# Comparison of Data Analysis Tools: Excel, R, Python, and BI Tools

### We look at these four main tools for data scientists and data analysts, examining the pros and cons of each one.

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Join For FreeThe era of data analysis has already arrived. From the state, government, and enterprises to individuals, big data and data analysis have become trends that everyone is familiar with. But you may not have the professional knowledge of data analysis and programming, or you have learned a lot about the theory of data analysis, but you still can't practice it. Here, I will compare the four tools that are most popular with data analysts, Excel, R, Python, and BI, as the basis for getting started with data analysis.

**1. Excel**

**1.1 Usage Scenarios**

- Data processing work under general office requirements.
- Data management and storage of small and medium-sized companies.
- Simple statistical analysis for students or teachers (such as analysis of variance, regression analysis, etc.).
- Combine Word and PowerPoint to create data analysis reports.
- Assistant tool of data analysts.
- Production of charts for some business magazines and newspapers (data visualization).

**1.2 Advantages**

- It's easy to get started with Excel.
- The learning resources are very rich.
- You can do a lot of things with Excel: modeling, visualization, reports, dynamic charts, etc.
- It can help you understand the meaning of many operations before further learning other tools (such as Python and R).

**1.3 Disadvantages**

- To fully master Excel, you need to learn VBA, so the difficulty is still very high.
- When the amount of data is large, there will be a situation of stuttering.
- The Excel data file itself can hold only 1.08 million rows without the aid of other tools, and it's not suitable for processing large-scale data sets.
- The built-in statistical analysis is too simple and has little practical value.
- Unlike Python, R, and other open source software, there is a charge for the genuine Excel.

**2. R**

**2.1 Usage Scenarios**

The functions of R cover almost any area where data is needed. As far as our general data analysis or academic data analysis work is concerned, the things that R can do mainly include the following aspects.

- Data cleaning and data reduction.
- Web crawling.
- Data visualization.
- Statistical hypothesis testing (t test, analysis of variance, chi-square test, etc.).
- Statistical modeling (linear regression, logistic regression, tree model, neural network, etc.).
- Data analysis report output (R markdown).

**2.2 Is R Easy to Learn?**

From my point of view, getting started with R is very simple. 10 days of centralized learning is enough for mastering the basic use, basic data structure, data import and export, and simple data visualization. With these bases, when you encounter actual problems, you can find the R package you need to use. By reading R's help files and the information on the network, you can solve specific problems relatively quickly.

**3. Python**

**3.1 Usage Scenarios**

- Data crawling.
- Data cleaning.
- Data modeling.
- Construct data analysis algorithms based on the business scenarios and actual problems.
- Data visualization.
- Advanced fields of data mining and analysis, such as machine learning and text mining.

**3.2 R vs. Python**

R and Python are both data analysis tools that need to be programmed. The difference is that R is used exclusively in the field of data analysis, while scientific computing and data analysis are just an application branch of Python. Python can also be used to develop web pages, develop games, develop system backends, and do some operation and maintenance work.

A current trend is that Python is catching up with R in the field of data analysis. In some respects, it has surpassed R, such as machine learning and text mining. But R still maintains an advantage in the field of statistics. The development of Python in data analysis has modeled some of the features of R in many places. So, if you are still newbie and haven't started learning yet, I suggest you start with Python.

Both Python and R are easy to learn. But if you learn both at the same time, it will be very confusing because they are very similar in many places. So it is recommended not to learn them at the same time. Wait until you've mastered one of them and then start learning the other one.

**3.3 Choosing R or Python?**

If you can only choose one of them to learn because of the limited time, I recommend using Python. But I still recommend that you take a look at both. You may hear in some places that Python is more commonly used at work, but solving problems is the most important thing. If you can solve problems efficiently with R, then use R. In fact, Python mimics many features of R, such as DataFrames in the Pandas library. And the visualization package under development, ggplot, mimics the very famous ggplot2 in R.

**4. BI**

There is a saying in data analysis: the text is not as good as the table, and the table is not as good as the graph. Data visualization is one of the main directions of data analysis. The charts of Excel can meet basic graphics requirements, but this is only the basis. The advanced visualizations require programming. In addition to learning programming languages such as R and Python, you can also choose BI tools that are simple and easy to use. For an introduction to BI, you can read my other article, What Data Analysis Tools Should I Learn to Start a Career as a Data Analyst?

Business Intelligence was born for data analysis, and it was born with a very high starting point. The goal is to shorten the time from business data to business decisions. It's about how to use data to influence decisions.

The advantage of BI is that it is better at interactions and reporting. It's good at interpreting both historical and real-time data. It can greatly liberate the work of data analysts, promote the data awareness of the entire company, and improve the efficiency of importing data. There are a lot of BI products on the market. Their principle is to build dashboards, through the linkage and drilling of dimensions, to obtain a visual analysis.

Published at DZone with permission of Lewis Chou. See the original article here.

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