This Data Analytics tools blog will help you gain insight into the following topics:
What are Data Analytics tools used for?
These days business decisions made are not at all based on the intuitions of people in charge; rather, it is driven by data. Analyzing data gives insights into the exact problems faced and helps clear the roadblocks toward increased revenue and customer experience. A variety of Data Analytics tools and techniques help in achieving this. The most important ones are listed here in this blog.
Tools for Data Analysis
R is one of the best Data Analytics tools used for data mining, modeling, and heavy statistical computing. This free, open-source software is not just employed in analyzing data, but also creates software and applications that can perform statistical analysis seamlessly.
Additionally, R has a graphical interface, making it a viable choice while carrying out a wide range of analytical modeling, such as time series analysis, linear/non-linear modeling, and data clustering, among others. Most statisticians use this because of the ready-to-publish nature of its plots, graphs, equations, and formulae.
The language can come off as slightly complex compared to other tools of Data Analytics, but it still remains one of the most favored.
This high-level language supports both object-oriented programming and structured programming. Despite being a high-level language, it is the easiest to learn in the IT world.
Python is a good facilitator in data analysis. Let us see how:
- It provides libraries like NumPy and Panda, making it easier to process data with parallel processing.
- Not always do you get data in readily usable formats? Python uses libraries like BeautifulSoup and Scrapy to extract it from the Internet.
- Visualizing datasets makes analysis much easier than trying to figure out patterns by just staring at them. Matplotlib and Seaborn are some libraries that help you represent data in the form of pie charts, bar graphs, etc.
- Its capacity to compute incredibly complex mathematical problems makes it the go-to tool for Machine Learning enthusiasts. Scikit-Learn helps them do such complex operations.
- Python also processes images using the OpenCV library.
Statistical Analysis System (SAS) finds its application increasingly in Machine Learning, Data Science, and Business Intelligence (BI) problems. It is among the most popular BI tools that come with various features, including data visualization, data exploration, creating easy and insightful analysis, interactive dashboards, and ad-hoc reporting.
SAS is one of the most frequently used statistical tools for data analysis. Some important features of SAS are as follows:
- The iOS and Android-friendly BI companion app helps you easily monitor business anywhere, at any time.
- SAS BI can be easily accessed through Microsoft Office, where analysts can create and distribute reports and charts as per convenience.
- SAS can predict behaviors and manage and optimize their communications.
This University of California developed tool is a large-scale data processing engine, which executes applications in Hadoop. It is known for its speed in processing, where the applications are executed 100x faster in memory and 10x faster on disk than Hadoop.
Because Apache Spark can work with Big Data efficiently, a lot of big companies with large amounts of unstructured data are adopting it into their stack.
- It can support basic data loading to handle SQL queries and Machine Learning.
- It has a unified nature that allows it to combine different libraries and processing types.
- It loads data from storage systems and performs computation operations on them.
- Spark is associated with a wide range of functionality. It includes libraries for structured data and SQL, stream processing, Machine Learning, and graph analytics.
Now, let us read about Excel BI tools which are also one of the top Data Analytics tools used in industries today.
Analyzing data has become the most powerful skill these days. MS Excel might be the most primitive tool used among Data Analysts, but it makes it the go-to option for any beginner in the field.
- Even if you are an expert in all of the above tools, you might still need to use Excel. It is the most basic and popular tool used in the industry.
- It makes use of pivot tables to filter complex data and draws insights from them as per client requirements.
- It has advanced analytics options that help in modeling capabilities.
The world of Data Science has become much simplified because of the Data Science tools used in this field. The above-mentioned Data Analyst tools are just an iceberg in the sea of options we have. Sign up for our Data Analytics course to enter the world of Data Science effortlessly!
It is considered a market leader amongst all the business intelligence tools available. Tableau eases the analysis and visualization of data by allowing analysts to work on live data sets without spending much time in data wrangling. Tableau’s core query language “VizQL” forms the basis of this tool. VizQL helps minimize the need for end-user performance optimizations by translating the dashboard and visualization components into back-end queries.
Benefits of using Tableau:
- Intelligent dashboard
- Unrestricted data explorations
- Automatic updates
- Prompt analytics process and outcomes
The products offered by Tableau include Tableau Online, Tableau Public, Tableau Reader, Tableau Server, and Tableau Desktop. Out of these, Tableau Public is a free platform.
RapidMiner is a platform that allows users to perform data processing like integrating, cleaning, and transforming data, required before running predictive analytics. It is one of the commonly used platforms that facilitate Machine Learning, Data Preparation, and Model Operations.
Easy extensions of RapidMiner can be possible by using R and Python scripts. The simple to use graphic interface allows analysts to prepare data and run models on their own. Also, numerous third-party plugins are available through RapidMiner’s marketplace.
Benefits of using RapidMiner:
- Ensured transparency
- End-to-End platform
- Easy-to-use graphic interface
The products under the RapidMiner tool include Studio, GO, Server, Real-time scoring, Radoop.
Power BI is offered by Microsoft and is commonly used by data analysts for creating interactive visualizations through the business intelligence capabilities and features offered by Power BI. Users are free to create and share dashboards and reports all by themselves with the help of Power BI.
As an efficient business intelligence platform, it supports multiple data sources too. To facilitate simple distribution, Power BI allows users to combine many dashboards and reports into one single Power BI app.
Some of the products offered by Power BI include Power BI Mobile, Power BI Embedded, Power BI Report Server, Power BI Premium, Power BI Pro, Power BI Desktop.
Benefits of using Power BI:
- Ease of data analysis
- Effective data connectivity
- Data Protection across various Office platforms
- Easy integration
It stands for Konstanz Information Miner. KNIME is a no-code-based ETL and data analytics tool where users can design and execute machine learning or operation research-based experiments for improved speed to value. It is a free and open-source data analytics platform, also offering reporting and integration for analytics on a GUI-based workflow.
KNIME works with machine learning and data mining libraries with no or minimal programming requirements. Basically, data scientists who wish to work with data for Machine Learning and do not possess programming knowledge, use KNIME as a platform.
Benefits of using KNIME:
- No prior programming experience required
- End-to-end modeling
- User-friendly graphical interface
QlikView is a self-service business intelligence platform aimed at enhancing business value through well-built support for data exploration and discovery by both technical and non-technical users. It is also one of the tools that support both cloud and on-premises deployment.
Qlikview converts raw data into a knowledge base through effective data integration, conversational analytics, data literacy, etc. QlikView does not impose any restrictions on the amount of data to be used and facilitates prompt integration of data from multiple sources.
Benefits of using QlikView:
- Promotes insight-driven actions
- Easy analysis
- Faster ROI due to shorter time period
- Dynamic dashboards
- Simple-to-use platform
- Mobility and Scalability
Talend is an ETL tool used for data integration and is considered one of the most impactful and efficient ETL tools. Talend aims at providing solutions for data management, data preparation, data quality, data integration, and big data. It is also being considered as the upcoming market leader in Cloud and Big Data integration software. Talend is also known for providing services for enterprise application integration.
Some of the products offered by Talend include Talend cloud data integration, Talend Data Loader, Talend Data Fabric, Talend Pipeline Designer, etc.
Benefits of using Talend:
- Single Platform
- Accelerated development and deployment
- Sustainable and future proof
- Reduced development cost
Splunk is a software platform that works on machine-generated data which is gathered from various sources like websites and devices and helps users search, analyze, and visualize the data. One of the most important services offered by Splunk is the real-time processing of data. Also, the best part in the case of Splunk is that the input data can be in any format and from any source. Splunk offers a unified method for organizing and pulling in real-time insights from machine-generated data.
Splunk offers products like Splunk Free, Splunk Enterprise, and Splunk Cloud.
Benefits of using Splunk:
- Enterprise-grade capabilities
- Real-time insights
- Single platform
- Ease of deploying
IBM Cognos is another business intelligence platform but it has in-built AI tools. It is used specifically for drawing out hidden insights in the data and presenting the same in simplified language.
IBM Cognos is known for its full-fledged automated capabilities for data preparation, data cleansing, and aggregation of data sources. It is also known for quick data integration and data experimenting for analysis. IBM Cognos is web-based and is one of the powerful tools for data mining, monitoring, scorecarding, etc.
The IBM Cognos suite consists of many products such as Report Studio, Query Studio, Cognos Connection, Event Studio, and Analysis Studio.
Benefits of IBM Cognos:
- Unrestricted communication with third-party technologies
- Data Security
- Self-service functionalities
- Single access point for all functions
Looker, a part of Google Cloud Platform, is one of the budding BI tools for data visualization and analytics. It offers multiple services including embedded analytics, custom data applications, and workflow integration. The modeling language used by Looker is known as ‘LookerML’.
Looker is gaining popularity as it allows anyone in the organization to create reports and dashboards along with access to a unified data source. Looker is also easily compatible with mostly all the transactional databases.
Benefits of using Looker:
- Multi-cloud capabilities
- Available for tech as well as non-tech users
- End-to-end customized applications
- Entirely web-based platform
Jupyter Notebook is an open-source web application aimed at helping users to create and share documents. It can be run on both a browser and a desktop. Jupyter Notebook was formerly known as IPython Notebook and hence it still offers a wide variety of Python packages for its users.
It is always a favorite for many data scientists as it supports almost all sorts of data science tasks like data cleaning, exploration, data visualization, deep learning, machine learning, etc.
Benefits of using Jupyter Notebook:
- User-friendly and interactive environment
- Easy integration with multiple platforms
- Flexible to use
- Prompt data processing
With this, we have finally come to an end. In this blog, we discussed in detail the multiple tools like Tableau, R, SAS, Python, Power BI, Excel, etc, that are used in Data Analytics. By now, it is quite clear that to handle a large amount of data, these tools are required by data analysts. With a growing consumer base, companies have now resorted to all the above-discussed tools and are on the lookout for professionals skilled with these tools.