Data analytics tools are used to perform various tasks like data mining, organization, processing, etc. You must know about these tools to perform data analytics without any issues.
Data Analytics Tools: Data analytics refers to the process of deriving meaningful insights from raw data. This process involves different stages like data collection, classification, cleaning, organization, storage, and processing. Manual operations will consume a lot of time and effort. Also, you won’t be able to take real-time actions based on insights if you rely on a manual process. Here is where sophisticated data analytics tools come into the picture. In this article, we will discuss the top data analytical tools that you should use to analyze data successfully.
Top Data Analytics Tools for Successful Analysis
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Microsoft Excel
Microsoft Excel may seem to be a traditional spreadsheet software at first glance. However, it is still very useful for daily business operations including data analysis.
The graphic functions and complex calculations can be easily managed with this tool. The pivot tables allow you to sort data. It also possesses various functions to manipulate data.
For example, using Excel’s search function, you can separate specific data. Yet, there are some drawbacks to using Excel. Firstly, it operates huge datasets slowly. Thankfully, you can use plugins to get rid of these limitations.
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Python
Python is a powerful programming language that every data analyst must know. With its vast range of resource libraries, you can perform various data analysis-related tasks.
For instance, NumPy can be used to manipulate data and it also performs complex calculations with ease. Matplotlib, which is one of its libraries, is excellent for reporting and data visualization. These features make it one of the best data analytics tools.
It is also useful for those who want to learn AI (Artificial Intelligence) and ML (Machine Learning). Both these technologies require you to solve complicated algorithms. With a simple yet powerful programming language like Python, you can perform these tasks with ease.
Also read: Python for Data Analysis
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R
R is also an open-source programming language like Python. It is slightly more complicated than Python. Yet, you can manage statistical computations with ease if you are well-versed in R.
R can be integrated with C++, C, and other programming languages. It is also used for data visualization. Sadly, there is no support team that works dedicatedly for R users. However, you can get immediate help from the community. Its memory management ability is also not at par with other programming languages. Despite these limitations, it continues to be one of the most used data analytics tools.
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Jupyter Notebook
With Jupyter Notebook, you can combine text, codes, visual elements, and equations with ease. It is mostly used as an interactive document creation tool. It is specifically designed as a data analytics tool and helps you showcase your work to your clients.
However, it lacks when it comes to collaboration and tracking changes. If you share it with others, you will also have to provide them with assets like runtime systems and libraries. Yet, it works perfectly for presentations and providing tutorials to aspiring data scientists!
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Apache Spark
You can use Apache Spark for processing huge data sets. Data analysts and scientists use it to perform heavy analytical tasks and computations. It is also a fast software framework and preferred over Hadoop for developing machine-learning models that contain huge amounts of data.
It possesses clustering, regression, and classification algorithms. Yet, it consumes memory quickly and also does not have a file management system. So, it has to be integrated with software like Hadoop to manage memory efficiently.
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SAS
Statistical Analysis System i.e. SAS is a suite comprising multiple data analytics tools. It also comprises business intelligence capabilities and is mainly used to create customer profiles, data mining, and predictive analysis.
SAS is a robust enterprise software that was designed specifically for large enterprises. So, it is on the costlier side. Yet, the consistent addition of new modules make it worthy of its price. Though it does not have a vast range of libraries like Python, its libraries are focussed and user-oriented. It also offers modules for specific tasks like analyzing IoT systems and detecting money-related online frauds. All these features make it one of the best data analytics tools in the market.
Also read: Data Analysis: Importance, Types, Methods of Data Analytics
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Microsoft Power BI
Microsoft Power BI is a business analytics suite that offers various data analytical tools. You can create dashboards and visual reports by using it. You can also integrate it with Excel, SQL server, Facebook Analytics, and some other cloud sources.
However, its user-friendly is on the bulkier side. The formulas can be rigid to use and DAX (Data Analytics Expressions) which is its proprietary language is not very user-friendly. Despite its limitations, it has emerged to be one of the popular data analytics tools. However, with a data limit of only 2 GB, it is not meant for heavy tasks and usage.
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Tableau
Tableau is a commercial data visualization tool which is mainly used to design interactive dashboards and visualizations. Unlike other BI (Business Intelligence) tools, it can manage vast amounts of data. Also, it is much easier to use. Its drag-and-drop feature makes it one of the best data analytics tools in the market.
However, it may not be great at performing complicated calculations or pre-processing data. It is not as great at data manipulation. You will have to execute scripting functions with R or Python before fetching data into this suite. Yet, it stands out due to its visualization features. You can also use it on your smartphone.
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KNIME
Konstanz Information Miner is a data integration platform that was developed in 2004 for the pharmaceutical industry. However, its ability to collect data from diverse sources and combine them into a single system has made it useful in other industries too. It is widely used in machine learning and customer analysis these days.
Its drag-and-drop function makes it perfect for visual programming. Though it boasts of a wide range of data analytics features, its main strength is data mining. If you want to use KNIME for statistical analysis, you must have a prior knowledge of R and Python. It is popular among small businesses as it is a cost-friendly option for them.
These were some of the best data analytics tools that successful data analysts use. You can choose the ones that suit your requirements and solve your data analysis issues.
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FAQs
Which are the four types of analytics?
Predictive, prescriptive, diagnostic, and descriptive are the four types of analytics. Descriptive analytics is used to understand historical data whereas diagnostic analytics is done for root cause analysis. Predictive analytics is used to predict future events whereas prescriptive analytics suggests actions based on previous events.
How to choose a data analytics tool?
Consider factors like your business requirements, budget, UI (User Interface), and integration while choosing a data analytics tool. Also, analyze whether it can be scaled easily as your business grows.
Is SQL a data analytics tool?
Though SQL (Structured Query Language) is a programming language, its data manipulation ability makes it suitable for data analytics as well.
Which is the most used data analytics tool?
Microsoft Excel is perhaps the most widely used data analytics tool because of its ability to analyze and interpret data in different ways.
Which are the primary data analysis methods?
Quantitative analysis and qualitative analysis are the primary data analysis methods.
What is Tableau used for?
Tableau is used for creating visual stories, dashboards, charts, graphs, etc. It is mainly used for analyzing and visualizing data to help businesses while making decisions.
Is Google Analytics a data analysis tool?
Google Analytics is a service that offers analytical and statistical tools for marketing and SEO (Search Engine Optimization) purposes.
How to perform statistical analysis by using Microsoft Excel?
Select a cell from the data range and click on the ‘Analyze Data’ button. MS Excel will analyze the data and generate visuals based on it.
Which are the three basic methods of data representation?
Textual, diagrammatic, and tabular methods are the three basic methods of data representation.
Which are the advanced data analysis methods?
Data mining, forecasting, visualization, machine learning, semantic analysis, cluster analysis, sentiment scoring, neural networks, and graph analysis are some of the advanced data analysis methods.
