Graphics + System + Compilation, the joy of creation.
Category: 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.
In the financial statements, compared to traditional dense tables, charts can visualize the data and display the data more intuitively, making the comparison, trend and structure of the data clear at a glance. This article analyzes the types of charts in financial statements from summary data analysis, development trend analysis, data comparison analysis, composition analysis, progress analysis, and map 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.
In this article, for newbies, I concluded the 3 basic steps to an effective financial statement analysis. To let the data speak and reflect the enterprise’s financial situation and problems.
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”.
How to shorten the distance between data and value? An article to tell you how to build a link between data and value in data gravity.
What’s the gravity between data and value? An article to tell you how to manage data in a more efficient way.
Still looking for the next place to travel? Let big data tell you the answer.
Learn more about data science and AI from movies.
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.
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.
This article tells you 4 principles to pick the perfect color combination for your data visualization. The color scheme sets the tone of anything that you’ve created and each color serves to represent a unique piece of information. And FineReport has built in a lot of beautiful color schemes to allow users to choose.
Finding a right reporting software can be really overwhelming. Although there are some review sites to help you, but you still need to review the review sites. This article will tell you the pros and cons of the review sites.
Statistics is the cornerstone of data analysis. Here is a summary of statistical knowledge points, which covers basic statistics, probability distribution, population and sample, confidence interval, etc.
An analysis system that can successfully operate and bring benefits often requires a series of links such as master data management, data warehouse construction, analysis platform, and indicator combing. What kind of financial problem do you want to solve? What’s your expectations and what data do you have now? What do you have to do step by step?
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.