Last updated on April 19th, 2022 at 01:57 pm
To perform data analysis, professionals use tools & software that ensure the best results. There are many of them in the market, but a few are on the top list for data analysts that need to be chosen wisely to extract the best benefit.
This article discovers the best data analyst tools and highlights key features of each based on different types of analysis processes. But first, here’s a basic definition & introduction.
What Are Data Analyst Tools?
Data analyst tools are applications & software that data analysts develop & perform analytical processes to make better, informed business decisions while keeping costs low & profits high.
Top Tools Used by Data Analysts?
1.) Business Intelligence Tools
BI tools are the most represented means to perform data analysis. Specialized in business analytics, they prove beneficial for every data analyst to monitor, analyze, and report essential findings. Self-service, advanced SQL modes and predictive analytics make them easily adjustable to every level of knowledge without immense IT involvement.
DATAPINE covers most requirements of beginners & advanced users.
Key Features:
Visual drag-&-drop interface
Powerful predictive analytics & interactive charts & dashboards
Intelligent alarms triggered for anomaly
2.) Statistical Analysis Tools
There exist multiple programming languages that make work easier & effective. With the expansion of languages, some rules and scenarios need special attention for statistical data analysis & modeling.
R PROGRAMMING is the most popular tool for data analysts.
Key Features:
An ecosystem of more than 10k packages & extensions for data analysis
Statistical analysis, modeling, & hypothesis testing
The active community of researchers, statisticians, & scientists
3.) General-Purpose Programming Languages
Programming languages help to resolve a variety of data problems. Tools like C#, Java, PHP, Julia, Ruby, and Python are many reliable solutions.
PYTHON is the most trusted tool.
Key Features:
Open-source solution with simple coding processes
Easy integration with other languages
Advanced analysis processes through ML & text mining
4.) SQL Consoles
SQL is a programming language used to manage data held in relational databases, effectively handling structured data for analysts. Highly popular in the data science community, these tools are used in various business cases & data scenarios.
MYSQL WORKBENCH is the most popular one.
Key Features:
- Unified visual tool for data modeling, administration, SQL development, backup, etc.
- Instant access to the database via Object Browser
- SQL Editor offers color syntax highlighting, reuse of SQL snippets, & execution history
5.) Unique Predictive Analytics Tools
Predictive analytics is an advanced technique to combines data mining, AI/ML, and predictive modeling to predict future events. It is an essential data analysis tool as it simplifies predictive analytics processes.
SAS FORECASTING is an analysis software helpful for predictive analytics processes & helps analysts to predict future scenarios.
Key Features:
- Automatic forecasting for entities or products, including hierarchical forecasting
- Scalability & modeling to create an ensemble
- An unlimited model repository including time-series & casual methods
Apart from the tools mentioned above, there are industry-specific analytics tools & data science platforms that assist professionals. A business analytics course can help you better understand what data analytics is!
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