In this tutorial, we will cover Unit 2: Data Literacy of PART B – Subject Specific Skills.

Unit 2: Data Literacy

In the table below, we have detailed the division of Unit 2 into subunits and topic descriptions.

2.1 Basics of Data Literacy

Let’s understand the concept of data literacy, types of data, and their sources. We will see data visualization and data dashboard in detail.

• Data Literacy and Its Impact

Data Literacy is a combination of two words, ‘data’ and ‘literacy’, which means knowledge of data. One who can read, understand, and work with data is called data literate. A data-literate person can easily collect, analyse, visualize, and extract useful features of data.

Data Pyramid:

Let’s understand data in the form of a pyramid moving up from the bottom.

● Data is available in a raw form and is neither useful nor easily understandable by any algorithm.
● Data is processed to extract information. (It answers Who?, What?, When?, Where?)
● Extracting knowledge from information on how things are happening in data?
● Understanding why things are happening in that particular way converts knowledge into wisdom.

• How to become Data-Literate?

There are 6 steps to becoming Data-Literate:

1. Understand Data Types

Understand that data is structured or unstructured, qualitative or quantitative.

2. Collect and Organize Data

Collect data from trusted sources and organize in an Excel sheet or CSV files. Use this data ethically.

3. Clean and Prepare Data

Clean data by removing errors in data, handling missing or duplicate data, and preparing data for further analysis.

4. Analyse Data

Analyse data for basic statistics like mean, median, mode, and range. Create various graphs like bar graphs, pie charts, histograms, scatterplots, box plots, and many more to find patterns in data.

5. Interpret Data

Interpret data by looking for trends, relationships, or anomalies in data.

6. Communicate Data Findings

Communicate findings in data using dashboards that present graph visuals, reports, or presentations.

• Data Privacy and Security

Data Privacy and data security seem to be the same terms, which are used interchangeably, but they are different from each other.

Data Privacy

Data Privacy governs how data is collected, shared, stored, and used. It refers to the proper handling of sensitive data, including personal, financial, and intellectual data. It should meet all regulatory requirements defined by the government while maintaining confidentiality and immutability.

Examples where data privacy compromised:

1. Download software or application from untrusted websites.
2. Accepted the terms of service without reading.
3. Using weak or common password.
4. Post personal information on social media.
5. Clicking on phishing links or fake emails.
6. Use of public wi-fi without protection.

Data Security

Data Security is protecting data from attackers who want to misuse it. Its aim is to protect digital information from unauthorized access, corruption, or theft throughout its entire lifecycle.

Reasons why data security is more important now are:

1. Cyber-attacks are more frequent and affects the world.
2. The evolution in technologies like Generative AI led to increase in cyber attacks.
3. More information stored is online like cloud.
4. Global connectivity increases the risk of security.

• Cyber Security

Cybersecurity provides security to data, networks, computers, servers, and mobile devices from attacks from malicious sources. Cybersecurity is very critical and nowadays even more important as our systems become more vulnerable with the increase in Generative AI applications.

In case you want to learn about Cybersecurity in the era of Generative AI, click on the paper published here.

Best Practices for Cyber Security are:

2.2 Acquiring Data, Processing, and Interpreting Data

• Types of data

• Sources of data

• Best Practices for Acquiring Data

• Features of Data and Data Preprocessing

• Importance of Data Interpretation

• Tools used for Data Interpretation

2.3 Project Interactive Data Dashboard & Presentation

• Data visualization & its importance

• Visualization of data with a No-Code tool

• Create a simple and interactive chart with a No-Code tool

Stay Tuned!!

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