Data Analyst is becoming one of the most in-demand IT specialties. So, this article will consider variants of data analyst jobs.
The role of data analytics in a digital product
The decision-making process in business is possible in two ways:
- method of expert assessments – decisions are made based on the experience of the specialist, his qualified opinion. The main disadvantage of this approach is that each person, due to personal experience and worldview, has a cognitive distortion of reality;
- data-driven approach – decisions are made based on data analysis. This approach allows you to confirm or deny the expert assessment and avoid poor quality decisions caused by cognitive distortion.
So, an analyst at an IT company works with data, and based on it finds insights, causation, growth points for business, weaknesses. This information is then used by product managers, marketers, CEOs, and other company specialists.
Analysts are needed in all areas of business: from marketing and sales to product development, from finance to management decisions. Competent data analysis is needed for all companies regardless of the industry: retail, e-sports, travel, education, medicine.
Analytics covers various areas, methods, and technologies. A data analyst can deepen their knowledge and become a data engineer or data scientist.
- Data scientist analyzes data to make predictions and identify valuable insights. To do this, he uses machine learning algorithms that already exist, or even develops his own new ones.
- The data engineer builds infrastructure, data flows, data warehouses so that, first, there is a place to store data; secondly, that the data be complete and of good quality and, thirdly, that this data can be passed on to other users, including analysts and Scientists.
How to become a data analyst?
The modern labor market in the field of data analytics is just being formed. However, it is already possible to identify certain desirable skills that a data analyst should strive for.
Five required hard skills:
- Knowledge of mathematics. It will help to understand the essence of the methods used by the analyst, what exactly are the calculations.
- Knowledge of SQL language (used to work with databases). 95% of an analyst’s job is to work with data.
- Excel knowledge at an intermediate level. Excel’s capabilities for analytics are very wide – from data processing to visualizations.
- Knowledge of Python or R. It is the programming languages that open up new possibilities for the analyst: in terms of analysis, speed and efficiency.
- Knowledge of visualization tools: Tableau, Power BI, or visualization libraries in Python or R.
Five soft skills:
- Critical thinking. Any data should be questioned and it is necessary to check what exactly it contains, how complete and correct it is.
- The initiative, proactivity. An effective startup analyst does not need to be tasked from above.
- An analyst who is not looking cannot do his job well.
- Not every task of an analyst ends with a significant result – a find or a useful insight.
- Striving for development. Technology is constantly evolving. You need to follow trends, improve your skills and tools.