Data analysis skills are one of the hottest skills that have been in high demand on the job market for the past few years. A "data analyst" job title is not new to the market, however, due to the growth of data generation and the facilitation of data storage provided by cloud computing, many companies have now the capabilities to store their big data and to derive insights and value from it. Data analysis has been and will stay a fundamental skill to have for most jobs. In the following, I will discuss how to start a career as a data analyst and how I was able to secure a job as a data analyst at a reputable company. Disclaimer Prepare yourself for the worse; learn more about that here . You should read it if You are looking for an internship or a junior opportunity as a Data Analyst. Data Analyst Trends A simple search of the term " Data Analyst " on google trends can show us a graph with a positive trend of the frequency of searches. We can observe that from 2
The Reality of Data-Related Jobs
The hype surrounding data science and data-related roles has created a misconception that everyone is eager to learn about data science, but the reality is quite different. Many individuals continue to work with data on a daily basis without possessing even the most basic data literacy skills. The idea that everyone is becoming data-oriented is often exaggerated.
For those aspiring to become data scientists, analysts, or engineers, here's what you should expect at your first job in an average company (not a FAANG/MAANG):
Data Literacy
It might be expected that everyone working with data at a company would have at least a basic level of data literacy. However, in reality, this is often not the case. Many mid-career professionals with decent pay do not prioritize upskilling in data-related areas. Unless they are compelled to do so, employees tend to stick with their Excel files and rely heavily on the data team to handle the rest.
Therefore, if your company claims to be data-oriented solely because of its data team, it may be a red flag, and you might have been misled.
Technical Work
Data-related job positions are often advertised with the promise of discovering amazing insights using data skills and improving a company's efficiency by an unrealistic percentage. While this can be true, it's not always the case. In many companies, the data team is primarily responsible for technical data work, such as writing Python scripts, creating dashboards, and querying databases. Other -business- teams handle the actual analysis and insights discovery.
For fresh graduates, this reality can be disappointing, as they may have expected to create impressive success stories for their CVs similar to the ones that inspired them to pursue careers in this field. While technical work can be enjoyable for some, others may prefer engaging in discussions and brainstorming sessions.
In summary, it's important to realize that much of the work will involve creating tools to enhance the efficiency of other teams in performing data analysis. This often means spending more time typing on a computer and less time engaging in discussions and brainstorming, as advertised.
Ad-Hoc Requests
Due to the lack of data literacy in many companies, you should expect to receive numerous ad-hoc requests from colleagues who can't query data themselves. Your main tasks may involve writing queries, saving data to Excel files, and sending it to those who need it. Sometimes, colleagues may even ask you to perform entire analyses without clearly specifying their objectives. This can lead to a situation where you do the work without understanding the reasoning behind it. Later on, you might find your work in a PowerPoint presentation on the company's shared cloud or internal servers.
This chaos can result in repetitive, nonsensical work being requested by multiple teams, and some may be impatient with your speed in completing their tasks.
Are We Doing Sorcery?
Another consequence of data illiteracy is the misconception that coding is akin to black magic. Simple tasks like ingesting data from Excel files into a database may seem straightforward to data professionals but not to others. Many people don't comprehend that a script is essentially a sequence of ordered tasks applied to an Excel file, and any minor changes in the file can lead to script failures.
Similar misunderstandings extend to other aspects of the field. For example, people may expect accurate forecasts even for poor-quality datasets. Take the case of time series data—without trends or patterns, data scientists anticipate poor fits and high errors on testing data. However, the average person in the company may not grasp these nuances and may assume that data professionals possess magical abilities that guarantee satisfactory results.
As absurd as it may sound, this misconception is a significant barrier to achieving high-quality work. When the "sorcery" doesn't yield good results, it's often the data team that takes the blame.
Life goes on...
In conclusion, the world of data-related jobs may not always align with the glamorous portrayal seen in the media or the high expectations of fresh graduates. Data literacy remains scarce in many companies, and the technical work promised in job descriptions can often be limited to creating tools and handling the back-end aspects of data analysis. Ad-hoc requests, driven by data illiteracy, can become a significant part of the daily workload. The perception of coding as sorcery and unrealistic expectations of data professionals further complicate the field. It's essential for aspiring data scientists, analysts, and engineers to be prepared for the practical realities of their roles and adapt to the unique challenges presented by the data landscape in different companies.
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