Data analytics is a field of knowledge that, while known for many years, is only now finding its true business application in the era of Big Data. What is data analysis used for? It is used to increase the efficiency of business processes, analyze customers, monitor transaction security, and for many other processes. Let’s take a look at the most commonly used tools that transform simple data into powerful insights that drive business decisions. Python with Pandas, NumPy, and SciPy libraries
We are all aware of the wide range of applications for the Python programming language. This technology is used for software development and supports AI and ML development processes, but above all, it is the primary weapon in data analytics. This application of Python is due to specific libraries that equip analysts with hundreds of functionalities and capabilities for processing, analyzing, and visualizing data. What are the most popular libraries used for data analysis in Python? Primarily:
- NumPy – An extremely useful library that speeds up the process of working with advanced mathematical models such as matrices or multidimensional arrays by several times. It is one of the essential libraries if you want to engage in data analysis with Python.
- Pandas – In the opinion of analysts, this is the most comprehensive and popular library for acquiring and processing large datasets. Pandas enables easy integration with other technologies such as Excel, databases, or R.
- SciPy – This, in turn, is the true favorite of all mathematicians and analysts who use Python for scientific purposes. The library enables the creation of highly complex functions, allows for the creation of spatial algorithms, and the analysis of concepts such as linear algebra.
SQL, or Structured Query Language
The next tool is SQL. Structured Query Language is not so much a tool as a query language that allows you to unlock the enormous potential of databases. Databases and data warehouses guarantee the storage of massive datasets, and their skillful use often guarantees a competitive advantage in the industry.
SQL is a language that serves as a universal technology for creating, managing, analyzing, and deleting databases built within the most popular database management systems. Currently, every application integrated with a database uses SQL, and the continuous development of this technology means that in the future, reports valuable to management will only be obtainable through appropriate SQL queries.
R programming language
The R programming language is a free and easily accessible programming environment that is primarily used by data analysts for data acquisition, processing, analysis, and visualization.
Similar to Python, R also boasts hundreds of useful libraries that, in practice, allow for any data-related task to be performed – from creating advanced econometric models and forecasting to extraordinary methods of data visualization. A significant advantage of R is the ability to integrate the environment with other frequently used languages like Python or Java, as well as other technologies like databases, from which we can retrieve data for analysis.
Julia programming language
Julia is a language less known to analysts but more familiar to mathematicians and scientists, which largely draws the best from technologies like R, Python, or the popular MatLab. What characterizes Julia is its extraordinary speed and performance, which is not found in any of the three previously mentioned technologies. Through the Julia language, we are able to call functions, sort, generate multiple loops, or work on huge multidimensional arrays with extraordinary speed.
Julia is a language created by specialists from MIT, and its main application is precisely working with huge operations, the solution to which might take a very long time when working with other languages.
Excel
Excel is in a different league when it comes to software used by data analysts, but it would be a significant understatement if this flagship element of the Office suite were not included on our list.
Microsoft Excel is versatile, universal, and so rich in possibilities for creating new functionalities (mainly thanks to VBA) that it is hard to find an industry or field where spreadsheets would not be useful. Excel is just as good a piece of software for analyzing and manipulating data, and the multitude of functions means that through this tool, we can create beautiful visualizations of our results. Excel is truly the foundation of data analysis, and if you have ever planned to start an adventure in this field of knowledge, your first steps should be directed toward Excel.
Power BI
Microsoft Power BI is another piece of software from the Microsoft stable that perfectly combines extensive reporting functionalities with integration with the Azure cloud. It is a great reporting platform, as evidenced by the growing popularity of using Power BI in large companies and corporations.
What impresses us about Power BI? Above all, its ease of use for inexperienced users, rich integration capabilities with other systems, and the ability to implement AI algorithms, which will increase the efficiency of working with such reports.
Google Data Studio
Google Data Studio is a relatively young tool that is largely used for reporting.
It is software that has replaced Excel in many organizations. What are the main advantages of using Google Data Studio? Primarily, free access to the tool and its intuitive operation. Additionally, GDS allows for the collection of many different data points on a single dashboard and clear manipulation of them to create beautiful visualizations. Google Data Studio is a tool that is increasingly used by marketing companies for web traffic analysis, keyword analysis, or many other processes that increase the efficiency of e-commerce activities.
