Data science is an extensive field that includes programming statistics, algorithmic math, and machine learning. These skills are not required to be data literacy. If they did, then they’d be a data scientist!
There are many data literacy software that can be used to teach data literacy to employees. Employees must be capable of reading and understanding data. An additional skill that employees need is the ability to use software programs to manipulate data. Employees should understand how data can benefit them.
Why is it important to establish a data literacy learning program?
Companies and their workers are constantly overwhelmed with data. Therefore, it is critical that employees can think critically about data and be able to problem-solve as well as use data analytically.
These skills are very difficult to learn or acquire on a whim. Management and employees cannot acquire data-related analysis skills on their own. This will cause inconsistent and ineffective results.
Employers should seek to learn at least 7 of each of the following skills: understanding data science and analysis, creating value from it and creating value.
- Data concepts & their application
Employees should understand the basics and applications of data. They should also be able to comprehend the underlying issues and potential problems related to data. They should also understand the value of data and concepts such as data security, data ethics, and data security.
- Access and collection
Now that employees have a solid understanding of the basics, they can access and location data from multiple sources. Employees should be able to identify the names of each source and the information contained within.
They should be able to assess the trustworthiness, utility, and validity of data source sources. It is, therefore, crucial to map and address data accessibility issues and automation in the planning stages. We will be discussing this later.
- Data management and synthesis
Another important skill is being able to analyze, organize, make sense of and interpret multiple data types, depending upon the context. Employees should know how to combine different data sources and their effects.
- Relevance
It is very difficult for employees to be data literate if they don’t understand the data they need. They feel overwhelmed when the results they desire are not being achieved. Employees should understand what data is relevant for which problems or questions. They should also be capable of understanding the relationships among data sources.
- Data-driven inquiry
Employees should have the ability to formulate and identify hypotheses. This involves data analysis. They should either have the ability to help or be able to search for someone who can.
This is how one can build an internal bridge between the team of data scientists and employees. Data scientists help employees with data expertise translate information into actionable steps.
- Data tools
It is continually changing what data exploration tools, platforms, and analytical tools look like. Employees should have a working knowledge of data analysis techniques, methods, and how these can be used.
- Data communication
Employees should feel confident in communicating data to multiple stakeholders. They should understand how data can help other people, and why it is important to them. They should be familiar with data visualization methods and be able to judge the data’s validity and errors. For more info please visit Data science course in Hyderabad.
