What Data Analytics tools should every Data Analyst know?
Discover the top 10 data analytics tools, including Power BI, Tableau, and AWS, essential for every Data Analyst. Learn why mastering these tools can boost your career and open doors to exciting opportunities.
So, you’ve started your journey to becoming a Data Analyst; you’re dedicating your time to studying, you’re attending the live classes, and you’re working with StudentCare and Career Services. You’ve seen the different data analysis tools but you’re not quite sure why they’re important, yet.
To help, we’ve put together a list of the top 10 Data Analysis tools that a Data Analyst or Data Scientist should know of and why they’re important.
Written by
Adam is a Marketing Executive at Learning People, specialising in software development and data with a background in tech and project management.
Power BI
Power BI is at the forefront of the data revolution. It works not just as a data visualisation tool but also for data consolidation and connectivity, using the various programmes within it such as PowerQuery and PowerPivot.
The business intelligence market is worth $38.6 billion dollars in 2024 and is projected to grow to $130.4 billion over the next 10 years. Microsoft represents a 15.34% market share of that, and Power BI is optimised for the Microsoft data process, including integration with Azure cloud.
If you’re looking to give yourself as many opportunities as possible throughout your career, there aren’t many better places to start.
Tableau
Once just used as a data visualisation tool, Tableau has grown and become an enterprise tool in its own right since being brought into the Salesforce ecosystem. From visualisation to cloud integration to data solutions for business decision-makers, Tableau is famed for its simple user interface making it a great choice as well for anyone that doesn’t have any SQL or coding experience.
Tableau offers a data analysis tool similarly as diverse as Power BI and almost as widely used. It has a market share of 13.9%, making it one of the two most sought-after skills in the industry. Understanding, and experience, of Tableau, in conjunction with Power BI, is a must for anyone looking to keep all their options open.
Google Data Studio and BigQuery
You don’t need to be told how big Google is, it’s become its own word in the dictionary. This means that knowledge of how to use Google Data Studio could save you a lot of time integrating another analysis tool. Combined with BigQuery, Google’s data warehousing system, and you’ve got a well-rounded toolset.
Jupyter Notebook
There’s so much that goes into every data project, no matter how small, that keeping everything together can be a small project on its own.
Jupyter Notebook is an incredibly powerful tool for developing and presenting data projects. It combines code, visualisations, narrative text, and other rich media into a single document, giving you a clear and concise workflow.
Excel
It’s often forgotten just how important Excel is. Employers will often check understanding and experience with the many functionalities of Excel during an interview. It might not provide the interactivity or vast data storage that these other tools offer, but it will give you quick and simple methods to process your data and provide insights for your organisation.
Sometimes the simplest tools are the best.
Apache Hadoop/Spark
Whilst Hadoop is probably the better known of the two, Apache Spark offers a much more varied list of bindings across Python, Java, Scala and R languages. Hadoop is Java-based which limits it’s uses a little in the modern data environment.
Both of them operate as big data distribution and analysis tools, giving Data Analysts faster queries, scalability and reliability with their distributed data file systems.
Talend
Talend is an open-source data integration platform that provides various software and services for integration, data management, enterprise application and cloud storage. It’s widely regarded as one of the leading next generation leaders in the industry, giving analysts faster development and deployment with its automated services and a unified platform.
Plus, being open-source means the costs are low as much of its capabilities can be used for free, and there’s a huge community to support one another.
MongoDB
Another open-source tool, MongoDB is a NoSQL (not only SQL) database management system that offers an alternative to the more traditional relational databases. Typically used for high-volume storage, it’s a tool that can manage document-oriented information, store or retrieve information, whilst doing so quickly.
There are other alternatives to MongoDB, but its open-source lowers expenses and it supports a lot of coding languages, including C, C++, C#, Go, Java, Python, Ruby and Swift.
Microsoft Azure
As we’ve already mentioned, Microsoft is a big player in the data sphere. Azure is its public cloud computing platform, well-known for its flexibility, it’s web hosting and Active Directory scale. From here, you can access and transform your data from anywhere in the world seamlessly and quickly.
It’s the second largest cloud computing platform with a market share of 24%, so most employers will expect you to have some experience of how to use it effectively.
AWS
If it’s not Azure, then AWS is your must-know cloud computing platform. It’s the largest out there, with a 31% market share, but its growth has slowed by comparison to Azure over the last few years. Still, it’s a giant of the industry, and if you can complete your knowledge of Amazon Web Services in conjunction with Microsoft Azure, then your job opportunities are massive and you’ll be set for the future.
If you’d like to discuss any of these tools or how to become a Data Analyst in more detail, then be sure to organise a consultation with one of our Career Consultants who will be able to guide you through the multitude of options open to you.