R & PythonAlthough software like Excel and BI tools have been designed with the utmost effort to consider the most application scenarios of data analysis, they are essentially customized. If the software doesn’t design a feature, or develop a button for a feature, chances are that you won’t be able to complete your work with them. The programming language is different for this. It is very powerful and flexible. You can write code to do anything you want. For example, R and Python are the indispensable tools for data scientists. From a professional perspective, they are definitely powerful than Excel and BI tools. So what are the application scenarios that R and Python can realize, while it is difficult for Excel and BI tools to achieve? 1) Professional statistical analysis In terms of R language, it is best at statistical analysis, such as normal distribution, using algorithm to classify clusters, and regression analysis. This kind of analysis is like using data as an experiment. It can help us answer the following questions. For example, the distribution of data is a normal distribution, a triangular distribution or other types of distribution? What is the discrete situation? Is it within the statistical controllable range we want to achieve? What is the magnitude of the effect of different parameters on the results? And there is also hypothetical simulation analysis. If a certain parameter changes, how much impact will it bring? 2) Individual predictive analysis For example, we want to predict the behavior of a consumer. How long will he stay in our store? How much will he spend? We can find out his personal credit status and make a loan amount based on his online consumption record. Or we can push different items based on his browsing history on the web page. This also involves the current popular concepts of machine learning and artificial intelligence.
Explore Data & Analytics Statistics
- Only 16% of organizations can currently say that 75% or more of their employees have access to company data and analytics.
- By 2025, 60% of the 163 zettabytes of existing data will be created and managed by enterprise organizations.
- Data warehouse optimization is considered the most important big data analytics use case, and is considered critical or very important by 70 percent of businesses.
- 53 percent of CEOs consider themselves the primary leader of their company’s analytics agenda.
- 98 percent of sales representatives at construction companies that adopt analytics and geographic data reported dramatic decreases in their time frame for providing price quotes.
- 55 percent of North American businesses have adopted big data analytics.
- By 2025, the amount of the global datasphere subject to data analysis will grow by a factor of 50 to 5.2 zettabytes.
- Insights-driven businesses are growing at an average of more than 30% each year, and by 2021, they are predicted to take $1.8 trillion annually from their less-informed peers.
- 95 percent of businesses need to manage unstructured data.
- 8 percent of businesses say data and analytics have fundamentally changed the nature of industry-wide competition
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