Although we’d like to provide you with a comprehensive list of the best tools to learn as a data analyst, this depends on the role you are in and the needs of the company. Different companies will have different requirements and may work with an internal or external set of tools; some data analysis tools are used for tasks such as executing algorithms, visualizing data and results, while others may be designed for preparing data for analysis, assessing results or for developing hypotheses or predictions.
Data analysis tools can also be used to automate tedious tasks, providing data analysts with more time for actual analysis. There are a few key tools that are used in data analysis, however. These include SQL, Python, Git and data management tools such as R, SAS and SPSS.
Let’s dive right in and discover the necessary tools to learn as a data analyst.
Essential Tools for Data Analysts
SQL
Many large companies use SQL for data analysis and it’s considered to be one of the key tools for analysts. Why? SQL, or Structured Query Language, is crucial for managing and manipulating relational databases. For instance, data analysts use SQL to perform queries that extract valuable insights from large data sets, such as identifying sales trends or customer behaviors. Popular SQL tools include MySQL, PostgreSQL, and Microsoft SQL Server:
It’s one of the most prominent tools you must learn as a data analyst and is also used by software engineers in software development.
SQL is a programming language that was initially created for managing data from a relational database.
It’s a straightforward tool to learn and can be used for complex, challenging data analytics and is a popular choice among data analysts, as the code itself is not difficult to read or understand and can be used to manipulate and change data.
It lets you collate data in a similar manner to Excel, but over large datasets and across multiple tables at once.
Python
Frequently used in inferential statistics and probability, Python is an open-source programming language that lets you build and manage data structures with the Pandas library. According to a survey by Kaggle, Python is the most popular programming language among data professionals, with over 80% of respondents using it for data analysis. Python's versatility and extensive library support, such as Pandas for data manipulation and Matplotlib for data visualization, make it indispensable for data analysts
Python is a versatile tool that supports data manipulation, data analysis, and data representation.
Many companies that have an online presence used Python to analyze user activity and it’s especially good for creating graphics and visual representations of numerical information.
It lets you visualize data collected by presenting it in charts or graphs and can also be used to interpret large datasets.
Python is also a useful tool if you need to test a hypothesis through A/B testing and evaluate the collected data.
Analytics Tools: Excel, VBA, and Matlab
A classic in the world of data analysis, Excel tops the list as a crucial tool to learn as a data analyst. It is a straightforward programme to learn and data analysts should be proficient in all aspects of Excel from using formulas to creating pivot tables. Any spreadsheet application will be useful, but Microsoft Excel is the most widely used one.
Along with this, a statistical analysis tool such as SPSS is often required by analysts; SPSS is a good option for newly qualified analysts (read more on SPSS below!). More experienced data analysts may require VBA - Visual Basic for Applications, a programming language specifically designed for Excel and often used in financial analysis also compatible with Word and Powerpoint. In addition, Matlab is another useful tool designed to create algorithms, build models and analyze data.
Source: FutureLearn
Data Management Tools: R, SAS, and SPSS
Three hugely important tools in data analysis, specifically data management, are R, SAS, and SPSS. Data management is the process of organizing, storing, and maintaining data within an organization or company and constitutes an important step in data analytics, as the information may need to be instantly available, up to date, and accessible for various analytical functions.
R
Compatible with all platforms (Windows, Mac OS, and Linux), the open-source programming language R is another essential and commonly used tool in data analytics:
It’s widely used by statisticians for statistical modeling, as it offers a great range of statistical and graphical possibilities and it is commonly used to perform data wrangling.
It lets you produce visuals such as plots and graphs to represent data and is available across various libraries such as Plotly.
It's used in finance and sales but also in some scientific fields such as medicine and technology; you’ll need a general understanding of statistics and programming to work with this particular data analysis tool.
Source: Photo by ODISSEI on Unsplash
SAS
Another data analysis tool that is often used in statistical modeling is Statistical Analysis System, or SAS, a popular tool for data management and supports processes such as data extraction and data mining:
It’s a highly secure tool and is available across a wide range of statistical coding libraries, is versatile, and can be used for all types of data.
SAS Studio is a stable piece of software that hosts SAS functions, easily handling large volumes of data and is compatible with platforms dealing in big data.
Converting external files into an SAS format is also straightforward, whether it’s an Excel file, SPSS, or Stata.
Note that SAS is not open-source software, so you would need a license to make the most of this particular data analysis tool; it’s also not the best option for creating a visual or graphical representation of data.
SPSS Software
Statistical Process for Social Sciences, SPSS, is another top tool in data analysis and one of the most important tools you must learn as a data analyst:
It’s a sophisticated tool often used for processes such as trend analysis and advanced analytics, as well as hypothesis testing and forecasting.
This is closed-source, so a license is needed in order to work with this tool.
It’s especially easy to work with and offers an in-depth analysis and highly accurate results.
It’s not the best option for dealing with large datasets and is not quite as versatile as other programming tools such as SAS and R.
Source: Photo by Myriam Jessier on Unsplash
Git
Lastly, Git is one of the top tools you must learn as a data analyst; it’s often used in data science, but if scientists or other professionals wish to analyze data and uncover evidence around a hypothesis, then Git is a very useful tool:
It can be used to perform data management tasks such as merging and collating information.
If you modify your database, keeping track of changes is generally done through a version control system like Git which allows analysts to monitor changes over time.
Take the Leap into Data Analytics
Get started with the fundamentals of data analytics and discover the tools you must learn as a data analyst with Ironhack’s Data Analytics Bootcamp. This three-month course will introduce you to topics such as data wrangling, creating a new data set and APIs, and provide you with foundational skills for data analytics tools such as SQL and Python skills. Presenting data and insights is a crucial part of data analysis and is also covered in this course.
If you’re ready to take that next step and start your data journey, we’re happy to answer any questions you may have.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
