My journey until I met Harve
As previously mentioned, in my first year of college I was part of JR Consultoria – UFPR, where I was able to develop two marketing plans, one for a small chain of stores and mattress factory, and another for an NGO. In addition, I worked at the Federal Court of Paraná, where I was able to “pull” the implementation of an electronic system for the section where I worked.
Towards the end of my Business Administration degree, during a work french email address list in finance at a startup, I started studying and applying SQL and Python, facilitating the extraction, transformation and loading of information in our area. That's when I realized how technology can help us make decisions with greater accuracy, agility and efficiency. Since then, I have never stopped studying programming, statistics, etc.
Why I chose Harve
Initially, a coworker told me about a course he was going to take, since we both share a passion for working with data. I took a look at the topics and tools covered in the course and realized that it would be worth the investment, since the experience would be very hands-on, and it would provide me with a network with professors who have been in this market for many years at large technology companies.
My experience so far with Data Science
I completed my data science training during the pandemic, but I was able to absorb a lot of knowledge and also practice a lot during that time. The best part of the training is being able to ask questions to people who really understand the subject, and not waste hours and hours searching for fragmented information on the internet (I think we all know how it is). During and after the course, I added several professors and students on LinkedIn, and I even ended up doing some interviews at the companies where they work.
I haven't had the opportunity to implement a Machine Learning project in "production" yet, but I've done a few on personal projects. I was recently approached for a financial analyst position at a large startup in Curitiba precisely because I know how to deal with SQL, Python, data and automation. Despite having been in the position for less than 3 months, I've already been able to automate several routines in my area, analyze different types of data and I feel like I'm really making a difference there.
As a side effect of data science, I also learned the skill of programming. I'm not a developer and I don't intend to be, but I've been able to supplement my income by doing some small jobs developing scripts in the last few months.
My expectations for the Data Science market from now on
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Despite working at another startup, we have a similar environment. All areas have contact with at least SQL for data analysis and routine execution.
In a world where companies grow at dizzying rates every year, Excel simply doesn't solve everything. Even if you don't have the desire to be a data scientist, analyst or engineer, I believe that understanding the basics of the area and having some skills such as SQL and programming in some language will be essential in the coming years. Where there is a lack of developers and data scientists to build queries, scripts and systems, there is a sea of opportunities for business analysts to develop their technological skills and apply these skills to make a difference, even enabling a path to the data or technology area.
Tech companies are absorbing a large number of data-savvy people, and with the amount of data generated increasing at a rapid pace, I'm sure this will continue for quite some time.
In a more macro scenario, I think that machine learning and data solutions are increasingly being democratized. We are already seeing some products from major players being well-received by the market, and it is only a matter of time before data solutions reach small business owners (with their own particularities, of course).
Which areas of Data Science do I identify with the most?
I'm currently working a lot with ETL. As part of data science, you'll need to know how to extract, transform, and load this data in a way that you can easily consume it, and I ended up identifying a lot with this part. In everyday life, data comes in many different forms, and little by little I'm improving my range of techniques and tools to work with it in the best way possible. For the areas within a company to function as a single organism, it's essential that information flows in the best possible way, and many business problems are solved this way.