5 Best Data Analyst Tools in 2024 – Data Analytics Course

5 best data analyst tools in 2024 – data analytics course

There is a growing range of data analysis tools available as the field of data analytics evolves.
You might wonder: What data analysis tools do I need to learn if I’m considering a career in this field?

The purpose of this post is to highlight some of the key data analytics tools you need to know.
Also, you’ll learn about open-source tools and commercial software, along with their applications and pros and cons. Moreover, you’ll be at the forefront of the field and know 5 Best Data Analyst Tools in 2024 and Data Analytics Course.

The must-haves will be the first on our list, followed by some of the more popular tools and also platforms utilized by organizations of all sizes. By the end of this post, you’ll have an idea how to proceed if you’re preparing for an interview, or deciding which tool to learn next.

You will also learn all the basics of data analytics in BPA Educators’ short course on data analytics.

The data analysis tools we will cover are as follows:

  • Microsoft Excel
  • Python
  • SQL
  • Microsoft Power BI
  • Tableau

Microsoft Excel

A glance at Excel:

  • Tool type: Spreadsheet software.
  • Availability: It is commercial.
  • The most common use: Reporting and data wrangling.
  • Benefits : Used widely, with a wide range of useful features.
  • Drawbacks : Expensive, calculation errors, poor handling of big data.
The world’s most popular spreadsheet software is Excel. Additionally, it comes with calculation and graphing functions that are ideal for analyzing data.

No matter what your specialization is, or what software you may need, Excel is a staple. In addition to pivot tables (for sorting and totaling data), it includes forms creation tools.

Data manipulation can also be streamlined with its various functions. A single cell can contain text, numbers, and dates when you use the CONCATENATE function. Using SUMIF, you can create totals based on variable criteria, and Excel’s search function allows you to isolate specific data.

There are, however, limitations to it. Large datasets take a long time to run, and it approximates large numbers inaccurately, resulting in slow performance. It’s a powerful and important tool for data analysis, and you can easily override Excel’s shortcomings with many plug-ins. Moreover, here are 12 Excel formulas that every data analyst should know.

Get a list of Excel shortcuts
Excel shortcuts

Python

A glance at Python:

  • Tool type: Language for programming.
  • Availability: It is commercial.
  • The most common use: Data scraping, management, analysis, and reporting.
  • Benefits : It has a simple learning curve, is highly versatile, and is widely used.
  • Drawbacks : Memory intensive—doesn’t run as fast as some other languages.
Python is a programming language that can be used for a wide range of purposes, making it a must-have for data analysts.

It is simpler and more readible than more complex languages, and many programmers are already familiar with it because of its popularity in the tech industry.

The Python programming language is also incredibly versatile; it has a variety of libraries for various data analytics tasks. we can streamline highly computational tasks, as well as manipulate data, with NumPy and pandas libraries, for example.

The Beautiful Soup and Scrapy libraries are used to scrape data from the web, while Matplotlib is used to visualize and report the data. The main disadvantage of Python is its speed. It is memory-intensive and slower than many other languages. However, if you’re creating software from scratch, Python’s benefits far outweigh its shortcomings. Read our complete Python guide to learn more.

SQL(Structured Query Language) – Data Analytics

A glance at SQL:

  • Tool type: Relation Database Management System.
  • Availability: Free and open-source, with thousands of libraries available.
  • The most common use: Data scraping, analysis, and reporting.
  • Benefits : It has easy data retrieval, manipulation and security.
  • Drawbacks : Complex, costly and partial control.

SQL (Structured Query Language) has become a fundamental requirement in the world of data science. SQL empowers data scientists to query and analyze vast datasets efficiently as the most important part of data manipulation and analysis.

Most companies are moving towards a data-centric approach due to the high demand for Data Science professionals in IT. A Data Science degree with SQL can be a great career move.

The data is stored in a database and managed and processed by a Database Management System (DBMS), which simplifies and organizes our work. The SQL language is a fundamental component of DBMSs for data management. By enabling professionals to extract insights from large and complex datasets, it plays a vital role in data science workflows.

The Structured Query Language (SQL) helps to manipulate data. In databases, different operations can be performed on data, such as updating, deleting, creating, and altering tables, views, etc. It is standard for big data platforms and organizations to use SQL as their primary API for relational databases.

The study of data in its entirety is data science. In order to work with the database, we must extract the data from it, and SQL can help us do that. The management of relational databases is an essential part of data science. The data scientist can define and create the database as well as query it using SQL commands. Although many industries and organizations use NoSQL to manage their product data, SQL remains the preferred choice for many.

To know about the data analytics course of SQL click here

Microsoft Power BI

A glance at Power BI:

  • Type of tool: An analytics suite for business.
  • Availability: Free and commercial software (both are available).
  • Mostly used for: Visualization and predictive analytics, among other things.
  • Pros: Consistent updates, good visualizations, good data connectivity.
  • Cons: Clumsy user interface, rigid formulas, and limited data (free version).

It is a relatively new player in the data analytics tool market, having been around for less than a decade. The software was originally developed as an Excel plug-in, but was redeveloped as a standalone business data analysis suite in the early 2010s. A minimal learning curve is required to create interactive visual reports and dashboards with Power BI.

Due to its great data connectivity, it works seamlessly with Excel (as you would expect, since it is a Microsoft product) but also text files, SQL servers, and cloud sources.

There is also a strong data visualization feature, but there is still room for improvement in other areas as well. The user interface, formulas, and proprietary language (Data Analytics Expressions, or DAX) are not particularly user-friendly. However, it does offer several subscription plans, including a free one. Although the free version has drawbacks, such as the low data limit (around 2GB), this is great if you want to learn how to use the tool.

To know about data analytics course at BPA Educators click here

Tableau

A glance at Tableau:

  • Type of tool: A tool that visualizes data.
  • Availability: For commercial use
  • Mostly used for: Data dashboards and worksheets.
  • Pros: Excellent visualizations, speed, interactivity, and mobile support.
  • Cons: No preprocessing of data, poor version control.

Tableau is one of the best commercial data analysis tools if you need to create interactive visualizations and dashboards without extensive coding expertise. There is no doubt that the suite handles large amounts of data better than many other BI tools, and it is very easy to use. An additional advantage over many other data analysis tools is its visual drag-and-drop interface. Tableau is limited in its capabilities due to the lack of a scripting layer. When it comes to pre-processing data and creating complex calculations, it’s not the best tool.

There are some functions for manipulating data, but they aren’t very good. In most cases, you’ll have to use Python or R to script your data before importing it into Tableau. But despite its drawbacks, the visualization is quite good, making it a very popular tool. Furthermore, it’s mobile-friendly. Even if you don’t need mobility as a data analyst, it’s nice to have if you want to dabble on the go!

Data analysis tools: how to choose one (Data Analytics)

Okay, so you’ve got your data ready to go, and now you’re looking for tools to analyze it. Do you know how to choose the one that’s right for your business?

First, keep in mind that no single tool can solve all your data analytics problems. In this list, you may find one tool meets most of your needs, but a secondary tool may be needed for smaller tasks.

The second step is to determine exactly who will have access to the data analysis tools and what their business needs are. Are they primarily intended for data analysts and scientists, non-technical users who need an interactive interface, or both? This list includes tools that are suitable for both types of users.

The third step is to take a look at what the tool is capable of when it comes to data modelling. In addition to these capabilities, does the tool have the ability to perform data modelling, or will you have to use SQL or another tool in order to do this?

Fourth—and lastly! Consider the practical aspects of pricing and licensing. Several options are free, or have some free features (but will require licensing for the full product). The licensing or subscription model will be used for some data analysis tools. It is important to consider the number of users needed or the subscription length if you are only considering a project-by-project basis.

Steps to take next

As we explored in this post, we can use a variety of tools to analyze data. You need to understand that there’s no one tool that does everything. Data analysts should have a deep understanding of different software and languages.

Here, BPA Educators, data analytics tools are best for specific processes:

If you find a tool on this list that you weren’t aware of, why not learn more about it? Learn about the rest of these tools by playing around with open-source data analysis tools (they’re free, after all!)

Data analytics tools are helpful, at the very least, to know which organizations are using them. Learn more about data analytics with BPA Educator’s Data Analytics course.

The following articles will give you more insight into the industry:

Data analysis tools: how to choose one

Okay, so you’ve got your data ready to go, and now you’re looking for tools to analyze it. Do you know how to choose the one that’s right for your business?

First, keep in mind that no single tool can solve all your data analytics problems. In this list, you may find one tool meets most of your needs, but a secondary tool may be needed for smaller tasks.

The second step is to determine exactly who will have access to the data analysis tools and what their business needs are. Are they primarily intended for data analysts and scientists, non-technical users who need an interactive interface, or both? This list includes tools that are suitable for both types of users.

The third step is to take a look at what the tool is capable of when it comes to data modelling. In addition to these capabilities, does the tool have the ability to perform data modelling, or will you have to use SQL or another tool in order to do this?

Fourth—and lastly! Consider the practical aspects of pricing and licensing. Several options are free, or have some free features (but will require licensing for the full product). The licensing or subscription model will be used for some data analysis tools. It is important to consider the number of users needed or the subscription length if you are only considering a project-by-project basis.

Steps to take next (Data Analytics)

As we explored in this post, we can use a variety of tools to analyze data. You need to understand that there’s no one tool that does everything. Data analysts should have a deep understanding of different software and languages.

Here, BPA Educators, data analytics tools are best for specific processes:

If you find a tool on this list that you weren’t aware of, why not learn more about it? Learn about the rest of these tools by playing around with open-source data analysis tools (they’re free, after all!)

Data analytics tools are helpful, at the very least, to know which organizations are using them. Learn more about data analytics with BPA Educator’s Data Analytics course.

The following articles will give you more insight into the industry: