Support Skills for Power BI Developers

As a basic requirement, every data analyst should handle at least one business intelligence tool. In our case, if you are interested in Power BI and decide to take that learning path, you will have to start learning each of the parts that make up the creation of a report in Power BI. This means you must know about Power Query for data extraction and transformation, have notions of data modeling to shape and create indicators for your reports using DAX, know how to choose the best chart to display the information that will be in your report, and know the best way to share your reports and manage them in the Power BI service. Knowing each of these parts and mastering them will take you to an advanced level of using the tool. However, the profile of a data analyst (interested in Power BI) goes beyond this, as it should include additional skills that will make the workflow smoother and make your profile as a data analyst more valuable.

In this...

Technical requirements

• Microsoft SQL Server

Importance of SQL for a data analyst

As a data analyst, having a query language in our skills inventory is essential. In large companies, analysts may have the luxury of requesting queries from the database department for use in their reports. However, not all companies have the budget to have a specific department for this, and in most cases, it is the responsibility of the data analyst to create their queries and retrieve the data they need from databases.

SQL is the most used query language for both managing and retrieving data from databases. SQL is a standard of both the International Organization for Standardization (ISO) and the American National Standards Institute (ANSI). Here is a list of the existing standards so far:

• SQL-86
• SQL-89
• SQL-92
• SQL:1999
• SQL:2003
• SQL:2006
• SQL:2008
• SQL:2011

Most of the existing databases today, such as Oracle, MySQL, PostgreSQL, and MS SQL Server, use this query language. Although you can use the SQL...

Programming languages

As a data analyst, another skill that I recommend you start including in your toolkit if you don’t already have it is a programming language, and even better if this language is specifically oriented toward data analysis.

Although solutions such as Power BI offer you countless functions to create any type of indicator, there will always be gaps or improvement opportunities that many programming languages already include, such as Python.

Python is currently the most popular programming language for both data-related areas and general programming. This means that you can use Python as a backend programming language, but also use the libraries it contains to analyze data, perform complex mathematical calculations, or create predictive models.

Among the existing libraries that you can use within Python for data analysis, we have the most common ones, which are numpy, pandas, plotly, and scikit-learn. We will learn a little more about them in a bit...

Design skills for a data analyst

A skill of little interest in programming or data analysis is design, and many may even see it as unnecessary. However, remember that at the end of the day, you will communicate with visual objects, and good design will make that communication more effective. Moreover, there must be some truth to the saying, “A picture is worth a thousand words.”

Don’t be confused when I say you should have design skills. What I mean is that as a data analyst, you should also think like a designer and ensure that your report is affordable, accessible, and aesthetic. How? Let’s find out.

Affordable design

In terms of design, when we talk about affordability, we refer to the close relationship between humans and things, but above all the actions that can be performed on them. For example, a light switch in a room is designed to be easy to find, easy to identify, and easy to operate, making it a simple task to turn on and off the lights...

Soft skills

In addition to the technical skills that help you solve daily tasks as a data analyst, there are subjective skills or skills tied to the person that are difficult to quantify. These are called soft skills. Every data analyst should develop at least one of the following soft skills for career complementation and professional growth.

Communication and collaboration

Knowing how to import data from an SQL database, transform it, and create measures in Power BI does not mean you are a good analyst if you don’t know how to communicate what you found. Company executives expect to make decisions based on the data you found, and it is your responsibility as an analyst to transmit the information as clearly as possible.

Problem solving

To err is human, and companies and technology are not perfect. If the data between the transactional system and the report is not matching, you as a data analyst must be able to know where to start addressing this situation and how...

Summary

In this chapter, we started covering the importance of knowing SQL and how it can be helpful for you to learn this language. I also showed you some key programming languages used in the field of data analytics and continued with a few key concepts you should take care of when designing a report. In the last section, you learned about the most relevant soft skills for a data analyst professional.

In the next chapter, we will start talking about Power BI and its workflow.