From Marketing to Data Analytics: My Journey (I)


One day I got up wanting something different in my life. After spending almost six years working in roles related to Digital Marketing and Communications, I felt it was time for a change. I have to say this was a slow process. From the very beginning, I used to love all the tasks related to data. For example, to create small web session forecasts for the next year based on previous information or to review the main KPIs on paid campaigns to adjust the budget. I liked to spend most of my time playing with Google Analytics data to find new tracking issues and develop reports for other stakeholders. 

That’s how my old me, tired of being responsible for getting more and more sales for e-commerce, decided to quit my job and start my journey into data. It wasn’t an easy goal to achieve. For this reason, I want to share my experience to help you land your first Data Analytics role.

The Decision

Back in 2019, I already tried to make this change in my life. I enrolled in a Data Science master’s that I couldn’t even start because of personal matters. A few months after the lockdown in Spain finished, I decided it was the right moment to switch careers. The Covid-19 crisis made me reconsider whether I was happily working in Digital Marketing or not. I arrived at the conclusion I wanted something different in my professional career. I was going to be a Data Scientist! 

The first step was to gather information about the different roles related to data. I have to tell you Data Analytics and Data Science aren’t the only paths you can choose in this field. Perhaps you are more interested in building data pipelines or exploring the data than creating predictive models. You can check a great variety of data roles in this Medium article.

I read a lot about the difficulties of finding an entry-level role as a Data Scientist. In the United States and other English-speaking countries, most jobs related to this area need a Ph.D. or master’s in a quantitive field or at least some years of experience in a similar position. I navigated into LinkedIn to see the requirements for the Spanish market. The number of junior roles available was relatively low, and there was a wide range of different requisites.

It is not necessary to match all the requirements to apply for a job. However, you still need to have at least a 70-80% of them. I felt overwhelmed due to the huge number of things I had to learn in such a short period. And it was almost impossible to find a boot camp or master that covered all those.

For this reason, I decided to rearrange my dream. I don’t want to say it is impossible to land a Data Science role with a permanent contract as a fresher, but it is hard. However, finding a job as a Data Analyst is relativity easier in comparison: there are more open job offers, and the requirements are more reasonable to acquire. It is a more realistic goal to pursue.

The Requirements

The main requirements for Data Analytics are the following:

  • Intermediate level of Math and Statistics
  • Relational Databases: SQL
  • Microsoft Excel
  • Business Intelligence Software for Data Visualization. For example, Power BI or Tableau.

Sometimes, you will see Python and R as desirable requisites. They are not a must-to-have. Nevertheless, it is a great tool to differentiate your profile from the competitors in the job market.

These requirements may change based on the country you live. Check the job offers available in your region and jot down the specific requisites.

Moreover, you will need business knowledge, which I think is the most valuable skill you cannot learn just by reading books or taking an Udemy course. If you are pivoting from another remarkable field where data has multiple usages – like Finance, Marketing, Healthcare, or Supply Chain – you can take advantage of that previous experience by finding a new job related to them. 

If this is the case, I would recommend you to look for those hard and soft skills related to your former experience and the Data Analytics role. For example, if you have been working as a Chemist, you can say you have logical thinking and high analytical skills, two key characteristics in the data field.

What can you do if you don’t have this pillar? There are different approaches to facing this challenge:

  • Take a course related to a field you are interested in. The idea is to know more about its distinctive features, challenges, and the metrics used to track that specific business.
  • Read, read, read. This will help you to stay tuned to the changes in the business. For example, Marketing is a very fluctuating field. Something that today is true, tomorrow will be out-of-style.
  • Create related projects you are interested in. That would help recruiters know that you know the field.
  • Network with other professionals working in that field. It doesn’t matter if they are working directly with data or not. The goal is to get in touch with colleagues to know more about that specific business.
  • Share all the specific learnings you have got across your journey. This process will help you to gain visibility around recruiters in that area.

What if I Want to Specialize in Marketing Analytics?

If this is the case, I recommend you to check the following resources:

It will provide you with a general picture of the field. There are other courses from Google that are focused on Digital Advertising or Organic Traffic (SEO), for example. You can choose to go deeper on some of those topics.

Do I Need a Degree?

It is a question that some of you asked me on LinkedIn. Again, I think it depends on your country specifically. From my experience, I consider it isn’t easy to find a job in data with only a High School certificate in Spain. You need a Professional Training certificate or a degree to have real opportunities in the job market. Or, at least, some relevant job experience.

What if you have a degree in another field? I don’t believe your studies need to be related to data. However, some companies will only hire employees with a degree in a quantitative field like Statistics, Mathematics, or Computer Science.

For example, I studied Journalism at the University, a degree purely related to Social Sciences. It can be a handicap for some employees. However, I try to remark this is a degree where communication is the key. For this reason, I can do great storytelling and simplify complex ideas for stakeholders.

What’s Next?

In the second part of this article, I will explain what main resources I used to learn the basic skills to break into Data Analytics and what type of course (master, BootCamp, self-learning) path I recommend based on my experience. Stay tuned!

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