Data Analyst Roadmap: more than just the tools
Tags: sql, beginners, dataanalysis, database
Published: 2022-08-01 11:35:47

I’ve seen a lot of career roadmaps recently.

They come in a variety of flavours. They are written for developers, data engineers, DevOps engineers, product managers, and everyone else. You will be encouraged to learn the newest framework and the latest [cloud technology](https://dev.to/helenanders26/aws-series-aws-from-a-to-z-45b1) you can’t live without.

All these shiny new tools are great to learn about as you level up or switch careers. But as more roadmaps pop up, I realise they rarely touch on people and processes. As tech professionals, these categories are important. I cannot think of any role where you pick up a perfectly [scoped ticket](https://dev.to/helenanders26/do-you-really-need-a-dashboard-57o5) and use any tool you like.

In this post, I’ll suggest topics to explore as you begin your journey as a data analyst. From project scoping, organisational context, and being curious about the data you are interpreting. This can be useful for a variety of data explorers, not just those starting from scratch in corporate environments.

Feel free to start wherever it feels good, and get excited about learning something new.

---

## Beginner

![Data analyst roadmap - beginners](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/vui433mwxmkwxf6kao2a.jpg)

### Data analyst foundations
Starting with a good foundation is important for absolute beginners. If you don’t understand the people and the process, you can’t jump into [SQL](https://dev.to/helenanders26/sql-series-from-a-to-z-2pk9) and visualisation tools. New analysts should explore the different roles and titles analysts can take and what they do. It’s just as important to understand how the analysis process works, where the analyst comes in, and how much data cleansing you should expect to do.

**Learn more:** [Foundations: Data, Data, Everywhere](https://www.coursera.org/learn/foundations-data#syllabus) (course)

### Excel 101
There’s a reason that Excel has stood the test of time as the tool of choice for beginners. It’s easy to see the data, how it fits together, and the ribbon of tools. Excel formulas are a good foundation when moving to SQL and other languages. Getting started with SQL is easier if you’ve used Excel to SUM, AVERAGE, or manipulate data.

**Learn more:** [Excel for Everyone: Core Foundations](https://www.edx.org/course/excel-for-everyone-core-foundations) (course)

### Data governance concepts
Online learning environments and self-teaching options often skip the realities of using data in the real world. Having a good grounding in the relevant legislation for your industry, country, and even your organisation will prepare you to work with personal and [sensitive data](https://dev.to/helenanders26/planning-and-developing-a-power-bi-data-governance-strategy-3kdn).

**Learn more:** [Data governance toolkit](https://data.govt.nz/toolkit/data-governance/) (website)

### Organisational context
The reality of working with data in an organisation is that you are not responsible for its entire lifecycle. You need to understand where the data comes from and who is responsible for it at each stage. Is it housed in a data warehouse? Is there a centralised function that takes care of this? What is the role of the data analyst in an embedded team? How are metrics published across the organisation and where is the ownership for these numbers?

---

## Beginner – Intermediate


![Data analyst roadmap - beginner-intermediate](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/fkz4sjlcj8fxsmltg8uc.jpg)

### Excel 201
Once you have a good understanding of formulas and functions in Excel it’s time for the next steps. These building blocks will help you work with larger datasets. 

**Learn more:** [Excel for Everyone: Data Management](https://www.edx.org/course/excel-for-everyone-data-management) (course)

### Data visualisation tools
In my organisation, this is [Power BI](https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/djwu3eywpk4nm) but the same recommendations apply to all data visualisation tools. This is a big step up for most Excel users as it can be their first experience modelling data. Training here should be focused on creating efficient data models, optimising performance, and answering [stakeholders’ questions](https://dev.to/helenanders26/do-you-really-need-a-dashboard-57o5). There are multiple ways to work with these tools so training should include hands-on practice, projects, and real-world scenarios.

**Learn more:** [Become a Power BI data analyst](https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/djwu3eywpk4nm) (course)

### Data visualisation best practice
An efficient data model is only one piece of the puzzle when using data visualisation tools. The visuals on a report can easily become cluttered and hard to follow and misleading and distorted at worst. Training should focus on making the message clear and easy to understand, using colour and white space wisely, and picking the right chart for the job.

**Learn more:** [10 rules for better dashboard design](https://uxplanet.org/10-rules-for-better-dashboard-design-ef68189d734c) (blog post)

---

## Intermediate


![Data analyst roadmap - intermediate](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/jkcafhfvqbi34fzkg3hd.jpg)

### Scoping sessions and prioritisation
Data isn’t always available, in the right format, and dashboards might not be the best way to present insights. As you move beyond delivering one-off reports you’ll need to get comfortable teasing out what’s important to a stakeholder. Gaining these skills means you can feel confident diving into the data with all the background information and the problem you are trying to solve.

**Learn more:** [Do you really need a dashboard?](https://dev.to/helenanders26/do-you-really-need-a-dashboard-57o5) (blog post)

### SQL 101
Some may say SQL should be one of the first tools new analysts get to grips with. However, not all organisations have data warehouses for analysts to access or allow analysts to access. If this is relevant to your role and the time is right your grounding in Excel should make this a smooth transition.

**Learn more:** [SQL Crash Course: Bite-sized SQL lessons for data analysts](https://sqlcrashcourse.com/) (course)

### Data interpretation and curiosity
So far, the focus has been on creating datasets and visualisations. Analysts are also expected to interpret the analysis of others, query the quality of the data, and find the message and meaning of charts. These skills don’t always come naturally but can be acquired through training and practice.

**Learn more:** [Why companies must close the data literacy divide](https://www.domo.com/blog/why-companies-must-close-the-data-literacy-divide-part-2/)

### Statistics concepts
There are plenty of analysts who do not come from a mathematical background. But as data becomes more of a part of everyone’s role you will need to revisit the statistics we learned in school and put it into practice.

**Learn more:** [Statistics: Unlocking the world of data](https://www.edx.org/course/statistics-unlocking-the-world-of-data) (course)

---

## Advanced


![Data analyst roadmap - advanced](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/7utkb3xemioh1apdc0lt.jpg)

### Communication and presentation skills
The data analysis process doesn’t stop at the end of a SQL script or with the emailing of a report. Translating the insights and communicating the business solution are all part of the process. Analysts working across teams and presenting results widely can benefit from practice giving talks about their projects in a Meetup group or workplace community of practice.

**Learn more:** [Communicating Data to an Audience](https://www.microsoft.com/en-us/research/uploads/prod/2019/01/DataAudience.pdf) (pdf)

### SQL 201
Further SQL training may be needed if you are building datasets, stored procedures, and performing more complex analyses within the database. Dealing with dates, indexes, and performance tuning makes it easier to get the data you need.

### Optional training in data engineering and data science
These two adjacent roles aren’t necessarily the next career step for analysts. Training in these disciplines is for a more well-rounded understanding of the data ecosystem rather than aiming for a promotion.

---

Data analysis skills are more in-demand than ever with more of us needing to use tools to produce insights for decision-making. But getting to grips with the tools is only part of the training new analysts should consider. People, processes, and understanding how data flows through an organisation are just as important.

Are you ready to be an analyst?