![Analytics Tools](https://inksem.com/wp-content/uploads/2023/07/analytics.png)
website analytics, social media analytics, or business intelligence tools, we will explore the diverse range of available tools, highlighting their role in helping businesses make data-driven decisions and achieve their goals.
Excel at a glance:
Python at a glance:
R glance:
Jupyter Notebook at a glance:
Apache Spark at a glance:
SAS at a glance:
Power BI at a glance:
Tableau at a glance:
KNIME at a glance:
In today’s data-driven world, analytics tools have become indispensable for businesses aiming to gain insights, make informed decisions, and drive success. These tools empower organizations to collect, analyze, and interpret vast amounts of data from various sources, enabling them to uncover valuable trends, patterns, and opportunities. In this blog, we will explore the realm of these tools, delving into their functionalities, benefits, and how they can be leveraged to unlock valuable insights and drive growth. Whether it’s Best Data Analytics Tools for Data Analysts in 2024
In this post, we’ll highlight some key data analytics tools you need to know and why. From open-source tools to commercial software, you’ll get a quick overview of each, including its applications, pros, and cons. Even better, a few of those on this list contain AI data analytics tools, so you’re at the forefront of the field in 2024. We’ll start our list with the must-haves—the data analysis tools you can’t do without. Then we’ll move on to some of the organizations’ more popular tools and platforms. Whether you’re preparing for an interview or are deciding which tool to learn next, by the end of this post, you’ll have an idea of how to progress. Also read : Lead Generation : Best Ways To Improve Your Client Base1. Microsoft Excel
![Microsoft Excel as Analytics tool](https://inksem.com/wp-content/uploads/2023/10/ms-excel-logo.png)
- Type of tool: Spreadsheet software.
- Availability: Commercial.
- Mostly used for: Data wrangling and reporting.
- Pros: Widely used, with lots of useful functions and plug-ins.
- Cons: Cost, calculation errors, poor at handling big data.
2. Python
![python programming language](https://inksem.com/wp-content/uploads/2023/10/python-logo.png)
- Type of tool: Programming language.
- Availability: Open-source, with thousands of free libraries.
- Used for: Everything from data scraping to analysis and reporting.
- Pros: Easy to learn, highly versatile, widely-used.
- Cons: Memory intensive—doesn’t execute as fast as other languages.
3. R
![Analytics tool R](https://inksem.com/wp-content/uploads/2023/10/R-logo.png)
- Type of tool:-source.
- They are mostly used for Statistical analysis and data mining.
- Pros: Platform independent, highly compatible, lots of packages.
- Cons: More difficult to understand than Python and needs to be faster and more secure.
4. Jupyter Notebook
![jupyter analytics logo](https://inksem.com/wp-content/uploads/2023/10/jupyter-logo.png)
- Type of tool: Interactive authoring software.
- Availability: Open-source.
- They are mostly used for Sharing code, creating tutorials, and presenting work.
- Pros: Great for showcasing, language-independent.
- Cons: Not self-contained, nor great for collaboration.
5. Apache Spark
![spark apache logo](https://inksem.com/wp-content/uploads/2023/10/spark-apache-logo.png)
- Type of tool: Data processing framework.
- Availability: Open-source.
- Mostly used for Big data processing and machine learning.
- Pros: Fast, dynamic, and easy to use.
- Cons: No file management system, rigid user interface.
6. SAS
![SAS analytics](https://inksem.com/wp-content/uploads/2023/10/SAS-logo.png)
- Type of tool: Statistical software suite.
- Availability: Commercial.
- They are mostly used for Business intelligence, multivariate, and predictive analysis.
- Pros: Easily accessible, business-focused, good user support.
- Cons: High cost, poor graphical representation.
7. Microsoft Power BI
![ms power bi logo](https://inksem.com/wp-content/uploads/2023/10/ms-power-bi-logo.png)
- Type of tool: Business analytics suite.
- Availability: Commercial software (with a free version available).
- Mostly used for Everything from data visualization to predictive analytics.
- Pros: Great data connectivity, regular updates, good visualizations.
- Cons: Clunky user interface, rigid formulas, data limits (in the free version).
8. Tableau
![tableau analytics logo](https://inksem.com/wp-content/uploads/2023/10/tableau-logo.png)
- Type of tool: Data visualization tool.
- Availability: Commercial.
- Mostly used for Creating data dashboards and worksheets.
- Pros: Great visualizations, speed, interactivity, and mobile support.
- Cons: Poor version control, no data pre-processing.
9. KNIME
![KNIME logo](https://inksem.com/wp-content/uploads/2023/10/KNIME-logo.png)
- Type of tool: Data integration platform.
- Availability: Open-source.
- Mostly used for Data mining and machine learning.
- Pros: Open-source platform that is great for visually-driven programming.
- Cons: Lacks scalability, and technical expertise is needed for some functions.
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