Top 7 Data Analytics Tools You Should Master UrgentlyAugust 29, 2021 2021-10-12 17:34
Top 7 Data Analytics Tools You Should Master Urgently
Data analysts use a wide range of data analytics tools. Some of these tools are advanced programming languages while others are database query languages.
Others are also Business Intelligence (BI) tools while sometimes the data analyst must turn to data modeling tools to get answers to the questions they may have.
In short, the average data analyst may have to learn more than a dozen data analysis just to get going and many more to get the job done efficiently.
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7 Data Analytics Tools Every Data Analysts Should Master For Career Success
If you’re a newbie data analyst and are wondering where to begin (which tools to start with), we’ve compiled a list of seven of the most important data analytics tools.
Let’s kick off with a BI tool. Business Intelligence tools are specialized tools data analysts use to monitor, analyze, and report important findings.
They are typically self-service tools with predictive analysis capabilities and advanced SQL modes that make them easily adjustable to every level of knowledge.
DATAPINE is one of the most exhaustive BI and data analytics tools. It’s focused on delivering simple yet powerful analysis features into the hands of beginners and advanced analysts alike. It’s also incredibly reliable.
Key features of DATAPINE include a visual drag-and-drop interface with an easy switch to advanced SQL mode and a powerful predictive analytics feature.
It also features interactive charts and dashboards that make it simple to use. Intelligent alarms are triggered to alert you that there’s an anomaly.
Data science has its set of rules and scenarios that need special attention for statistical analysis and modeling.
Thus, there’s a need for data analysts to master at least one statistical analysis tool to help you explore, manipulate, and generate insight from statistical data. One of the best tools here is R programming or R Studio.
R was developed in 1995 and is one of the most used data analytics tools for statistical analysis and data science. It runs on an open-source license and can work on a wide range of platforms, including Microsoft Windows and Apple OS.
Meanwhile, R Studio is by far the most popular integrated development environment for statistical analysis.
R comes with unmatched data mining, data reduction, and data analysis report generation capabilities. It comprises more than 10,000 packages and extensions that you can explore by categories, allowing you to perform any kind of statistical analysis, including regression, conjoint, factor cluster analysis, and so forth.
Data analysts do a lot of programming. For example, we’ve already mentioned that they depend on R for statistical programming.
However, they also need a general-purpose programming language that uses letters, numbers, and symbols – a language that uses formal syntax as used by other programmers.
Although C#, Java, Ruby, and Julia are all excellent options, many analysts turn to Python for this purpose.
What makes Python an excellent choice is its accessibility. It’s much more accessible than the other top programming languages. It’s also portable and extremely user-friendly. Users can code with Python on one operating system and import the cord to another operating system without making any changes.
Python is one of those data analytics tools that data analysts use for data crawling, cleaning, modeling, and constructing analysis algorithms for different business scenarios.
Data analysis involves a great deal of database querying and many professionals in the trade often turn to SQL for this purpose.
SQL is a programming language that helps users manage/query data held in relational databases and is particularly effective in handling structured data.
There are many relational (SQL-based) query languages, including MySQL, PostgreSQL, and MS SQL. However, data scientists should consider MySQL Workbench when starting.
MySQL Workbench allows data analysts to design, model, and manage databases, optimize SQL queries, and administer MySQL environments.
It also allows you to utilize a suite of tools to enhance the performance of various MySQL applications. With it, you can create and view databases and objects, configure serves, etc.
Predictive analysis is an advanced technique data analysts use to extract insight from troves of data. It combines data learning, predictive modeling, and artificial intelligence to predict future events.
This partly explains why nearly all data analysis tools now have built-in predictive analysis capabilities. Specifically, BI tools nearly always have predictive analysis capabilities.
However, you also need to master at least one standalone predictive analysis tool, and SAS Forecasting is perhaps the best option.
SAS Forecasting has established itself as one of the most in-demand predictive analysis tools and offers a wide range of forecasting methods, including event modeling, hierarchical reconciliation, what-if analysis, and scenario planning.
This list of data analytics tools for data analysts would be incomplete without a data modeling tool. Data modeling tools help analysts create models to structure the database and design business systems using diagrams, texts, symbols. They then allow the modeler to represent how the data flows.
This way, data analysts can determine the exact nature of the information they control and point out the relationship between different data sets. One of the best data modeling tools is ERWIN.
ERWIN works as structured and unstructured data in warehouses and the cloud and is one of the best at finding, visualizing, designing, deploying, and standardizing data assets.
It reduces data complexities with ease and allows you to understand data sources to meet business goals. It also allows you to automatically generate models and designs to minimize errors while boosting productivity.
Excel for Spreadsheet Applications
Finally, data analysts also depend on spreadsheets for simpler tasks that don’t involve complex and large volumes of data and databases.
Spreadsheets are also useful when the data analyst doesn’t wish to create new code. One of the best tools for spreadsheets applications is Excel.
Excel is part of the Microsoft Office family, thus is easily accessible. It’s often considered a traditional analysis tool, though is still one of the most widely used tools within the industry. Its main attractions are simplicity and versatility.
Excel also allows analysts to export data and send it to targeted recipients with ease.
Some of the tasks you can accomplish with Excel include arranging and manipulating data, calculations and evaluation, building complex equations, and using pivot tables.
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There are many other useful data analytics tools that data analysts need to learn to become even more productive. However, mastering the above gives you an excellent starting point.
And if you are looking for a place to begin your Data analytics career with ease and flexibility, sign up for our Data Analytics Career Track training and see your career blossom.