Data is the new oil. It’s not hard to see why. Recruiters are scrambling for qualified candidates due to the increasing importance and demand for data analytics from companies around the globe. Data analytics can seem daunting. The sheer number of tools available doesn’t make it easier. Which one should you choose? While some open-source tools do better work than others, premium ones are more reliable and offer higher quality. Here’s a list of the Top 10 Data Analytics Tools, according to their popularity, performance, and learning.
Top 10 Data Analytics Tools
1. R Programming
R is the most popular analytics tool in the sector and is used extensively for data modeling and statistics. It is able to manipulate data and present it in various ways. It surpasses SAS in many aspects, including data capacity, performance, and results. R can be run on a variety of platforms, including Windows, macOS, and UNIX. You can browse packages by category and it has 11,556 packages. R provides tools that automatically install packages according to user requirements. This can also be done with Big data.
Also read: Unlimited Guide Data Analytics for Beginners
2. Tableau Public
Tableau Public is a free tool that allows you to connect any data source. It can connect to corporate Data Warehouse, Microsoft Excel, or web-based data. You can also create data visualizations, maps, and dashboards. You can also get real-time updates via the internet. These files can be shared on social media with your clients or sent to them. You can also download them in multiple formats. It shines when there is a reliable data source. This is when Tableau really shines. Tableau’s Big Data capabilities are why it is so important. Tableau’s ability to analyze and visualize data is superior to any data visualization tool on the market.
3. Python
Python is an object-oriented scripting language that is simple to understand, write, maintain, and modify. It is also open-source and free. Guido van Rossum developed it in the late 1980s. It supports both structured and functional programming.
Python is simple to learn because it is similar to JavaScript and Ruby. Python also has excellent machine learning libraries, viz. Scikitlearn and Tensorflow are also available. Python’s unique feature is its ability to be assembled on any platform, such as an SQL server or MongoDB database. It is also capable of handling text data very well.
4. SAS
SAS is a language and programming environment for data manipulation. It is an industry leader in analytics. The SAS Institute developed it in 1966. It has been further developed over the years in the 1980s, 1990s, and now in its current form. SAS is easy to use, manageable and can analyze any data source. SAS launched a wide range of products for customer intelligence in 2011, as well as many SAS modules for marketing analytics, web, and social media. These products are used to profile customers and prospects. This can be used to predict customer behaviors and manage and optimize communications.
5. Apache Spark
Apache was developed by the University of California Berkeley’s AMP Lab in 2009. Apache Spark is a large-scale, fast data processing engine that executes Hadoop clusters 100x faster in memory than on disk. Spark is built around data science, and the concept makes data science easy. Spark is popular for machine learning models and data pipeline development.
Spark includes a library – MLlib – that contains machine algorithms for repetitive data science techniques such as Classification, Regression, and Collaborative Filtering.
6. Excel
Excel is an extremely popular, basic, and widely-used analytical tool that can be used in almost every industry. Excel is essential, regardless of whether you are an expert user of R, Sas, or Tableau. When analytics are required on clients’ internal data, Excel is essential. Excel analyzes complex tasks and provides a preview of the pivot table that allows you to filter the data according to your requirements. Excel offers advanced business analytics that allows you to model. Excel has prebuilt options such as automatic relationship detection, creation of DAX measurements, and time grouping.
7. RapidMiner
RapidMiner is an integrated data science platform that can do powerful things. RapidMiner is a powerful integrated data science platform that can perform predictive analysis, text analytics, machine learning, and visual analytics. RapidMiner supports any data source, including Access, Excel and Microsoft SQL, Tera data as well as Oracle, Sybase IBM DB2, Ingres MySQL, IBM SPSS, Dbase, Sybase, Sybase, and IBM DB2. RapidMiner is powerful enough to generate analytics based on real-life data transformation settings. You can set the data formats and data sets that you want to use for predictive analysis.
Also read: Top 10 Data Preparation Tools and Software in 2022
8. KNIME
KNIME was developed by software engineers from the University of Konstanz in January 2004. KNIME, a leading open-source reporting, and integrated analytics tool allow you to model and analyze data using visual programming. It integrates several components that allow data mining and machine learning via its modular data pipelining concept.
9. QlikView
QlikView offers many unique features, such as patented technology and in-memory data processing. This allows the user to quickly access the result and save it in the report. QlikView can compress data to less than 10% of its original size. Data associations in QlikView are automatically maintained. The colors used to visualize data relationships are: one color is assigned to related data, and another to non-related data.
10. Splunk
Splunk analyzes and searches machine-generated data. Splunk can pull in text-based log data, and provide a simple way for users to search it. The user can pull all types of data and perform statistical analyses. They can also present the data in different formats.
These tools can help you to supercharge any data-related task that you can imagine. We hope that you will find the right tool from all the many.
