FAQs: Data Analytics

Regression modelling is a reliable data analysis technique that enables analysts to accurately identify which variables impact a topic of interest. It helps analysts confidently determine which factors matter most and which can be ignored, as well as how important factors influence each other.

To give a simple example, a person’s weight will definitely increase as their height increases. Analysts look for these kinds of relationships between statistics because they clarify how one factor will affect another, making outcomes predictable.

Because data that sits untouched in a spreadsheet is of minimal value to a business, Data Analysts are responsible for visually and verbally sharing their data insights.  

Data analysts will convert their findings into high-impact visualisations (charts, graphs, tables and other infographics) that make data insights easy to comprehend. 

Our ability to absorb visual information is far speedier than our ability to grasp the meaning of words and numbers. If a picture is worth a thousand words, a clear data visualisation might be worth a million data points.

As well as being able to visualise data, data analysts need to be able to tell compelling stories about their data insights.

Data analysts need to deliver simple, linear narratives that clarify what their data uncovers about a business. This can create an “aha” moment, when a deep insight is fully understood by an audience.

Data analysts don’t need to be bestselling authors, but they do need to tailor their pitch. To secure stakeholder buy-in for a recommended initiative, it’s vital to build a narrative that’s meaningful and relevant to whoever is listening.

Data Analysts rely on a wide variety of tools to be more effective and efficient in their day-to-day work. Some of the handiest tools that Data Analysts use include:   

• Excel

Beautifully simple and perfectly functional, an Excel spreadsheet organises raw data into a readable format and makes it easier to extract insights. With more complex data, Excel allows you to customise fields and perform automated calculations.

• Python Programming

Python is a multi-purpose programming language that’s popular among data analysts due to its extensive collection of libraries, which are useful for combining multiple datasets and performing complex calculations.       

Python coding skills come in handy at all stages of a data research project, helping data analysts clean, manipulate, examine and visualise data. 

• SQL

As its name suggests, Structured Query Language (SQL) is a standardised programming language that’s used to retrieve and query data. Because SQL is standardised, it’s easy to understand and learn. 

SQL can be used to access and communicate with large amounts of data wherever it’s stored. This means analysts don’t have to copy data into other applications. Instead, they can instantly start organising and analysing data at the source. 

• Power BI

Microsoft Power BI is a collection of software services, apps, and connectors that work together to turn disparate sources of data into coherent insights. Whether it’s stored in a spreadsheet or a cloud database, businesses can aggregate data into a single workable data model. 

Assisting data analysis projects from end-to-end, Power BI also has the tools to turn insights into interactive, immersive visuals, which can be shared with other Power BI users throughout an organisation.  

• R 

R is a programming language and a free, open-source software library that’s used for cleansing and prepping data, generating statistics and creating visualisations. R’s coding language is simple but powerful and often used by data scientists to train machine and deep learning algorithms. 

R can be used directly and interactively on the web, and also easily integrates with BI software, helping analysts combine a range of critical data.

• Google Colaboratory

‘Colab’ is a Google Research product that allows anybody to write and execute Python code through a browser. Data can be drawn directly from GoogleDrive, or imported from an external source. It also works as a comprehensive notebook where analysts can write code, run code, see the output and then share the whole process with teammates.

• Google BigQuery

BigQuery is a cloud-based architecture that allows analysts to query masses of data without the need for a database management system. Analysts can auto-scale their search results up and down, and only pay for the data they process. 

• Tableau

Analytics teams use Tableau to drill deep into data and then convert uncovered insights into clear infographics. Tableau’s emphasis on visuals makes it a great tool for quickly exploring data and packaging it in a way that’s interactive, collaborative and easy on the eye.

• Datawrapper

Data from various sources can be copied into Datawrapper, which then converts information into interactive pie charts, line charts, bar charts and maps. These can be embedded into a website and even customised to suit the aesthetic of a particular brand or platform.