Tools you must learn as a Data Analyst

Data analytics

The best tools to learn as a data analyst depend partly on the role you are in and the needs of the business. 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, visualising 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.

SQL

Many large companies use SQL for data analysis, and it is considered to be one of the key tools for analysts. It is one of the most prominent tools you must learn as a data analyst. SQL 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 is a straightforward tool to learn and can be used for complex, challenging data analytics. It 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. Additionally, it lets you collate data in a similar manner to Excel but over large datasets and across multiple tables at once.

SQL Databases

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. Python is a versatile tool that supports data manipulation, data analysis and data representation. Many companies that have an online presence used Python to analyse user activity, for example. Python is especially good for creating graphics and visual representations of numerical information. It lets you visualise data collected by presenting it in charts or graphs, and it 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 to evaluate the data collected.

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 (more on SPSS below). More experienced data analysts may require VBA - Visual Basic for Applications. It is a programming language specifically designed for Excel and is often used in financial analysis. It is also compatible with Word and Powerpoint. In addition, Matlab is another useful tool designed to create algorithms, build models and analyse data.

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Data Management tools - R, SAS and SPSS

Three hugely important tools in data analysis, in particular data management, are R, SAS and SPSS. Data management is the process of organising, storing and maintaining data within an organisation or company. It 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 is broadly 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, sales but also some scientific fields such as medicine and technology. You need an understanding of statistics and programming in general in order to work with this particular data analysis tool.

SAS

Another data analysis tool that is often used in statistical modeling is Statistical Analysis System, or SAS. it is a popular tool for data management and supports processes such as data extraction and data mining. It is a highly secure tool and is available across a wide range of statistical coding libraries. Therefore, it is versatile and can be used for all types of data. SAS Studio is a stable piece of software that hosts SAS functions. It can easily handle 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, or SPSS or Stata. Note that SAS is not open-source software, so you would need a license to avail of this particular data analysis tool. It is 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 best tools you must learn as a data analyst. It is 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 is especially easy to work with and offers in-depth analysis and highly accurate results. It is not the best option for dealing with large datasets and is not quite as versatile as other programming tools such as SAS and R.

Git

Lastly, Git is one of the top tools you must learn as a data analyst. Git is often used in data science, but if scientists or other professionals wish to analyse 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.

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