Four Skills essential for data analysts in Tableau

Right now, a company’s capacity to leverage data is certainly its biggest strategic edge. Naturally, businesses are also growing their data workforces and investing in the hiring and training of key personnel for positions like business intelligence and data analysts.

What is Tableau?

Tableau is a fantastic business intelligence and data visualization tool for reporting and analyzing massive volumes of data. Salesforce acquired Tableau, a 2003-founded American company, in June 2019. It enables users to create a range of charts, graphs, maps, dashboards, and stories with the goal of displaying and analyzing data to assist in business decision-making.

Tableau is one of the most popular business intelligence tools because of all of its unique and fascinating features (BI). Let’s take a closer look at a couple of the most crucial Tableau Desktop features. Now that we know what a tableau is, let’s look at some of its essential features.

Skills for data analysts in Tableau

• Tableau Desktop
• Literacy in data analytics
• Literacy in data visualization
• Querying data with ANSI SQL

Tableau Desktop

Tableau Desktop is the most widely used tool among analysts for connecting to, interacting with, and visualizing data. It can easily obtain data from a number of data sources, work with and combine data from many sources, and quickly develop analytical content meant for wide distribution.

Thanks to its dynamic interface and drag-and-drop technique, you can stay in the flow of the analysis. Tableau Desktop is a necessary tool for data exploration, curation, display, and communication in analysis.

Analysts need to be skilled at building different views or visualizations and utilizing Tableau Desktop’s built-in features. Highly trained analysts ignored the “show me” menu in order to create unique content that directly addresses complicated business concerns.

Working from the marks card, being at ease doing operations on top of views using table calculations, and being proficient at modifying data granularity using the level of detail expressions are all requirements for achieving true mastery. In the world of self-service analytics, you need to be adept at using actions and sets as well as incorporating interaction to allow end users to dig into their urgent queries.

Literacy in data analytics

Every day, data sets must be questioned, questions must be answered, and advice must be given based on interpretations. As a result, you need a solid grounding in mathematical and statistical concepts. Summarization and aggregation of data are frequently required in order to synthesize and understand it. Using the median and quartiles rather than the average, for example, can aid in obtaining an accurate picture when dealing with outlier-sensitive data.

Analysts are skilled in a variety of techniques, including simple A/B testing, building models that fit and describe your data, ANOVA, and hypothesis testing.

You should understand how data connects to itself in addition to knowing the relevant statistical procedures to utilize. The ability to comprehend diverse data structures and storage methods are necessary for analysts. They also need to know how to construct sound data sets utilizing concepts like primary keys, many-to-many relationships, dimensions, and facts.

Literacy in data visualization

Your capacity to clearly convey the insights and conclusions you draw from data is the last and, arguably, the most crucial talent you need to master. Every analyst should be very careful about how they communicate their findings. Due to this,

• Utilizing various chart kinds in a useful manner in light of the study.
• Recognizing the size and position of the pre-attentive qualities.
• Streamlining methods to enhance comprehension and cognition

These principles of data visualization literacy give people the foundation to make insightful deductions from your work. Today’s top analysts are able to modify best practices to suit their audience and are knowledgeable about the right graphic to use while addressing the most common business concerns.

Querying data with ANSI SQL

To fully utilize data, analysts must be fluent with Structured Query Language (SQL). The most used computer language, SQL, is essential for gaining access to data stored in databases. If you know how to extract data, build joins, develop stored procedures, create aggregations, and work with various data types, getting the data you need will be a lot simpler.

Your ability to effectively offload taxing computations and modify structure at the data model level may also be a determining factor in your ability to create complicated visuals. Because they will be proficient in these concepts, a good data analyst will be able to work with data engineers and architects to assist in the building of distinctive data models for analysis.

Conclusion

It is clear that data visualization has a wide range of potential applications in different sectors as our brief study on the topic comes to a close, but we also need to be aware of its practical and ethical implications. The prior chapters discussed some essential theoretical and practical considerations to keep in mind when constructing a data visualization. We have also examined and analyzed a lot of examples of data visualization, picking up useful advice and insights along the way. As we’ve shown, it’s challenging to produce an ethical and effective data visualization. In this chapter, we’ll talk about the future of data visualization and additional resources for data visualizers.