What does a data analyst do? (2022)

5 min readMay 30, 2022

Get to know the Roles and responsibilities of a data analyst and where, and how you can start learning or growing your skills.

From several job descriptions for data analysts, one gets confused as to what the role entails. Here, I try my best to summarise all these roles and responsibilities to help prepare you for your journey ahead.

What is data analysis?

Data Analysis put simply is the process of identifying and collecting data from useful sources and cleaning the data so that it can be Analysed. The data analyst then interprets and presents the analysis to the stakeholders, so they can make use of the insights for business decisions.

Types of data analyst roles

Data analysts can be of many types like -

Business analysts, business intelligence analysts, market research analysts, Financial analysts etc. Each of these “Roles” does not have a set in stone definition, but can be used to loosely define analysts who play different roles in an organisation.

For example, a Business Intelligence analyst may provide the management or business decision-makers with Stats, Graphs and other information to simplify the decision-making process.

Likewise, A Financial Analyst may be responsible for analysing financial time-series data to find patterns, trends and other insights to help predict financial KPIs.A data analyst should be able to function well in any of these roles.

Skills for Data analysis:

Technical skills:

Data analytics is a fairly technical profession, and a good understanding of the tools and techniques is essential. We can classify the tools into their respective categories in terms of importance:

A Spreadsheet tool: The absolute fundamental skill. Required by almost all the data related roles. Spreadsheets are used for navigation, data exploration and quick analytics to help you get a feel of the data. But more importantly, In most cases, it is the only tool that your stakeholders will be familiar with.

A query language: Like SQL for database management and infrastructure.

Data Visualisation tools: Like Tableau or Power BI are not only used for exploratory analysis but also to build dashboards and reports. They are also commonly used to visually measure KPIs and other key metrics.

Programming languages: like R or Python for advanced analysis and data exploration. This Skill may not be a requirement in all data analytics roles. As a majority of basic analytics can be performed with excel and Tableau/ Power BI.

A basic understanding of Statistical and mathematical concepts is a must. Though a more in-depth understanding of topics like — Descriptive statistics, Probability distribution functions and other related concepts can be complementary and can really help you perform better.

Non-technical skills

These Non-technical/ Soft skills are usually more important than the technical skills that we have mentioned earlier.

Communication:Clear communication is an essential skill for a data analyst. All information and message should be delivered to stakeholders clearly and in the easiest to understand way.

Critical Thinking:A Data Analyst must be able to think critically and analyse and synthesise information to generate insights, and must be able to conclude these findings.

Problem-Solving:A data analyst should be able to quickly identify problems and generate efficient and effective solutions.

Ethics: A data analyst must know what is right and wrong and should provide security to the data they are responsible for.

Tasks and responsibilities of a data analyst.

The tasks and responsibilities of a data analyst are largely dependent on the industry and company. While some roles focus on Data cleaning, others focus completely on Building dashboards and reports.

Similarly, some roles may solely focus on Spreadsheet reports and analytics. while some may be completely focused on Python and in-depth exploratory analysis.

Typically, the following roles can be used to outline their responsibilities-

Designing, maintaining and managing data systems and databases.
Identify critical Metrics, Measures, and KPIs.
Collecting or extracting data from primary and secondary data sources.
Filtering, cleaning, Organising and Maintaining data
Identify, analyse and interpret trends and patterns in complex data sets using several tools and skills.
Visualise and present findings to key stakeholders.
Build, customise and maintain reports and dashboards.
Create and maintain documentation regarding data models, measures, KPIs and other systems as they are developed.

Where and how to start? My personal suggestion for starters.

If you’re confused after reading all of this and still don’t know where to start, there’s nothing to worry about. Here, I’ve just tried to explain the overall concept by summarising a whole lot of information and ff course, I expect you to dive deeper and do much more research before you make a choice.

To grab the very basics of data analytics. I would recommend taking a look at one of the several amazing courses available on Udemy. This combined with either the IBM or Google, Data analytics certification from Coursera will provide you with a solid foundation and even some very good experience on real-world projects.

Coursera also has data science courses, which you can audit for free. I.e. You don’t have to buy a course unless you’re really sure about completing it and earning a certificate.

Another great way to learn is to do what you are doing now. There are some amazing blogs on data science and some awesome youtube channels with great content to help you learn data analytics.

If you like to read, here’s a list of absolutely essential books to help you:

You can checkout My Data Science Blog, where you can learn everything about data science. From the basic foundational knowledge to advanced concepts and Ideas. I will also be sharing free resources, guides, tools and links. It is one of the best places to start your Data science journey!

Thanks for reading, and Best of Luck!😁