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Running Start/AD450 - Data Science Development/Discussion - What is a data engineer.md

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+- **Role and Impact in Business**
+    - Discuss the contribution of data engineers to the success of a business. How do they enable data-driven decision-making?
+    - Data engineers contribute to the success of a business by giving access to important data about what is happening. Without data about what users want, their activity, and the success of the product it is very challenging for a business to grow, improve and create better products for its customers. Providing data allows for making decisions that align with what is happening and not just based on what seems correct or what some people think might be best. It can also help to measure success quantitatively to ensure that something is succeeding or to figure out when it is not.
+- **Essential Skills and Tools**
+    - Identify and explain the most crucial skills for a data engineer. Why are these skills important, especially in the context of big data technologies and ETL processes?
+    - Some of the most crucial skills for data engineers are being able to use languages such as Python, R and SQL as well as understanding different pipelines, databases and storage schemes. Being able to work with different programming languages is important because it unlocks many powerful tools for quickly and efficiently collecting, cleaning, and organizing data. Understanding different pipelines and schemes is also important so that data scientists are able to use what is best for the company they work at.
+- **Data Engineers vs. Data Scientists**
+    - Compare and contrast the roles of data engineers and data scientists within a data analytics team. How do these roles complement each other?
+    - Data engineers focus more on gathering data and ensuring it is ready for analysis and able to be gathered and analyzed. Data scientists focus on analyzing the data and looking for patterns significance and future trends. These two roles compliment each other by providing the two large steps of the process that are collecting and processing the data and analyzing it for use.
+- **Industry-Specific Challenges**
+    - Examine the unique challenges data engineers might face in industries like healthcare, retail, and financial services. How do these challenges impact their work?
+    - Industries such as these are more data intensive and require the processing and analysis for large quantities of data. In order to effectively operate companies in these industries must use data to impact their decision making so that they can make the best decisions to be the most profitable that they can be. The large quantities of data needed can make the job of data scientist more challenging since larger datasets can be more unwieldy and require more time for cleaning and preparing the data.
+- **Evolution of the Field**
+    - Analyze how the field of data engineering has evolved and predict future trends. Consider the impact of emerging technologies and data volume growth.
+    - The field of data engineering has become much more important for companies since using data to analyze trends and consumer behavior is vital for companies to stay competitive. Because of this data engineering has become a much more sought after position and as the role has also expanded to meet demands it has also split into many different more specialized roles. Along with more course and education around these topics the role has expanded and specialized to provide companies with the data they need. In the future this role will likely continue to expand, especially with companies continuing to collect more data on consumers and try to predict their behavior more.
+- **Career Pathways**
+    - Discuss the educational and experiential pathways beneficial for aspiring data engineers. Assess the value of certifications, degrees, and hands-on learning.
+    - There are several things beneficial for aspiring data engineers such as university degrees, projects and certifications. University degrees can include those in applied mathematics and computer science. Projects can also useful to build a portfolio and show potential employers what you can do and what you have worked on. Certificates can also be useful to show employers specific skill sets that you have learned and are proficient in. All of these options can be helpful to improve your skills and be more competitive in the job market.
+- **Ethical Considerations**
+    - Explore the ethical considerations in data engineering, focusing on data privacy and security. How should data engineers approach these issues responsibly?
+    - Privacy and security are important considerations for data engineers especially as more and more data is harvested from users by large companies. There likely isn't much data engineers can do at most companies since they are likely not the ones making the decisions about what data to use and record but ensuring that they follow the terms of service or privacy policy is important. Securely storing private information is also vital so that it is not leaked unintentionally.
+- **Real-World Applications**
+    - Provide examples or case studies where data engineering played a critical role. Discuss the solutions implemented and their effectiveness.
+    - One example that was discussed in one of the articles was about LinkedIn. Near the start the platform would just let users find others and create connections on their own. Someone had the idea for the platform to suggest others to users to increase the number of connections that they would make and used data on the platform to suggest users. These suggestions massively increased engagements and connection on the platform since it exposed users to others that they might not have connected with using data engineering and analysis.
+- **Collaboration with IT Professionals**
+    - Describe how data engineers collaborate with other IT professionals. Highlight the importance of teamwork and communication in successful data projects.
+    - Data engineering is not the only aspect of most projects and people to help with networking, UI design, backend development and more are necessary for projects to succeed. Communication is important to ensure that all of these different aspects work together and form one cohesive product or system.
+- **Future in AI and Machine Learning**
+    - Discuss the evolving role of data engineers in the context of AI and machine learning. How must data engineers adapt to these technological changes?
+    - Data engineers are necessary in the development of AI and machine learning systems since large amounts of data are required for them to be developed and function. These systems can also provide new tools for analyzing and organizing data and help to make the work of data engineers more efficient. Learning to effectively use these new tools is important to stay competitive and successful as a data engineer.