As the area of data analytics increases, so does the number of data analysis tools accessible. In this piece, we'll go through some of the most important data analysis tools you should be aware of and why. You'll receive a short review of each, including its uses, advantages, and downsides, from open-source tools to commercial software.
Let’s discuss some of the most useful data analysis tools, but first let’s understand what do we really mean by data analysis
What is Data Analysis?
Data analysis is described as the process of cleansing, transforming, and modeling data in order to find usable information for business decisions. The goal of data analysis is to extract usable information from data and make decisions based on that knowledge.
A basic example of data analysis is when we make a decision in our daily lives, we consider what happened previously or what would happen if we make that decision. This is nothing more than examining our history or future and making judgments based on that analysis.
We acquire memories from our past or future aspirations for this purpose. So that is just data analysis. The same activity an analyst conducts for commercial goals is now known as data analysis.
Data analysis is the process of cleansing, analyzing, and displaying data in order to get important insights and make better business decisions. The methods you employ to examine data will differ depending on whether you're looking at quantitative or qualitative data.
In any case, data analysis tools will be required to assist you extract relevant information from business data and make the data analysis process easier. In business, you'll often hear the word data analytics, which refers to the science or discipline that spans the whole data management process, from data collection and storage through data analysis and visualization.
While part of the data management process, data analysis focuses on the process of converting raw data into meaningful statistics, information, and explanations.
Why is Data Analysis Important?
"Data is Everywhere," in spreadsheets, social media platforms, product evaluations and comments, and so on. It is developed at dizzying speeds in our modern information age and, when properly examined, may be a company's most valuable asset.
If your firm is not developing, you must look back and acknowledge your faults before creating a new plan to avoid repeating those mistakes. Even if your firm is expanding, you must plan for future expansion. All you have to do is examine your data and operations.
Businesses must understand their consumers' needs in order to boost client retention and attract new customers. However, in order to understand exactly what consumers want and what their pain points are, firms must go deep into their customer data.
In summary, data analysis may yield insights that inform you where you should focus your efforts to help your organization thrive. It may assist firms in improving certain areas of their goods and services, as well as overall brand image and customer experience.
If your firm is not developing, you must look back and accept your mistakes before creating a new plan to avoid repeating those mistakes. And even if your firm is expanding, you must plan for further expansion. All you have to do is examine your business data and operations.
Also Read | Steps for Data Analysis
Data Analysis Tools
There are several tools available to aid in this Data-Driven Decision-Making process, and selecting the proper tool might be difficult for data scientists or data analysts.
Common questions include: how many people use tools, how easy it is to learn, how it is positioned in the market, and, if you are a company owner, the cost of ownership of such tools. The following are some of the most commonly used data analytics tools :-
Tools for Data Analysis
Python was created as an Object-Oriented Programming language for software and web development, but it was later improved for data research. Python is now one of the fastest-growing programming languages.
It is a sophisticated Data Analysis tool with a fantastic set of user-friendly libraries for all aspects of scientific computing. Python is a free, open-source programming language that is simple to learn.
Pandas, Python's data analysis library, was developed on NumPy, one of Python's first data science libraries. You can accomplish anything with Pandas! Data frames allow you to execute extensive data transformations and numerical analysis.
Pandas supports a variety of file formats; for example, data from Excel spreadsheets may be imported into processing sets for time-series analysis. (By definition, time-series analysis is a statistical approach that analyzes data acquired at regular intervals of time.)
Pandas is a very effective tool for data visualization, data masking, merging, indexing and grouping data, data cleaning, and many other tasks.
Excel is the most well-known spreadsheet program in the world. Furthermore, it has data-analysis-friendly computations and graphing capabilities. Excel is a must-have in any area, regardless of specialization or other software requirements.
Excel’s built-in capabilities are crucial, including pivot tables (for sorting or summarizing data) and form development tools. It also offers a number of other capabilities that help to speed up data manipulation.
For example, the CONCATENATE function combines text, integers, and dates into a single cell. SUMIF allows you to generate value totals depending on variable criteria, and Excel's search tool allows you to easily isolate certain data.
It does, however, have restrictions. For example, it operates quite slowly with huge datasets and has a tendency to approximate large numbers, resulting in mistakes. Nonetheless, it's a vital and powerful tool, and with a plethora of plug-ins accessible, you can easily overcome Excel's drawbacks. Begin with these five Excel formulas that every data analyst should know.
SAS is a statistical software package that is frequently used in business intelligence (BI), data management, and predictive analysis. SAS is proprietary software, and businesses must pay to utilize it. For students to learn and utilize SAS, a free university version has been provided.
SAS has a basic GUI, making it simple to use; yet, a strong understanding of SAS programming is required to utilize the tool. SAS's DATA phase (where data is produced, imported, changed, merged, or computed) assists inefficient data management and manipulation.
Jupyter Notebook is an open-source online tool for creating interactive documents. These use a combination of live code, mathematics, graphics, and narrative prose. Consider something similar to a Microsoft Word document, but significantly more dynamic and tailored exclusively to data analytics!
It's ideal for showing work as a data analytics tool: Jupyter Notebook is a browser-based programming environment that supports over 40 languages, including Python and R Programming.
It also connects with large data technologies such as Apache Spark (see below) and provides a variety of outputs such as HTML, pictures, videos, and more. However, it has constraints, just like any other instrument.
Jupyter Notebook documents have inadequate version control, and keeping track of changes is difficult. This means it's not ideal for development or analytics work (you should use a dedicated IDE for both), and it's not conducive to cooperation.
Because it is not self-contained, you must give any additional assets (e.g. libraries or runtime systems) to everyone with whom you share the document. However, it remains a useful data science and data analytics tool for presentation and educational purposes.
Power BI is available in three editions: Desktop, Pro, and Premium. Users can use the desktop version for free; however, Pro and Premium are paid editions. You can view your data, connect to several data sources, and distribute the results around your business. You can use Power BI to bring your data to life with live dashboards and reports.
Power BI interfaces with other tools, like Microsoft Excel, allowing you to get up and running fast and seamlessly with your existing solutions. According to Gartner, Microsoft is a Magic Quadrant Leader in analytics and business intelligence systems. Nestle, Tenneco, Ecolab, and other leading firms use Power BI.
Tableau is one of the greatest commercial data analytics tools available for creating interactive visualizations and dashboards without considerable coding knowledge. The suite handles vast volumes of data better than many other Business Intelligence tools and is extremely user-friendly.
It offers a graphical drag-and-drop interface (another definite advantage over many other data analysis tools). However, because it lacks a scripting layer, Tableau's capabilities are limited. For instance, it’s not great for pre-processing data or building more complex calculations.
While it does provide data manipulation features, they aren't very good. Before importing your data into Tableau, you'll usually need to perform scripting procedures in Python or R.
Despite its flaws, its visualization is rather good, making it quite popular. It's also mobile-friendly. Mobility may not be a necessity for you as a data analyst, but it is useful if you want to dabble on the go!
KNIME (Konstanz Information Miner), an open-source, cloud-based data integration platform, is the last on our list. It was created in 2004 by software developers at Germany's Konstanz University.
Although it was originally designed for the pharmaceutical business, KNIME's ability to aggregate data from several sources into a single system has led to its use in other fields. Customer analysis, business intelligence, and machine learning are examples of these.
Its biggest selling point (apart from the fact that it is free) is its usability. It is suitable for visual programming due to its drag-and-drop graphical user interface (GUI). This implies that users do not require a high level of technical competence to construct data pipelines.
While it promises to cover the entire spectrum of data analytics jobs, its true strength is in data mining. Although it provides in-depth statistical analysis, users will benefit from some Python and R experience.
As it is open-source, KNIME is highly adaptable and adjustable to the demands of any organization—all without incurring significant fees. As a result, it is popular among smaller firms with limited finances.
R is a prominent open-source programming language, similar to Python. It is often used in the development of statistical/data analysis applications. The syntax of R is more complicated than that of Python, and the learning curve is higher.
However, it was designed primarily for intensive statistical processing workloads and is widely used for data visualization. R, like Python, has a network of publicly accessible code known as CRAN (the Comprehensive R Archive Network), which has over 10,000 packages.
It works well with various languages and systems (particularly large data applications) and can access code written in languages such as C, C++, and FORTRAN.
On the other hand, it has poor memory management, and while there is a large user community to turn to for assistance, R lacks a dedicated support staff. However, RStudio is an outstanding R-specific integrated development environment (IDE), which is always a plus.
Without SQL consoles, our list of data analyst tools would be incomplete. SQL is essentially a computer language used to manage/query data stored in relational databases, and it is very useful at managing structured data as a database tool for analysts.
It is widely utilized in the data science community and is one of the analyst tools used in a variety of business cases and data scenarios.
The explanation is simple: because most data is kept in relational databases and you need to access and unlock its value, SQL is a vital component of corporate success, and analysts may gain a competitive edge by knowing it. MySQL, PostgreSQL, MS SQL, and Oracle are examples of relational (SQL-based) database management systems.
ETL is a procedure utilized by businesses of all sizes all over the world, and as a company expands, chances are you will need to extract, load, and convert data into another database in order to analyze it and generate queries.
There are three main types of ETL tools: batch ETL, real-time ETL, and cloud-based ETL, each having its own set of criteria and capabilities that cater to diverse business requirements.
These are the tools used by analysts that participate in more technical data management activities inside a corporation, with Talend being one of the greatest examples.
Also Read | Data Visualization Techniques
It can be concluded that, with a little practice, data analysis is simple. All of the tools will not be equally useful. It is beneficial to focus on one tool and become an expert in that tool.
Understanding data is critical for determining where we stand in terms of data analysis. Programming is not very crucial in data visualization and analysis. However, certain tools get you closer to programming.