‘Data’ is a word that has been around for a very long time, there is data everywhere you look. Have you ever wondered how so much content is handled by different data analytics tools and how it gets to the audience? Well, there are certain vital roles today in the industry, and out of it, a Data Analyst has the responsibility to gather insights from data. So, today we’ll list some of the tools that analysts find to be the most useful and popular. Let’s see how data analysis works. You may find the basic definition of data analysis in the flowchart below.
Definition of Data Analysis
I hope that was easy. Even though it seems straightforward, there is a lot of work involved in delivering the appropriate information to the right people. However, how can this occur? Data analysts use modern tools and technologies to make life for corporations simple. With so many alternatives accessible today, it can be difficult to choose a tool that is both simple to use and provides reliable results.
Why Are Data Analytics Tools Used?
How do we choose the best data analytics tools? Well, decisions made in the company these days are driven by statistics rather than the judgment of those in control. Data analysis helps remove obstacles in the way of boosting revenue and improving customer experience by providing insights into the precise issues being faced. To do this, a range of data analytics tools and methods are used.
In this blog, the most significant data analysis tools are listed that may be utilized to boost productivity. The tools for data analytics listed below are not presented in any particular sequence.
Top 10 Data Analytics Tools
- Apache Spark
- R Programming
- Power BI
Top 10 Data Analytics Tools
One of the easiest ways of keeping data, creating visualizations of data, computing data, cleaning data, and communicating with data in an understandable way is through using MS Excel. It is efficient as an application meant for collecting information, as well as one of the Microsoft Office tools quite commonly used. The newcomers in the industry always start with MS Excel which can become the most basic tool of a data analyst.
Microsoft Excel is an ideal starting point for analyzing the data. An amalgamation of opportunities for performing a statistical analysis is availed by Excel’s Data Analytics Tools set. Your data is well presented through Excel’s graph and chart.
Microsoft Excel for Data Analysis
Make sure that you emphasize such strength in your resume. If you want to understand more about using Excel as a data analysis tool, then feel free to take a free online course known as “Excel for Beginners”. It provides three major categories:
- At Home
- For Professional Use
- For Businesses
Out of all, the most powerful today is the ability to analyze data. Despite the fact that you can be proficient in the listed technologies, you could still demand Excel. It is one of the fundamentals as well as the most common tool used in that sector. On this note, pivot tables help in sifting through complex data and offer relevant information based on customers’ demands.
More refined analytical options that provide modeling capabilities. For the duration of the data science subject, the use of various Data Science tools has made the field of Data Science much more manageable.]
2. Apache Spark
The University of California developed this technology, an engine for processing large-scale data that operates Apache Hadoop programs. It has a reputation for high-speed processing. It can process 100 times faster on RAM and 10 times faster on disk in the application of data analytics compared to Hadoop.
Due to its processing efficiency for big data, many large enterprises with lots of unstructured data increasingly incorporate Apache Spark into their stack.
It could also allow simple data loading for managing SQL queries & machine learning.
Because of its unified design, it can incorporate many libraries and processing modalities.
It retrieves data from storage systems and runs computations on it.
Numerous functionalities are linked to Spark. It comprises libraries for stream processing, machine learning, graph analytics, and SQL as well as structured data.
Role of Apache Spark in the Big Data Industry
Data scientists and analysts can quickly analyze enormous data volumes with the use of the software architecture Apache Spark.It was donated to the charitable Apache Software Foundation when it was initially created in 2012. To examine unstructured large-scale data sets, a distributed analytics framework was created called Spark.
Spark is incredibly quick as there are other frameworks that are similar to it like Apache Hadoop. Spark framework makes use of RAM rather than local memory as It is approximately 100 times quicker than Hadoop. This framework is basically employed in the creation of machine learning models that require a lot of data.
It even features a library of machine learning methods called MLlib that includes, to name just a few, clustering, regression, and classification algorithms. The drawback of Spark’s high memory usage is its high computational cost. Additionally, it lacks a file management system, necessitating connection with other programs like Hadoop.
3. R Programming
R programming is an open-source programming language, much like Python. Software for statistical and data analysis is frequently made with it. Python’s syntax is simpler than R programming, but R’s learning curve is more challenging. R programming was created expressly to handle complex statistical computing tasks which was widely used for data visualization. R has a network of open-source software called CRAN (the Comprehensive R Archive Network), similar to Python which contains more than 10,000 packages.
The founders who created R programming an open source two of them Ross Ihaka and Robert Gentleman from the University of Auckland as their names both begin with the letter R, which inspired the name “R” for the programming language.
It can make use of code written in languages like C, C++, and FORTRAN and integrates well with other systems and languages (including big data software). The software’s drawbacks include inadequate memory management and the absence of a dedicated support staff, despite a helpful user base that may be tapped for assistance. RStudio is a fantastic IDE that is designed specifically for R programming, which is always a plus!
Features of R Programming
One of the most widely used languages for statistical modeling, data analysis, and visualization is R. It is a programming language that is open-source. Data manipulation is simple with the aid of R and packages like plyr, dplyr, and tidy. It is great with tools such as ggplot2, lattice, and plotly, among other things – for data analysis and visualization. Also, an extensive development community is there to help. It is freeware to install R.
One of the best data mining models, and a popular choice for complex statistical computations is R. Besides analyzing data, this is open source and free software also used in building programming tools and applications for easy statistical analysis.
One option is R which has a graphical user interface with capabilities for different analytical modeling tasks such as time series, linear and non-linear models, and data clustering just to mention a few. This is what most statisticians use since their graphs, equations, plots, and formulae for printing are already prepared. It is nonetheless one of the most used languages though it seems slightly more sophisticated than others.
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- Data Analytics With Power Bi
4. Power BI
Compared to other data analytics solution providers in the market, Power BI has just been around for the past few years (about 10). Developed initially as an Excel plug-in in the early 2010s, it has since been significantly upgraded to the current standard suite of corporate data analysis tools.
It is important to note that it takes very little time to learn how to develop compelling visual reports and dashboards for Power BI users. Cloud sources such as Google and Facebook analytics work perfectly well with Power BI.
It has some space for betterment but it offers excellent data visualization. To illustrate, the DAX proprietary language is not very user-friendly and the user interface is quite awkward. They also form very rigid equations. Nevertheless, it gives a few subscriptions with one being free.
It’s great for those who are looking to know more about the system itself but the free version imposes several limitations with the most notable one being the insufficient space (about 2GB).
POWER BI Products
Microsoft provides Power BI products that many data analysts use in creating interactive displays containing their business intelligence functions, abilities, and properties. Users can develop their own dashboards as well as share personalized reports through Power BI. The Power BI has easy data analytics for efficient connectivity, and data security over multiple office platforms, as well as easy integrations.
Being a powerful BI tool, it also works with different types of data sources. Integrating several dashboard and report apps to produce a single Power BI app would make deliveries easier for users of Power BI. Some of the products offered by PowerBIB include; PowerBIMobile, PowerBI Embedded, PowerBI Report Server, PowerBI Premium, PowerBIPro, and finally; PowerBIDesktop.
One of the most potent data analytics tools at the user’s disposal is Python, why? Because it is a free, open-source program and the first programming language that the majority of programmers choose to learn as it is simple and flexible. Python is a great programming language that supports object-oriented and has also a wide range of applications including a large number of packages and libraries which is why it is an essential tool for every data analyst.
In the computer industry, many programmers use Python widely as it places a higher priority on readability than on sophisticated languages. Pandas is the most popular data analytics library included with Python. The other libraries like NumPy and Pandas are excellent for supporting general data processing as well as streamlining highly computational workloads.
Python has packages like Matplotlib and Seaborn that assist you in representing data like pie charts, bar graphs, etc. which can also be used for sophisticated visualization.
Python Libraries for Data Analysis
The web may be scrapped for data using programs like Beautiful Soup, Scrapy, and Matplotlib which are great tools for reporting and data visualization. Python’s primary flaw is its speed; as it uses a lot of memory and is slower than many other languages. But generally speaking, Python’s advantages exceed its disadvantages if you’re creating stuff from scratch.
Both object-oriented programming and structured programming are supported by this high-level language. Whether we’re using Google, Instagram, or YouTube to browse the web, each of these programs uses Python for its programming requirements.
Python is advantageous for web development since it takes less time and has a straightforward syntax. Python offers support for a number of frameworks, including Flask, Django, Falcon, Web2Py, Sanic, and more. Let’s examine how as It offers modules like NumPy and Panda to make parallel data processing simpler. They can perform such difficult procedures because of Scikit-Learn. Python also uses the OpenCV library to process photos.
To build interactive visualizations and dashboards, Tableau is one of the best commercial data analysis tools which is incredibly user-friendly and handles massive volumes of data better than many other BI tools. Apart from many other data analysis tools, tableau’s visual drag-and-drop interface feature sets it all but it lacks in the scripting layer.
Among all of the various business intelligence products, it is regarded as the market leader. By enabling analysts to work on live data sets without investing a lot of effort in data wrangling, Tableau makes data analysis and visualization easier.
This tool is built on the “VizQL” core query language from Tableau. By converting the dashboard and visualization components into back-end queries, VizQL helps reduce the requirement for end-user performance enhancements.
Advantages of Tableau
Usually, Data analysts use Tableau, a business intelligence tool, to view, examine, and comprehend their data. Tableau can investigate a variety of data sources, including databases, spreadsheets, Hadoop data, and cloud services, and also offers quick analytics. It is simple to use and it requires less work to create excellent interactive dashboards due to its robust GUI. You can deal with real data without spending too much time on data manipulation with the help of Tableau.
Tableau is constantly working to enhance its offerings, and the most recent improvements provide customers with smart dashboards, data exploration, usability, quick analytics, automatic updates, and the ability to publish a dashboard for sharing live on mobile devices or the web. If you want to improve your knowledge of this data analysis tool, you can enroll in a free online tableau course.
Tableau Desktop, Tableau Server, Tableau Online, Tableau Reader, and Tableau Public are the products that fall under this category. The fact that Tableau is free is another benefit.
A self-service business intelligence platform called QlikView aims to increase business value by providing both technical and non-technical users with strong support for data exploration and discovery. It is moreover one of the technologies that supports both on-premises and cloud deployment.
Through efficient data integration, conversational analytics, data literacy, etc., Qlikview transforms unstructured data into a knowledge base. The quantity of data that may be used is unrestricted, and QlikView makes it easy to quickly integrate data from many sources.
Features of QlikView
A self-service solution for business intelligence, data visualization, and data analytics is called QlikView. It offers capabilities like Data Integration, Data Literacy, and Data Analytics in an effort to expedite the business value that can be obtained from data. Over 50,000 customers worldwide rely on QlikView, as it provides a wide range of items to its consumers, some of which can be tried out for free a month.
There are many capabilities for ad hoc queries that make decision-making fast & easy. It responds instantly as there are no data volume restrictions. QlikView is economical and cost-effective and also helps you make the best business decisions that require having access to information and trends.
Statistical Analysis System(SAS) is one of the most widely used BI tools that offers a variety of capabilities. SAS is a well-known commercial suite of business intelligence and data analysis tools that includes interactive dashboards, ad-hoc reporting, data exploration, data visualization, and easy and insightful analysis. The Statistical Analysis System (SAS) is increasingly used to solve issues in business intelligence (BI), data science, and machine learning.
In 1960, The SAS Institute created the data analytical tool Statistical Analysis System(SAS). Today, client profiling, reporting, data mining, and predictive modeling are its key uses. Software designed for the enterprise market is typically more reliable, adaptable, and simple to use for big businesses. This is due to the fact that their levels of in-house programming ability frequently vary.
SAS programming workflow
One of the most used statistical tools for data analysis is SAS. Here are a few crucial SAS characteristics:
- You can effortlessly keep tabs on business from anywhere, at any time, using the BI companion app, which is compatible with iOS and Android.
- Microsoft Office makes it simple for analysts to access SAS BI and produce and share reports and visualizations as needed.
- SAS is able to manage and optimize communications while also anticipating behavior.
However, SAS has a heavy price tag because it is a commercial product. Nevertheless, there are advantages to paying more; in response to client demand, additional modules are frequently added. It contains fewer of these, compared to, say, Python libraries, but they are quite targeted. For instance, it provides modules for niche applications like anti-money laundering and Internet of Things analytics.
Check here for the best data analytics courses:
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KNIME stands for Konstanz Information Miner which is an open-source data analytics tools and a cost-free platform used for reporting and integration for workflows based on a graphical user interface. It aids in data collection and the creation of models that are required for production and deployment. KNIME is designed for analytics on a GUI workflow.
Users can create and carry out machine learning or operation research-based experiments with KNIME, a no-code-based ETL and data analytics tools, to increase speed to value. KNIME is a platform used by data scientists who want to work with data for machine learning but lack programming experience.
KNIME offers two software options. Specifically, the KNIME server and analytics software. The first one allows you to design workflows, and reusable components, and clear out your data. The Knime server is deployed at the workplace for workflow development, automation, and team collaboration.
RapidMiner is a tool where one can integrate, cleanse, convert data, and then do predictive analytics. Many people use this platform often for modeling operations, data preparation, and machine learning.
Moreover, using R and Python scripts could also help in extending RapidMiner. A user-friendly graphic interface enables analysts to self-prepare data and run their models on their own terms. Further to that, the company’s marketplace contains numerous third-party plugins. In the 2020 Gartner magic quadrant, RapidMiner was positioned as a visionary.
FAQ – Frequently Asked Questions: Data Analytics Tools
Here are the FAQs regarding data analytics tools.
1. Is data analytics a technology?
Data analytics helps people and organizations understand how data is obtained, classified, and analyzed by a data analyst in the world of technology. Data analytics is actually not a technological system but has a way of extracting useful patterns, insights, and information from data.
2. The functions of data analytics tools.
By using data analytics tools, one can generate information about customers and locate patterns and trends so as to improve business decision-making processes and decisions taken by a company.
3. Where do we go from here in data analytics?
The use of contemporary technologies, such as blockchain, machine learning, and artificial intelligence, is sure to make the future of data analytics very promising. The impact of these emerging technologies is enough to revolutionize data.
4. The places where data analysis normally occurs.
Moreover, data analytics in common for banking and securities, government, media and entertainment, and several others require rapid demand for analytic solutions and tools.
5. What am I analyzing with my data?
The first step in this process is to review what type of data to analyze through data analytics tools before you actually begin using it. Do you consider this as quantity or quality data?
Converting quantitative data is usually numerical data that are stored in databases or spreadsheets and can easily be understood through the visual insights generated by BI tools such as Tableau and data analysis features of Microsoft Excel.
Nevertheless, qualitative data that include e-mails, social media conversations, and open-ended questionnaires often require AI data-analysis software.
Conclusion: Data Analytics Tools
For now, that is all we can talk about. This is the case as at this point it should be clear that data analysts require these technologies for handling the huge amount of data they get from different sources all over the world. With increasing numbers of customers, companies are employing any of the mentioned tools and seeking employees who can use those Data Analytics tools.
We sincerely hope this was useful to you. Do you aspire to succeed as a data analyst? Online courses in data analytics are widely available and many of them are free. Today’s free online courses from IIM Skills can help you advance your skills if you want to learn more about these ideas.