Exploration of the Information Universe

Hello there, I am a data specialist with five years of expertise who has recently switched to working remotely. Currently, I have been enjoying the benefits of mobile work for four months with Works, and I am thrilled to be part of this community.

With the growing popularity of remote work, it is no longer necessary to talk about the benefits of being able to work for a worldwide company from any part of the world. The focus now is on obtaining employment with your desired organisation.

I have acquired my expertise mainly through my efforts to improve my skills and meticulously choose dependable sources of information.

Upon graduation, I landed my first job as a data analyst. Initially, I anticipated that my responsibilities would involve applying descriptive statistics to a well-structured dataset and then comprehending the outcomes with more intricate regression methods. However, in line with the typical responsibilities of data analysts, this was not the case.

My familiarity with SQL was limited when I first encountered unnormalized data and multiple missing values in a relational database. I looked up tutorials on YouTube to gain a better comprehension of data modelling concepts, normal forms (1NF, 2NF, 3NF, etc.), and merging tables to create new outlooks.

After building the view and arranging the data neatly, accessing it was uncomplicated using a statistical language like R and a SQL connection package.

The process of gathering information becomes more efficient when the data is in the appropriate format. To utilize R’s analysis and visualization tools (lm, ggplot2), I merged data from multiple tables into one database view, ensuring the data was in the ideal format, typically 3NF or 4NF, with one row per observation.

We automated the reporting process using software like RMarkdown after completing the data view. To start a company’s digital transformation, it’s critical to grasp the organization’s data goals and how the different data sources (databases, streams, warehouses) can be utilized to achieve those goals. This was my initial experience in the realm of data analysis, and I quickly realised that I lacked the necessary skills and was employing the incorrect tools for the task. Nevertheless, I dedicated time to researching how I could modify my approach, and I ultimately developed a solution that works nearly as effectively.

Working in the data sector or any digital industry necessitates a mindset of continual growth and development. It is critical to use any encountered hurdles as motivation to seek solutions to overcome or, at the very least, reduce them. Because the data being processed is seldom as clean as required for analysis, many data analysts shift into fields such as data science and data engineering. A data engineer’s responsibilities, such as ingestion, transformation, and pipeline optimization, are often integrated with those of a data scientist.

I pursued this path, and it’s something I’m eager to explore further with you in upcoming pieces!

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