5 Essential Tools Every Data Analyst Must Have
Tags: database, datascience, datastructures, beginners
Published: 2023-10-26 12:27:40

#####(The beginners’ guide on the most important tools every data analyst cannot do without)


The evolution and growth of data analysis as an industry has been tremendous. This is thanks to the ever-growing need for data in today’s advanced digital age. Every business needs data to get insights, drive decisions, fuel innovations, and achieve success. However, data can be useless if it’s not properly gathered, visualized, analyzed, and processed. 

You need the right data analysis tool or software to utilize data properly. Since numerous tools are available today, the ability to find the essential ones makes you a good data analyst. 

This article will introduce you to the most important tools and software you cannot do without as a budding data analyst. It will also explain their pros and cons, how to apply them, how to determine what tool to use at any particular time, and their alternatives. 

#####Prerequisites
To get the most out of this guide, you should have a basic understanding of what data analysis is, the different types of data analysis, and what a data analyst does. If you are unfamiliar with these, I recommend taking some time to familiarize yourself before proceeding.

#The Tools You Must Have As A Data Analyst
The data analysis tools we’ll cover in this article are:
-	Microsoft Excel
-	R
-	Python
-	Tableau 
-	Power BI

##Microsoft Excel
[Microsoft Excel](https://www.microsoft.com/en-us/microsoft-365/excel) is arguably the most important data analysis tool today. Widely considered an ordinary spreadsheet software, the functionalities and capabilities that the software possesses are gravely overlooked. Microsoft Excel comes with built-in features, formulas, calculations, and functions that provide better data insights. 

Microsoft Excel helps you visualize, understand, and analyze data with ease. Businesses use Microsoft Excel to explore data, identify trends, initiate plans, develop ideas for growth, make better decisions, and even assemble everything into readable dashboards. 


![Excel](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/03w67t1qq29jfw319sb1.PNG)

####Usage and application
Data analysts, organizations, and individuals use Microsoft Excel for the following purposes:
-	Data entry and record-keeping.
-	Data analysis.
-	Financial management.
-	Project and inventory management. 
-	Reporting. 
-	Data visualization. 
-	Customer Relationship Management (CRM).
-	Scheduling, Time tracking, and Payroll.

####Pros 
Here are the pros of using Microsoft Excel in data analysis: 
-	Microsoft Excel is easy to learn and use. 
-	Microsoft Excel comes with numerous built-in functions and formulas. 
-	Microsoft Excel aids simple and complex calculations. 
-	Microsoft Excel can process vast amounts of data.
-	Microsoft Excel integrates seamlessly with other Microsoft software like Outlook, Word, and PowerPoint. 
-	Microsoft Excel allows data importation and exportation from several formats and sources.
 
####Cons
Here are the cons of using Microsoft Excel in data analysis: 
-	Microsoft Excel can be slow and unstable when working with complex data.
-	Microsoft Excel can present inconsistencies and errors during manual data entry, or when working with multiple worksheets and complex formulas. 
-	Microsoft Excel can be expensive to keep updated, especially for new data analysts. 


####Alternatives
Several other spreadsheet software that offer different benefits and features for data analysis are available. Some of the best ones include [Google Sheets](https://www.google.com/sheets/about/), [ClickUp](https://clickup.com/), [Zoho Sheet](https://www.zoho.com/sheet/), and [LibreOffice Calc](https://www.libreoffice.org/discover/calc/). 




##R
Alongside Python, `R` is a programming language necessary to excel in the field of data analysis. `R` is an open-source software that aids Statistics and Analytics. `R` is made up of inbuilt software facilities that help with graphical display, calculation, and data manipulation. `R` allows you to design visuals like graphs and plots to explain data. Data wrangling is also possible with `R` 


![R](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ex89nkkxo2epmt57dhjb.PNG)

####Usage and application
Data analysts, organizations, and individuals use `R` for the following purposes:
-	Hypothesis testing.
-	Descriptive and Bayesian statistics.
-	Regression and Time series analysis.
-	Non-parametric tests.
-	Creating charts, graphs, and plots.
-	Interactive data visualization.
-	Heat maps.
-	Geographic mapping.
-	Custom data visualization for reports and presentations.


####Pros
Here are the pros of using `R` in data analysis: 
-	`R` does not require licenses or fees since it is open-source. 
-	`R` is a perfect software for statistical analysis and data visualization.  
-	`R` comes with packages that help transform messy, disorganized data into a structured format. 
-	`R` can be integrated with other programming languages like Python and SQL.
-	`R` can easily import data from various file formats like CSV, and Excel, and export results to different formats.

####Cons
Here are the cons of using `R` in data analysis: 
-	`R`’s syntax and programming concepts may be difficult for some users to understand at first.
-	`R` may not be the best choice for big data processing, as certain operations can be slower.
-	`R` can be memory-intensive and may struggle to handle large datasets that exceed available RAM.
-	`R`’s open-source nature can raise concerns regarding data security, especially when dealing with confidential data.

####Alternatives
Several alternative programming languages and tools can be used for data analysis, in addition to `R`, or in place of it. Some of the best ones include [Matlab](https://www.mathworks.com/products/matlab.html), [RapidMiner](https://rapidminer.com/), [KNIME](https://www.knime.com/knime-home), and [Scala](https://www.scala-lang.org/).  



##Python
[Python](https://www.python.org/) is another programming language you will need to excel in the field of data analysis. Python is an open-source, high-level, interpreted language with an easy-to-understand syntax. Python has a Pandas library that helps you design and manage data structures. Python is great for data representation and data manipulation and can be used in probability and inferential statistics. With Python, you can interpret your data in graphs or charts. You can also evaluate data with Python, or use A/B testing to test a hypothesis. 


![Python](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ika8bx0tiiwezgp8bk81.PNG)


####Usage and application
Data analysts, organizations, and individuals use R for the following purposes:
-	Data collection and extraction.
-	Data cleaning and preprocessing.
-	Data analysis and exploration.
-	Machine learning.
-	Time series analysis. 
-	Data visualization and reporting. 
-	Data mining and wrangling. 
####Pros
Here are the pros of using Python in data analysis: 

-	Python has an easy-to-read and understandable syntax.
-	Python is a multi-purpose language that can also be used in other fields like web development, artificial intelligence, scientific computing, and more.
-	Python has a vast standard library that provides ready-made functions for various tasks.
-	Python is available on numerous platforms, including Windows, macOS, and Linux. 
-	Python can be used for both small scripts and large, complex applications.

####Cons
Here are the cons of using Python in data analysis: 
-	Python consumes a lot of memory space.
-	Python can be slow during large code execution because it is an interpreted language. 
-	Python frameworks are not the most robust. 
-	Python is not suitable for real-time applications because of its strict timing limitations. 

####Alternatives
Just like with R, so many other programming languages can be used in place of Python in your data analysis journey. From [Java](https://www.java.com/) to [C++](https://cplusplus.com/), [Matlab](https://www.mathworks.com/products/matlab.html) to [RapidMiner](https://rapidminer.com/), your options are numerous. However, you have to carefully research and pick one that will be easy to learn and suit your needs. 


##Tableau
[Tableau](https://www.tableau.com/) is arguably the number one data analysis tool when it comes to data visualization. Tableau allows you to create interactive and easy-to-understand dashboards without extensive coding knowledge. Tableau has a drag-and-drop functionality that is intuitive, allowing data analysts to create visuals that are compelling. 



![Tableau](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/gogqrd3qsfsu987wisdd.PNG)

####Usage and application
Data analysts, organizations, and individuals use Tableau for the following purposes:
-	Data visualization.
-	Data exploration.
-	Dashboard creation.
-	Data blending and integration.
-	Data storytelling.
-	Data aggregation and summary. 

####Pros
Here are the pros of using Tableau in data analysis: 
-	Tableau is very easy to understand and implement.
-	Tableau can connect to a wide variety of data sources, making it easy to integrate data from multiple databases, files, and cloud services.
-	Tableau has the ability to handle and visualize large datasets quickly, thanks to features like data extracts and in-memory processing.
-	Tableau is highly scalable and can scale with an organization's growing data and user needs. 
-	Tableau has tight security features like data source encryption and authentication that protect sensitive data.

####Cons
Here are the cons of using Tableau in data analysis: 
-	Tableau can be expensive, especially for large enterprises, as licensing and subscription costs can add up. 
-	Tableau’s more advanced features can be difficult to master.
-	Tableau’s built-in analytics capabilities are not as robust as the other tools in this guide, like R or Python.
-	Tableau is not the best tool for complex statistical analysis. So, you may need to export data to other tools.
Alternatives
Tableau is a powerful data visualization and business intelligence tool, but several alternatives are available. Some of them include [QlikView](https://www.qlik.com/us/products/qlikview), [Looker](https://cloud.google.com/looker), [Sisense](https://www.sisense.com/homepage/), and [Metabase](https://www.metabase.com/). 


##Power BI
Another Microsoft product on this list, Power BI is an amazing software for business analytics. It allows you to create interactive dashboards and visual reports without any coding expertise. Power BI connects easily with Microsoft Excel and other data sources, so it allows you to prepare and transform data with ease. Power BI is designed to help individuals and businesses gain insights from their data and make data-driven decisions.



![Power BI](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/cx7mgaxkyhvi3vqaepai.PNG)

####Usage and application
Data analysts, organizations, and individuals use Power BI for the following purposes: 
-	Data visualization and reporting. 
-	Data exploration and analysis
-	Business intelligence. 
-	Data integration and data cleaning. 
-	Data storytelling. 

####Pros
Here are the pros of using Power BI in data analysis: 
-	Power BI has an intuitive and user-friendly interface. 
-	Power BI allows users to create a wide variety of charts, graphs, and custom visuals.
-	Power BI can build interactive dashboards that provide real-time insights.
-	Power BI has a ‘Q&A’ feature that allows you to ask language questions and receive data-driven responses and visualizations.
Cons
Here are the cons of using Power BI in data analysis: 
-	Power BI’s licensing costs can be relatively high, especially for large organizations, and may limit accessibility for some users.
-	Power BI may be difficult to export data from Power BI to other formats.
-	Power BI needs additional expertise and configurations to analyze real-time data. 

####Alternatives
Like Tableau, some alternatives to Power BI include [Domo](https://www.domo.com/), [QlikView](https://www.qlik.com/us/products/qlikview), [Looker](https://cloud.google.com/looker), [Sisense](https://www.sisense.com/homepage/), [Yellowfin BI](https://www.yellowfinbi.com/), and [Metabase](https://www.metabase.com/).

##Choosing a data analysis tool
Data analysis has no particular rule or format to follow when choosing a tool. You have to consider factors like data type and complexity, data size, the scope and purpose of analysis, and your expertise at that particular time. Do not rush into making a decision; check for the pros and cons of various tools, weigh their alternatives, try some of them out, assess your goals and needs, then ask industry experts and colleagues. Doing all of these will enable you to determine the most essential functions and features necessary for your project. You will also discover the compatibility and suitability of the different tools while learning from the recommendations and experiences of experts. 

##Conclusion
In this article, we have highlighted the most important tools you need as a data analyst to perform tasks effectively. You should always remember that an all-purpose, all-encompassing tool, does not exist. You need a combination of the tools listed here, and some of their alternatives. Combining them correctly will help you make better data-driven decisions, separating you from the average data analyst.