What is Data Literacy

What is data literacy? ● Data literacy means knowing how to understand, work with, and talks about data. It's about being able to collect, analyze, and show data in ways that make sense. ● Every data tells a story, but we must be careful before believing the story. ● Data literacy is essential because it enables individuals to make informed decisions, think critically, solve problems, and innovate. Data Pyramid is made of different stages of working with data. Moving up from the bottom ● Data is available in a raw form. Data in this form is not very useful. ● Data is processed to give us information about the world. ● Information about the world leads to knowledge of how things are happening. ● Wisdom allows us to understand why things are happening in a particular way. What is Data Security and Privacy? How are they related to AI? Data Privacy Data privacy referred to as information privacy is concerned with the proper handling of sensitive data including personal data and other confidential data, such as certain financial data and intellectual property data, to meet regulatory requirements as well as protecting the confidentiality and immutability of the data. Best practices can help you ensure data privacy: ● Understanding what data, you have collected, how it is handled, and where it is stored. ● Necessary data required for a project should only be collected. ● User consent while data collection must be of utmost importance. Data Security ● Data security is the practice of protecting digital information from unauthorized access, corruption, or theft throughout its entire lifecycle. The most possible reasons why data security is more important now are: ● Cyber-attacks affect all people. ● The fast-technological changes will boom cyber attacks. Data Security vs Data Privacy The following best practices can help you ensure data privacy: ● Knowledge about the data: Understanding what data you have, how it is handled and where it is stored, information about how it is collected and acted upon should be clear enough. ● Minimize Data Collection: Necessary data required for the project should only be collected. ● Be transparent with your data: User consent while data collection must be of utmost importance. Also, the users should also be informed where the data is collected and why and also option for users to modify or opt out of data collection. Best Practices for Cyber Security Cyber security involves protecting computers, servers, mobile devices, electronic systems, networks, and data from harmful attacks. Do’s • Use strong, unique passwords with a mix of characters for each account. • Activate Two-Factor Authentication (2FA) for added security. • Download software from trusted sources and scan files before opening. • Prioritize websites with "https://" for secure logins. • Keep your browser, OS, and antivirus updated regularly. • Adjust social media privacy settings for limited visibility to close contacts. • Always lock your screen when away. • Connect only with trusted individuals online. • Use secure Wi-Fi networks. • Report online bullying to a trusted adult immediately. Don’ts • Avoid sharing personal info like real name or phone number. • Don't send pictures to strangers or post them on social media. • Don't open emails or attachments from unknown sources. • Ignore suspicious requests for personal info like bank account details. • Keep passwords and security questions private. • Don't copy copyrighted software without permission. • Avoid cyberbullying or using offensive language online. Types of Data Textual Data (Qualitative Data) ● It is made up of words and phrases ● It is used for Natural Language Processing (NLP) ● Search queries on the internet are an example of textual data ● Example: “Which is a good park nearby?” Numeric Data (Quantitative Data) ● It is made up of numbers ● It is used for Statistical Data ● Any measurements, readings, or values would count as numeric data ● Example: Cricket Score, Restaurant Bill Types of Numeric data: Continuous data is numeric data that is continuous. E.g., height, weight, temperature, voltage. Discrete data is numeric data that contains only whole numbers and cannot be fractional. E.g. the number of students in the class – it can only be a whole number, not in decimals Types of Data used in three domains of AI: What is data acquisition? Data Acquisition, also known as acquiring data, refers to the procedure of gathering data. This involves searching for datasets suitable for training AI models. The process typically comprises three key steps: Best Practices for Acquiring Data Data acquisition from websites Ethical concerns in data acquisition Features of Data and Data Preprocessing Usability of Data There are three primary factors determining the usability of data: 1. Structure- Defines how data is stored. 2. Cleanliness- Clean data is free from duplicates, missing values, outliers, and other anomalies that may affect its reliability and usefulness for analysis. In this particular example, duplicate values are removed after cleaning the data. 3. Accuracy- Accuracy indicates how well the data matches real-world values, ensuring reliability. Accurate data closely reflects actual values without errors, enhancing the quality and trustworthiness