As the subject of data analytics develops, so does the number of data analysis tools available. If you’re thinking about a career in data analytics, you’ll want to know: What data analytics tools should I learn?
In this article, we’ll go over some of the most important data analytics tools you should be aware of and why. You’ll get a fast review of everything from open-source tools to commercial software, including its uses, benefits, and drawbacks. Are you short on time? Then have a look at this four-minute overview of the most widely used data analytics tools.
Excel is the most well-known spreadsheet programme in the world. Furthermore, it has data-analysis-friendly calculations and graphing utilities. Excel is a must-have in any area, regardless of specialisation or other software requirements. Pivot tables (for sorting or totaling data) and form creation tools are among its many useful built-in features. It also offers a number of other features that make data manipulation easier. The CONCATENATE function, for example, lets you mix text, numbers, and dates in a single cell. SUMIF allows you to construct value totals based on variable criteria, and Excel’s search tool allows you to isolate certain data quickly.
It does, however, have restrictions. For example, it is slow when dealing with huge datasets and has a tendency to approximate enormous numbers, resulting in
R has become one of the industry’s most widely known analytics tools. It has surpassed SAS in usage and is now the preferred data analytics tool, even for companies that can afford SAS. R has grown in strength over the years. It handles large data sets much better than it did even a decade ago. It has also become far more adaptable. There are some concerns about the sheer number of packages, but this has undoubtedly expanded R’s capabilities. R also integrates well with a wide range of Big Data platforms, which has contributed to its popularity.
SAS is still one of the most widely used data analytics tools in the industry. The SAS Institute’s pricing flexibility has aided its cause. SAS remains a robust, versatile, and simple-to-learn tool. SAS has introduced a slew of new modules. SAS Analytics for IoT, SAS Anti-money Laundering, and SAS Analytics Pro for Midsize Business are some of the recently added specialised modules.
Since its inception, Python has been one of the most popular programming languages. The main reason for its popularity is that it is a simple to learn language that is also quite fast. However, with the development of analytical and statistical libraries such as NumPy, SciPy, and others, it has evolved into one of the most powerful data analytics tools. It now covers a wide range of statistical and mathematical functions.
Tableau is one of the most user-friendly data analytics tools, capable of slicing and dicing your data as well as creating stunning visualisations and dashboards. Tableau can produce better visualisations than Excel and handle significantly more data than Excel. Tableau is unquestionably the way to go if you want interactivity in your plots.
Microsoft Power BI
Microsoft Power BI is a leading business intelligence platform that supports a wide range of data sources. This data analytics software allows users to create and publish reports, displays, and dashboards. Users can combine a collection of dashboards and reports into a Power BI app for quick delivery. By combining Machine Learning with Azure Machine Learning, Power BI enables users to create and implement automatic models.
Jupyter Notebook is one of the powerful free, open-source online data analytics tools that can be managed in a browser after installation with the Anaconda platform or Python’s package manager, pip. It allows developers to create reports using Live Code Data and views. This data analytics software is compatible with over 40 programming languages. Jupyter Notebook, formerly known as IPython Notebook, was created in Python. It enables developers to use Python’s extensive analytics and visualisation packages. The tool has a large user base that includes people who speak other languages.
Pig and Hive
Pig and Hive are key data analytics tools in the Hadoop ecosystem that help to simplify the creation of MapReduce queries. Both of these languages are similar to SQL (Hive more so than Pig). Pig and/or Hive are used by the majority of companies that work with Big Data and use the Hadoop platform.
RapidMiner is a powerful integrated data science platform created by the same company that performs predictive analysis as well as other advanced analytics such as data mining, text analytics, machine learning, and visual analytics without the need for programming. RapidMiner can connect to any data source, including Access, Excel, Microsoft SQL, Teradata, Oracle, Sybase, IBM DB2, Ingres, MySQL, IBM SPSS, Dbase, and others. The tool is extremely powerful, as it can generate analytics based on real-world data transformation settings, allowing you to control the formats and data sets for predictive analysis.
Apache Spark is an open-source cluster computing framework that is used for real-time processing and is one of the most successful projects in the Apache Software Foundation. As the most active Apache project at the moment, it comes with a fantastic open-source community and a programming interface. This interface ensures fault tolerance as well as implicit data parallelism.
KNIME A team of software engineers at the University of Konstanz created it in January 2004. KNIME is a leading open source, reporting, and integrated analytics tool that allows you to visually analyse and model data. It integrates various components for data mining and machine learning through its modular data-pipelining concept.
QlikView has many unique features, such as patented technology and in-memory data processing, which delivers results to end users quickly and stores data in the report itself. In QlikView, data associations are automatically maintained and can be compressed to nearly 10% of their original size. Colors are used to depict data relationships; one colour is assigned to related data and another to unrelated data.
Without SQL consoles, our list of data analyst tools would be incomplete. SQL is essentially a programming language used to manage/query data stored in relational databases, and it is especially effective in handling structured data as a database tool for analysts. It is widely used in the data science community and is one of the analyst tools used in a variety of business cases and data scenarios. The reason is simple: because most data is stored in relational databases and you need to access and unlock its value, SQL is a critical component of business success, and analysts can gain a competitive advantage by learning it.
Google Data Studio
Google Data Studio is a free dashboarding and data visualisation tool that integrates with the majority of other Google applications, including Google Analytics, Google Ads, and Google BigQuery. Data Studio is ideal for those who need to analyse their Google data because of its integration with other Google services. Marketers, for example, can create dashboards for their Google Ads and Analytics data to gain a better understanding of customer conversion and retention. Data Studio can also work with data from other sources, as long as it is first replicated to BigQuery using a data pipeline like Stitch.
Qlik offers a self-service data analytics and business intelligence platform that can be deployed in the cloud or on-premises. The tool is well-supported for data exploration and discovery by both technical and nontechnical users. Qlik supports a wide range of charts, which users can personalise using both embedded SQL and drag-and-drop modules.