What do we mean by the phrase ‘data literacy?’ Simply put, being data literate means being able to understand and work with various types of data.
Obviously it’s advantageous to have a basic grounding in data literacy even if you’re not working with data every day.
You’ll need data literacy skills when you’re reading the news, for example. Data is a part of everyday life.
If you work either in tech or in a data-based role you might be aware of the fact that data and technology are becoming increasingly intertwined. To be data literate means to be able to know your way around machine learning tools, for instance. It also means that you have an appreciation of accuracy, facts and how they can inform everything from business decisions to personal health.
Technology enables data professionals to work with huge sets of data to find out more facts faster.
If you’re thinking of improving your own data literacy skills, here are a few pointers on where and how to start.
Technical skills for data literacy
There are a lot of technicalities to working with data nowadays. Professionals can use tech tools to collect, format, clean, process and identify patterns from data.
Data cleaning refers to the process of ensuring that data is accurate and correctly formatted. Mining data involves trawling through huge raw datasets to extract data.
Once the data is warehoused, or kept in a specific system, it can be used and learned from by people other than data scientists.
Business analysts are not hugely technical but they will often draw from data that has been cleaned and mined by their more technically-minded colleagues in order to help businesspeople make informed decisions.
Recently, SiliconRepublic.com looked at visualisation skills and why they are important for business intelligence professionals.
But it is not just business intelligence professionals who need to have data visualisation skills. Anyone who works with data and has to communicate data-based findings to others should possess visualisation skills. These skills are a little bit easier than other data tech skills to pick up. You can use tools, for example Venngage’s chart maker tool, to present charts in reports and infographics.
Non-tech skills for data literacy
Critical thinking, research and communication are three of the most important skills and qualities to possess when working with data. Without those, any technical skills you have are not going to be of much use.
You’ll need critical thinking and a questioning mindset to properly assess whether the data you are working with is useful to you. After all, if your dataset is not diverse or there is a flaw in the collection method, that’s going to skew your results and findings. It’s very important to be aware of how external factors can affect the quality of data.
Communication is important when it comes to relaying your findings to others. Perhaps you are a business analyst, for example, charged with helping companies make decisions based on their data. Not everyone is able to instantly understand complex data so you’ll need to be able to break it down.
Online resources for developing data skills
You can read about real-world examples of how data is used in everything from fighting crime to keeping people informed about health on its blog.
There is also a section on the site for learners who want to delve a little deeper into some of the concepts the blogposts mention. Think quick individual modules on data storytelling, understanding aggregations and advanced analytics concepts.
Tableau is another good website for breaking down some of the fundamentals of data literacy. It has a free programme called Data Literacy for All, which is self-paced.
Pluralsight, Coursera and Udemy are all great bets for online data literacy courses also. Udemy offers a course that teaches learners how to work with data, while Pluralsight’s data literacy course claims to bring non-technical concepts associated with data to technically-minded people.
Or, if you want to do a course with an Irish university, University College Dublin’s Professional Academy runs a series of courses, including introductory data analytics and data analytics essentials.
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