A simple guide on how to become a data analyst
3 key things to know for your data analysis journey.
Introduction
Data analysis is the process of identifying, cleaning, transforming, and modeling data to discover meaningful and useful information. It has become a global requirement for businesses to efficiently use their resources by making data-driven decisions. This has led to a huge demand for data professionals and I'm guessing that's why you’re reading this post. To keep this succinct, I will not be going into details on types of analysis and different data roles that exist, you can read about that here.
A little note: I work as a data consultant for a Microsoft partner company so I primarily work with Microsoft tools and learning resources. You will notice those feature more in my recommendations.
My intention for this post is that it serves as a guide to anyone starting their journey into data analysis. It is by no means a manual or instruction. I think it is important for each individual to chart their own path based on their interests and abilities. This is just to help you get started.
There are 3 aspects I think are crucial to the journey.
1. Learn key concepts and deepen research skills
Understanding foundational data structures will place you ahead of the learning curve on your data journey. My fellow math fans will recall the term "First Principles". Mathematicians and philosophers believe that thinking about or "solving" a problem from first principles means starting from the basic level of knowledge and building up from there. A lot of people struggle when trying to learn new things because they want to learn quickly but end up skipping steps in the learning process. Here's an example I liked from this article:
The difference between reasoning by first principles and reasoning by analogy is like the difference between being a chef and being a cook. If the cook lost the recipe, he’d be screwed. The chef, on the other hand, understands the flavor profiles and combinations at such a fundamental level that he doesn’t even use a recipe. He has real knowledge as opposed to know-how.
A solid understanding of data structures will make it easier for you to use any tool or processing language to analyse data. Similarly, learning how to learn and research are 2 key skills that will help you build real knowledge.
Here's some topics I recommend you start with:
- Structured and unstructured data.
- Relational databases, what they are & why they are used
- Data normalisation
- Primary & Foreign keys
- Slowly Changing Dimensions
- OLTP & OLAP data processing
- Model Schemas: Star & Snowflake
Resources for deepening data knowledge
- Google, Wikipedia & YouTube (no joke, research research research)
2. Learn how to use Analysis & Visualisation tools
There are SO many different tools we can use for data analysis: Excel, Power BI, Tableau, Python, R, SQL and more. My advice is to just pick one, it doesn't matter which one because the principles of how they work will be transferrable enough to make learning how to use the next tool easier. I also think people spend too much time pondering over where to start so I say start anywhere.
When I started my data journey in 2019, I took a course on EDX on data analysis with Excel because I was using Excel for work at the time. I moved to learning Python on Dataquest, and now use Power BI & SQL for work. Do not feel the need to learn everything at once but also know when to move to a different tool if you feel it would better suit your interests/skills.
Resources for learning how to use data analysis tools (that I have used)
- The Training Workspace: Instructor-led Power BI training classes
- Microsoft Certified Data Analyst learning path
Learning platforms
Reading
YouTube Channels
Power BI
SQL
Social Media
Find & follow active members of the data community online. Here are some of my favourites Gift Ojeabulu, Jessica Uwoghiren, David Abu, Bukky Akinsola, Toyin Olape, Yinka Oke, Zainab Ayodimeji
3. Build your non-technical skills
Technical people are infamously known for wanting to focus solely on their work. However, in a professional setting there are certain tasks you will be required to complete and skills to hone when delivering an analytical project. I'll talk about some of them here.
Problem scoping and solution design
This stage is where you ask and get answers to relevant questions. The art of thinking is a favourite research topic of mine and I enjoy learning about mental frameworks for solving problems. This is important because it helps you ask better questions, this often leads to a deeper understanding of the problem. I like to use the BUS framework.. It is popularly used in product development but its principles can be applied here as well. This article is a long but insightful read.
BUS stands for
Business problem
User problem
Solution
You first define the business problem, then the user problem then create a solution.
It's really easy to focus on or start from the User problem, sometimes the user themselves dont know what they want! They may have an idea so it is your task to understand the root issue and design a solution for that.
Example of a user problem: Finance and HR want you to create reports for them so they can have summary visuals on a dashboard.
Digging deeper to the business problem: Finance and HR reports take a very long time to prepare and the company would like to automate the reports so the staff have more time to focus on more productive tasks. The goal is to reduce report creation time for efficient resource utilisation.
Try to continue asking "why?" till you understand the specific goal of your analysis. Some questions you can ask yourself while working on analysing data include:
- Which insights do my users want/need/require
- Can they use the insights I’ve provided for decision making?
- How does this solve the business problem?
- How does it solve the user problem?
- Can the data I have answer the business problem?
- Is my report/dashboard easy to navigate?
- Is the representation of the data accurate?
Communication and presentation skills
As an analyst, you will be required to communicate with your team mates and stakeholders in your analysis. A stakeholder is anyone with an interest in result of your analysis. In a professional setting this could be internal or external depending on whether you conduct analysis for the company you work for or for clients.
You need to be able to communicate how you have solved the users problem or how you have met the requirements given to you by the stakeholders. This could include walk-throughs and Presentations where you ensure your audience grasps how your solution works and how to consume it (e.g your report or dashboard). To ensure user engagement is at a high level, you would have to explain how the report features (e.g. filters) work and how visuals interact with each other.
Writing documentation
In my opinion, this is one of the best things you can do for yourself. Make it a habit to document your processes, thought frameworks, issues you face and how you resolve them. This develops your writing skills as well as gives you something to reference whenever you face an issue again or have to share a solution with someone. In a professional capacity, you may also be required to create documentation on your solution for your stakeholders to reference after the solution has been delivered.
I use OneNote for documenting but you can use any tool of your choice.
Resources to build your soft skills
Note taking
Communication and more
- The 3 Magic Ingredients of Amazing Presentations
- 5 QUICK Ways to Improve Your PowerPoint Design
- Business Soft skills playlist from CrashCourse
Et voila, those are my 3-ish things!
I hope you enjoyed reading this post. Do share if you found it helpful and leave a comment if you have any :)
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