Digital Literacy and Data Analysis

Digital Literacy

As the business landscape rapidly transforms in the digital age, it's more important than ever for employers to prioritize candidates with robust digital literacy skills. Digital literacy empowers individuals to effectively gather and share information using technology while staying vigilant about online risks. Throughout my learning journey, I have developed a strong command of digital literacy, equipping me to help organizations thrive and boost their performance and profitability. Embracing these skills enhances individual contributions and positions businesses for success in an increasingly interconnected world.

Common Security Threats

Spam

Sent as emails or text messages
Normally unsolicited commercial messages
Used to trick people into sharing sensitive information

Phishing

Attempts to steal sensitive information such as:
- Passwords
- Usernames
- Credit card information

Malware

Tricks people to install malicious software onto a device
Types of malware include:
- Viruses
- Worms
- Trojans
- Adware
- Spyware
- Ransomware

Hacking

A form of creative problem solving that takes advantage of computer and network vulnerabilities
Why hacking occurs:
- Intellectual curiosity
- Personal protection
- Enjoyment
- Activism
- Financial gain
- Criminal activity

Source: Schwartz (2019)

Evaluating Sources

Written by staff members, journalist, or freelance writers
Rarely includes reference or bibliographies
Uses colorful images
Intended for the general public
Uses a casual writing style

Source: Bednar (2020)

Written by scholars, researchers, and experts
Have reference list or bibliography
Peer-reviewed by other researchers
Use tables or graphs that visualize raw data from research
Intended for scholars, researchers, and students
Uses a formal writing style

Linkedin Learning

Data Analysis

Data analysis is a game changer for any organization aiming for success and a strong reputation. Throughout my learning journey, I discovered how to craft engaging charts, graphs, and diagrams that turn raw data into actionable insights. I also learned the importance of ethically reporting statistics on an organization's performance, which is crucial for transparency and trust. Moreover, I gained skills in developing strategic plans driven by data, ensuring that decisions are both informed and impactful. As Johnson (2022) points out, harnessing the power of data analysis gives organizations a competitive edge and equips them to tackle business challenges more quickly and rigorously. Embracing data isn't just an option; it's necessary to thrive in today's fast-paced world!

Descriptive analytics focuses on examining past events to understand what has occurred. Data in this context is categorized into two types: quantitative and qualitative.

Quantitative data is expressed in numerical form, while qualitative data consists of non-numerical information that is generally descriptive. Examples of quantitative data include annual sales figures, performance review ratings, and monthly profitability reports. On the other hand, qualitative data may involve summaries of written comments collected from customer suggestion boxes.

It's important to note that both quantitative and qualitative data should be based on a defined set of observations.

Predictive analytics addresses the question of what might happen in the future. While it's nearly impossible to predict the future with certainty, understanding customer patterns can provide valuable insights into additional products they may be inclined to purchase alongside their current selections. Therefore, predictive analytics helps identify which future events are most likely to occur. By recommending additional products, companies can increase their profits at each point of sale.

One of the most challenging questions leaders face is how to move forward. These questions are complex because they involve several key areas, including past events, future predictions, and business acumen. However, two essential components of prescriptive analytics are choosing the right data and understanding different situations. If careful consideration is given to these two areas, successful analyses and conclusions can be reached.

Source: Johnson (2022)

To learn more about my education journey, please visit my Undergraduate Work page!

References

Bednar, M. (2020). Popular and Scholarly Sources Tutorial [Video]. OU MyMedia. https://mymedia.ou.edu/media/Popular+and+Scholarly+Sources+Tutorial/0_4jl41img

Johnson, J. (2022). Business Leaders and Data Analytics - Data Analytics for Business Professionals Video Tutorial [Tutorial]. LinkedIn. https://www.linkedin.com/learning/data-analytics-for-business-professionals-14936642/business-leaders-and-data-analytics?autoSkip=true&resume=false&u=2150050

Schwartz, M. (2019). Chapter 3: Understanding Digital Citizenship. Digital Citizenship Toolkit. https://pressbooks.library.torontomu.ca/digcit/chapter/chapter-3-digital-citizenship/

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