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First and for all before count the differences between statistics/ statistical analysis and data analysis you should identify what does statistics and statistical analysis mean? And what is the data analysis mean? After identify them and their importance, you will be ready to know the differences between data analysis and statistical analysis.
Statistics means that the science of learning from raw data. In other words, statistics is the process of how to collect data, organize data, interpret data, evaluate data and present data. But analysis is the process of how to use statistics to identify patterns or to develop and improve new theory. Statistics and statistical analysis are two integrated process to transfer raw data to meaningful data that will the non-technical people understand it.
As people we use statistics/ statistical analysis in a lot of fields in our life. For example, statistics/ statistical analysis is used by experts to predict forecasting by analyzing data by specific ways. Also, scientists using statistics/ statistical analysis in their researches in the fields of genetics, medical and medicine products.
Data analysis process is the process of cleaning data, analyzing data, interpreting data, recapping data and visualizing data to get better decisions for effective business to be done. Also, data analysis process that transfer raw data with no meaning for people to get specific statistics, useful information and explanation.
While data is everywhere and information are evolved in a magic speed, then you need data analysis process to deals with these huge amounts of data to get best results from analyzing data correctly. Here some points why data analysis is important:
- The main purpose of using data analysis is to get useful and meaningful information from raw data to make effective decisions.
- If you are a business man/ owner, data analysis will help you to improve your products and services that agreed to your customers.
- Data analysis process will tell you to where you need to focus your effort.
- You can analyze customer feedback about specific product by using data analysis process.
Data analysis process is different from statistical analysis process. Before many years ago, the gap between data analysis process and statistical analysis process is very wide. But nowadays as data analysis involved and as the improvement methods of statistical analysis process, this gap is narrower.
You can say that data analysis is a part of statistical analysis and statistical analysis is a part of data analysis. That’s means some of statistician have experiences with programming languages used for data analysis. And in the other hand an expert data analyst has a good understand of using statistical analysis tools.
Statistician is the person who has an experience of statistical analysis and statistics methods and tools. Statistician is a statistical analysis specialist who used numerical and mathematical methods to gain useful data. Data scientist is the person who has an experience of data analysis and data analysis methods and tools. also, Data scientist is a data analysis specialist. Data scientist also called a data analyst and he use science data tool kit like R programming languages to examine data and infer analysis.
First: data analysis process analyzing data, cleaning data, transforming data and modeling data available into useful information. That’s in order to allow non- technician people understand this information.
Second: in the other side, statistical analysis process applying statistical analysis methods and tool into a sample of data to understand the whole data.
Third: statistical analysis process using data from various sources and combined these data to perform statistical analysis process. And the data which results from data analysis process can be used as an input to perform statistical analysis process.
Both of data analyst/data scientist and statistician define data analysis in different ways:
- For statistician, the statistician performing data analysis on a sample amount of data. Then statistician apply statistical methods and techniques on this sample to get results
- For data scientist/ data analyst, the data analyst dealing with a large amount of data. data analyst cleaning data, interpreting data, analyzing data and modeling data to gain useful information. And this information can be used by non-technician people who can understand it to do their work. Other thing that the data analyst performed data analysis on a large amount of data by using computers.
In the issue of data analysis:
- As you read in the previous paragraph, data analyst using data science toolkit to make inferences and perform data investigation. This toolkit can be a programming language like R or Python programming language, or it can be a framework experience like Hadoop.
- Statistician in the other hand using numerical and mathematical techniques to make data inferences.
| Differentiation |
Data science |
Statistics |
| Definition |
The process of scientific techniques that extracts information from data |
The process of collect, analyze and evaluate data |
| Concept |
Uses advanced statistics and mathematics to extract new information from big amount of data |
Applies some of statistical methods and functions on data to get appropriate results |
| Information basis |
Used to solve problems related with data |
Used to design and formulate real world questions related with data |
| Application |
Manufacturing, Finance, Engineering, a healthcare system and market analysis |
Commerce, Trade, Industry, the population study, Physiology and Biology |
| approach |
Using random data to apply scientific methods in problem solving |
Use of mathematical concepts, formulas and mathematical methods |
Data analysis tools:
- Analytics tool: this is the first tool in data analysis process which provide firstly, visual analysis and dashboarding. Secondly, provide collaborative review of analysis data and also, deeply analyze data.
- R programming language: this an analysis programming language that help analyst to analyze data with coding some instructions
- IBM SPSS modeler: this is a predictive platform for big data analysis. It is a big data analysis tool that contains a group of advanced algorithms and analysis techniques.
- Apache Spark tool: it is an open source for big data analysis tool. Spark tool provide more than 80 high-level operators which make the easiest to build parallel apps.
- Xplenty analysis too is a cloud-based ETI tool. Which allow the analyst to clean, normalize and transform data.
- Azure Microsoft HDInsight: this analysis tool is a park and Hadoop cloud service. Azure Microsoft HDInsight provides standard and premium cloud categories for big data.
- Skytree analysis tool: it allows the analysts and scientists to build more accurate and fastest models for data.
- Talend: is a big data analysis tool that simplifies and automates big data integration.
Conclusion
- Not all data analyst can be a statistician and not all statistician can be a data analyst.
- Both of data analysis/ data science and statistical analysis/ statistics are processing to coexist with each other.
- A field of data analysis and statistical data cannot work separately.
- Later, data analysis and data science are grown with the big data and they will be developed continuously as data continue developing.
- Data is used in statistical analysis which can be combined from various sources in order to assist the process of statistical analysis.
- Statistical analysis and data analysis are used together to solve business problems.
With greetings: Al - Manara Consulting to help researchers and graduate students