2. Understanding Key Data Skills

Overview

There are many different data analytics skills that you should know to be fully data literate. We’ll explain all these skills in detail in this lesson.

Summary

  1. Lesson Goal (00:10)

    The goal of this lesson is to learn about the most important data literacy skills.

  2. Understanding Data (00:38)

    The most important data literacy skill is understanding what data is and how it can contribute to your business. You should consider questions such as where your company gets its data from, and how you can use that data to tell stories and make decisions in your business. You also need to know how to keep your data safe and secure.

  3. Analyzing Spreadsheets (01:30)

    After understanding the basics, the most important data skill is spreadsheet analysis. Spreadsheets are an incredibly versatile tool in data analytics. They can be used for various purposes, from creating a few calculations to building a complex financial model. Although spreadsheets are rarely the perfect tool for any task, they’re a usable tool for virtually any business situation.

  4. Transforming Data (02:33)

    Data transformation allows you to manipulate datasets into a useful format before you start analyzing it. Real-world datasets tend to be messy and disorganized, so the ability to transform datasets before analyzing them is a key data literacy skill.

  5. Visualizing Data (03:52)

    Data visualization is a key skill in communicating the results of any data analysis. Visualization turns dull numbers and figures into compelling visualizations that people will actually notice. As a result, visualization is another key data literacy skill.

  6. Advanced Analytics (04:26)

    Data analytics makes many people think of advanced techniques like artificial intelligence and machine learning. Even if you don’t use these methods in your day-to-day work, it’s still useful to understand the non-technical fundamentals so that you understand where data and analytics is going in the future.

  7. Communicating Messages (05:07)

    Finally, you need to be able to communicate the results of your data analysis to people inside and outside your company. This means you’ll need the ability to write documents, create presentations, and keep on top of your emails, in order to maintain the level of organization required to communicate your data literacy.

Transcript

There are wide variety of skills you need to know in order to become data literate. In this lesson, we'll learn about the most important data literacy skills you should have. The most important data literacy skills fit into a six-step framework containing the following steps, one, business understanding, two, data understanding, three, data cleaning, four, data exploration, five, predictive modeling, six, data communication. The first step is business understanding. It may seem obvious, but the most important skill you'll need is to understand what data really is and how it works. You might instinctively think this is obvious. But for example, do you know where your company gets its data from? Is it stored in a neat database or is it spread across a bunch of Excel files in your email inbox? How can data help you tell a story that can then be used to make business decisions? Do you know how to keep your data secure? If you've ever used the word password as a password to some account, the answer is probably no.

Think of it as learning the building blocks of data literacy. There's no value in learning how to process or visualize before before you know the fundamentals of how it fits into your company.

The second step is to understand your data. The way way to do this is using spreadsheets. Spreadsheets, such as those used in Excel or Google Sheets, are the most versatile data analytics tools in existence. Most people associate spreadsheets with doing a few calculations and maybe creating a few charts. But in reality, their capabilities go well beyond that. Spreadsheets can be used for a wide variety of business purposes, from creating charts and dashboards to valuing an investment. They can even be used to understand the consequences of two large companies merging. You might think that complex financial transactions like this would involve specialist software applications, but actually a simple spreadsheet model can do the job. Spreadsheets are rarely the perfect tool for any one task, but they're an effective tool for a lot of tasks. As a result, a good knowledge of spreadsheets is essential in becoming data literate. The third step in our framework is data cleaning. In the real world, many of the data sets you access won't come or be presented in a neat and tidy format. They'll need to be manipulated and transformed into a useful format before you can really analyze them. This process forms a large part of a process commonly known as extract, transform, and load, or ETL for short. The extract process refers to the reading of the data from a database, often from a range of different sources. The transform process converts that data gathered from the extract process into a format that can be used elsewhere. Data may be combined and reformatted using rules or lookup tables in preparation for the final stage of ETL, the load process. The load process involves writing the transformed data into a selected destination database, making it ready for consumption.

ETL can be performed through sophisticated no-code applications, such as Ultrix or Tableau Prep. But, a basic understanding of the SQL programming language can also allow you to move data through this process.

The fourth step in our framework is data visualization. This converts dull numbers and figures into compelling visuals.

If you want to be able to communicate and have people actually take notice of what you want to say with your data, then a grasp of visualization techniques is key.

An application such as Tableau or Power BI can create striking visualizations and dashboards that will get you and your work noticed.

The fifth step in the framework is predictive modeling. When many people think of data and analytics, they might think of advanced subjects like artificial intelligence or machine learning. If you've heard of these subjects before, you might feel that they aren't for everyone. And today you might be right, but there's no doubt that areas like this are growing in popularity. Many simple business tasks have already been automated and more are likely to be followed in the future. While understanding the fundamentals of AI can be useful for everyone, understanding the finer details could be particularly useful if you're in a analytical role.

The sixth and final step in the framework is data communication. All the data analysis skills in the world won't help you if you can't then communicate your message to other people inside and outside your company. If you have business experience, it's very likely that you've sat through a boring business presentation, had to read a long and complicated document, or struggled to find some important email in a cluttered inbox. To avoid being the person who subjects others to such misery, you will need a good grasp of how to communicate the results of your data analysis in an effective manner using the charts and dashboards mentioned earlier.

This concludes our look at the main data literacy skills that are worth having. But why are these skills important and how do you acquire them? That's where Kubicle comes in, as we'll learn in the next lesson.

a b c
Understanding Data
Learning Data Literacy

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