a personal office software is unique, but for commercial use, in fact, there is a slight lack of efficiency in office collaboration, however,FineReport can directly interact with the database (data export + data filling), can connect the data of each system, can be high Efficient batch report, display, interactive analysis, visualize large screens, and achieve office collaboration.
Tableau is suitable for professional analysts who are familiar with analytical techniques and business executives who have the need for pivot analysis. FineReport is the IT staff leading design, business executives involved and used. Because of this difference in targeting for people, there are differences in the handling of many functions.
Based on my own experience and understanding of data analysis, I have summarized my years of experience and knowledge into 35 thinking models & data models. Through these models, which can help you quickly get started with data analysis and reduce the detours in the process of self-exploration.
On October 12, 2018, the “2018 Fanruan New Product Launch Conference and User Experience Day” kicked off in Shanghai. This event brought together nearly 800 enterprise IT technicians and data peers from all walks of life to gather together. With the theme of “FINE DAY”, the conference focused on the development direction of current big data. The main agenda of this conference is divided into three parts: first, the new product launch conference of FineReport 10.0 and FineBI 5.0, followed by the user’s benchmark speech and exhibition area exchange. Experience and so on.