Best Data Analytics Tools 2023 | Top 7 List

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Last Updated on October 19, 2022

Are you looking for the best data analytics tools for data analysis in other to enhance your skills and add up to your professionalism? You are in the right place. In this article, we’ve listed the best data analytics tools to use.

Data analysts need data analytics tools to ensure them produce the best results of what they want in several tasks like preparing data, executing algorithms, automate processes, and generate predictions.

Before we delve into the list of the best analytics tools, let’s have a clear insight on what data analytics tools are, and also features to look out for in other for you to choose which of them can be used to exhibit your work or perhaps build a career.

What Are Data Analytics Tools?

Data analysis tools are software and programs that collect and analyze data about a business, its customers, and its competition in order to improve processes and help uncover insights to make data-driven decisions. These tools help reduce the risks inherent in decision-making by providing useful insights and statistics which can be presented in charts, images, tables, and graphs.

Note that all data analytics tools have their own differences (different functions) with when and how they are used. So you need to be guided in other to have a balanced idea of which tools to use. This is as important as your work (data).

Having known what data analytics tools are, let’s lead you to the best data analytics tools for data analysis that will meet up your need or demand.

7 Best Analytics Tools For Data Analysis

1. Microsoft Excel

Microsoft Excel is spreadsheet software mostly used for data wrangling and reporting. It is commercial application software.

It is one of the best data analytics tools and the world’s best known spreadsheet software.

Its major features are calculations and graphing functions with, in-built features which includes pivot table and form creation tools. The concatenate function allows you to combine text, numbers, and dates into a single cell, SUMIF allows you create value totals based on variable criteria, and Excel’s search function makes it easy to isolate specific data. What an amazing feature. 

Its advantage is that, it is widely used with lots of useful functions as we’ve stated. But the disadvantage is that Excel runs very slow when handling large data and sometimes performs calculation error leading to inaccuracies.  Notwithstanding, it is an amazing tool for data analysis because of its useful functions and available plug-ins.

2. Jupyter Notebook

Jupyter Notebook is interactive authoring software mostly used for sharing code, creating tutorials and presenting work. It is an open-source application which combines codes, equations, visualizations and narrative text.

The advantage of this tool is that it is language dependent ( it supports over 45 languages including Python and R). It offers outputs from HTML to images, videos and many others as well as integrating with big data analysis tools like Apache Spark.

 Its disadvantage is that it has poor version control and tracking changes is not intuitive.  Never the less, it remains one of the best data science and data analytics tool for presentation and tutorial purposes.

3. Python

Python is a programming language software tool used for data scraping and reporting. It is an open-source application with thousands of free libraries like Bumpy, pandas, Soup, Scrapy, Matplotib.

Python is versatile that is, it has a huge range of resource libraries suited to a variety of different data analytics tasks as well as supporting general data manipulation.

The advantage of Python is that it’s easy to learn, widely-used because of it’s versatility. But it disadvantage is that it is slower than some languages and the memory is intensive.

None the less, Python is one of the best if you’re building software from scratch.

4. R

R is a programming language tool used for statistical analysis and data mining. It is as open-source application like Python but more complex. It deals with heavy statistical computing tasks and data visualization.

Just like Python, R also has a network called CRAN ( the Comprehensive R Archive Network) which offers 0ver 12,000 packages.

The advantage of R is that the platform is independent and highly compatible with lots of packages. But it disadvantage is that it has poor memory management which is slower, less secured and more complex to learn than Python. None the less RStudio is very recommending.

5. MICROSOFT POWER BI

BI is a business analytics suite tool used for data visualization to prescribe analytics. It is a commercial application software. BI allows users to create interactive visual reports and dashboards, with a minimal learning curve. Its attractive feature is its great data connectivity. It has free data subscription with serveral other subscriptions. This tools is more or less like Microsoft excel but it text files, SQL server, and cloud sources, like Google and Facebook analytics.

The advantage of Microsoft power BI is that it has a great data connectivity and good visualizations. But it’s disadvantage is that it has low data limit of 2GB in the free version with rigid formulas and a large user interface. Most times, it is not user-friendly

6. Tableau

Tableau is a data visualization tool used to create data dashboards and worksheets. It’s commercial application software which handles large amounts of data.

One of its major advantages is that it has a visual drag and drop interface. It has high memory speed with mobile support. But its disadvantage is that it has poor version control and can’t do more complex calculations. None the less, Tableau is one of the best commercial data analysis tools available.

7. Apache Spark

Apache spark is data processing framework tool used for big data processing and machine learning. It is open-source application software that allows data analysts and data scientist to process broad data sets. It also distributes heavy analytics tasks across many computers.

The advantage of apache spark is that it has a large RAM that is; the memory is fast and can store as many data as possible. It is as well easy to use.

It has a library of machine learning algorithms, MLlib, including classification, regression, and clustering algorithms. But its disadvantage is that it occupies so much space on the memory with rigid user interface. There is no file mangement system and that is why it integrates with other software like Hadoop. None the less, it is one of the most used tools for data analysis.

Conclusion

Before you go for any of the data analytics tool, you should have known your aim and what tools is essential at the point of need. Above is the list of the best data analytics tool for data analysis with their features, advantages and disadvantages. Do well to follow them closely and I assure you the best of your results.


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