Who is a DataAnalyst...?

👉WHAT IS Data analysis is the process of transforming data into insights. It gives organizations the ability to support strategic business decisions.

In a nutshell, this practice involves collecting data from different data sources, cleaning data to remove errors, and then applying different techniques to find patterns and identify anomalies. While the role of data analysts might be often confused with data scientists, those roles are not exactly the same.

By using tools like data visualization and techniques, such as creating charts with Power BI or Microsoft Excel, you can present data in a way that helps stakeholders interpret it and make informed decisions (this is what the industry calls “data-driven decisions”).

In essence, it’s all about using technical skills, different languages, and methods such as predictive modeling to analyze the data, predict future trends, and ultimately support the so-called data-driven insights.

🖥What does a Data Analyst do?

A data analyst does many things, because the role itself is very versatile, it involves everything from collecting data to the presentation of findings (i.e. plotting results in charts, or presenting reports with found insights) and everything in between.

To be more specific, a data analyst's work revolves around collecting and gathering data from databases, spreadsheets, and other data sources (usually structured data sources). They perform data analysis by cleaning data and then applying techniques such as regression analysis, and data mining to model data. The final aim is to identify trends and predict outcomes.

🎛What skills are required for Data Analysis?

There are several skills required for data analysis, and they’re all technical skills. Data analysts must start by getting a solid foundation in working with raw data. They then need the ability to ingest and collect data from different sources—be it through databases, spreadsheets like Microsoft Excel or Google Sheets, or specialized data collection tools.

Once you’ve gathered the data comes data cleaning. This means removing inconsistencies, errors, and outliers so that your data sets are accurate and reliable. Techniques like statistical analysis and data mining help you identify anomalies and ensure that you’re working with data that truly represents your problem universe.

With the clean data, the next logical step is to analyze it. This involves using several techniques, such as regression analysis, and statistical modeling to recognize patterns and trends. In the end, the end goal is to learn from the data and share those insights with the business.

When it comes to skills, understanding and having proficiency in languages such as Python or R, along with expertise in various tools like Power BI, are incredibly valuable here. They enable you to perform predictive modeling and even apply machine learning techniques when needed.