A year and a half of my career change to Data Analysis
Tags: data, datascience, beginners
Published: 2024-03-17 02:42:42

A year and a half later, and I've learned so much from tools, strategies, best practices and soft skills. It was challenging in the beginning, but I'm sure I made the best decision. In this article, I will describe a little bit of this transition period and explain why everything gets better after at least one year.

## The First Year is the Most Challenging
My first opportunity as a data analyst lasted about a year. In one year, I learned so much that it is hard to list here, but I can say that between the challenges and the imposter syndrome, everything gets better after this period. The dashboards that were super complicated at first, start to become part of the routine and the tasks go from complex to almost natural. Almost... 

For routine tasks, there is still a cognitive effort that needs to be devoted on checking and verifying errors, for example. These tasks require more attention and concentration. For non-routine tasks and new knowledge that will be acquired in the process, you need to have willpower and also concentration.

The more you focus on learning something new, the easier it becomes. Think of it this way: after a year of dedicated practice, what seemed difficult at first, will feel much more manageable. So keep at it – with focus and perseverance, you'll see improvement in many areas!

## Why Soft Skills Are Extremely Important
Through self-discovery, I realized my strengths, in identifying bottlenecks, which was valuable in my previous role. However, transitioning to a technical team required a shift in focus. By understanding my limitations, I learned to trust the existing processes and focus on my new technical responsibilities.

Coming from a generalist role, specializing meant mastering technical delivery: using optimal tools, best practices, and ensuring code readability. Focusing on technical performance provided an initial boost of confidence while minimizing overwhelm.

In this context, existing soft skills, particularly communication and transparency, proved valuable. My experience with product helped communicate effectively with clients and work independently. Transparency was the key: I could flag unusual situations, team overload, or personal need for assistance.

This highlights the importance of continuous learning. Some skills are acquired, others honed, and the learning process itself becomes valuable.

## Strategic Start
Strategic career planning was crucial for setting me on the right track. While I initially craved learning everything at once, I strategically focused on Data Visualization (dataviz) first.

Dataviz offers a compelling entry point for several reasons. Firstly, the demand is high - companies constantly need dashboards, from simple to complex.  Secondly, data cleaning - a crucial skill you'll acquire - is essential for effective dataviz.  Finally, the learning curve is relatively gentle, making it a great starting point.

## Tools and Processes
Data cleaning processes vary across companies. Some rely on built-in BI tool functionalities, while others leverage SQL. My first data role involved both: cleaning data within the BI tool and handling data extraction and collection.

Initially, I used connectors (for pre-built integrations) and even Python web scraping (especially for social listening dashboards). This highlighted the interconnected nature of data careers – one skill often leads to another! What I expected to be a linear learning path became a continuous acquisition of new knowledge with every project and personal challenge.

My second role introduced me to the world of SQL, the industry standard for data cleaning. While I initially only knew basic SELECT queries, after three months of consistent practice, I'm now comfortable writing complex queries using CTEs (Common Table Expressions). This fast progress is a testament to the power of focused learning. If you dedicate yourself, you'll be surprised at what you can achieve!

## Conclusion and Next Steps
I am incredibly grateful for everything I have learned and for everything I have yet to learn! Changing careers has truly opened doors that I never dreamed of. It allowing me to access fantastic teams, incredible mentors, and wonderful learning experiences.

If you are also transitioning your career to the data field, or if you have any questions about this article, feel free to reach out to me on [LinkedIn](https://www.linkedin.com/in/mouralet/). It would be great to exchange experiences and give back the help that I received at the beginning of my journey!

What will I learn tomorrow? Where will this take me? Stay tuned and find out. ツ