Week 5 - Team Comments, Capstone Ideas, Weekly Journal, Industry Expert Interview
Part One: Team Comments
Part Two: Capstone Ideas
1. User-friendly appointment booking system for small businesses - Many small businesses have a hard time with inefficient and difficult scheduling systems. This project would create a simple, mobile-friendly appointment booking system with features like automatic reminders and calendar, significantly improving customer experience and reducing no-shows.
2. Smart navigation system or app for public spaces - Many people struggle with navigating crowded places like malls, airports, or large events. This project would create a user-friendly mobile app that provides real-time navigation indoors, showing the least crowded paths based on foot traffic data. It could also include accessibility-friendly routes for users with disabilities, improving overall user experience and convenience.
3. AI-powered smart form autofill & error detection - Filling out online forms can be extremely frustrating, especially when users make mistakes or have to keep entering the same information. This project would develop an AI-enhanced form assistant that can autofill repetitive fields using smart suggestions. Detects and corrects errors in real-time (missing required fields, incorrect formats like invalid emails or phone numbers). Provides helpful tooltips based on user behavior and maybe even a chatbot to communicate to reduce confusion. This would significantly improve user experience, reduce frustration, and increase form completion rates, benefiting all types of businesses.
Part Three: Week 5 Summary
This week wasn't too bad alhamdulillah. We had some fun assignments like the industry expert interview and some more insight into AI within one of our writing assignments. Through the week's module we learned how internships and practical experience is very important and that we need to take advantage of different opportunities.
We also viewed at least 3 capstone ideas which was very fun as they all were fantastically done and gave us a good overview of what to expect in the upcoming months.
I have been applying to internships and jobs since last year but haven't had any luck so far with it. I will continue to apply, but also work on some skills that I could write in my resume to have better chances.
Another thing we did this week was starting on the youtube video project with our team. We had a meeting with our team as soon as the module opened and went over different ideas that we could make an informative video on.
Industry Expert Interview
Disclaimer: The interviewee preferred not to be recorded; therefore, all information in this report is based on detailed notes taken during the conversation.
Introduction
Thankfully, I had the chance to interview a Data Scientist named Naeem Rehmat working for a car manufacturing company. My interest in data science and artificial intelligence as well as in how these technologies are influencing markets beyond the traditional tech firms helped me choose this expert. This expert is currently pursuing a PhD from the University of Michigan and has served as a machine learning, statistics, and generative AI expert for a while. He has firsthand research experience as well as knowledge of real world applications in the field.
Summary
Career Path and Current Role
While working on his PhD, the expert applied for an internship at the company and he was now officially beginning his professional journey into data science. It was a panel of 2 experts who evaluated his skills in machine learning and statistics in the interview process. During his internship, he worked on a Generative AI project that made him build a system which would produce marketing content using a Large Language Model (LLM). For users to utilize this, he built the backend and frontend. His manager and senior leadership was impressed by his work and therefore gave him an offer to join the company without an interview once a role opened within the department.
He plans to spend the next three years at the company to further understand its data ecosystem and contribute to high-impact projects. He wants to be a Staff Scientist where he can use his expertise and present insights to senior management to bring about business improvement.
Challenges in Data Science
He believes that in his role, handling data from multiple sources is one of the major challenges. It is very complex to manage data storage, integration and interpretation and the data needs to be accurate as any errors in data will result in poor decision making. Another major challenge is the ever-changing AI environments. He mentioned how data scientists have to continuously adapt to new AI models and technologies to stay relevant.
Emerging Trends and Technologies
The expert highlighted Large Language Models (LLMs) as a game-changer in AI. These models are changing the world of coding, documentation, and problem-solving. The different AI-powered development tools are now automating documentation, drastically reducing the need for formal technical writers. Instead of relying on Google or Stack Overflow, developers can now input code into AI models, which not only can find bugs but also provides the developer with possible solutions. This automation has extremely reduced the development timeline in regards to coding, testing, and debugging, making teams way more efficient. Tasks that used to require around 10 developers can now be handled by just 2, due to artificial intelligence.
Essential Skills for Success in Data Science
The expert stated that success in this field requires technical and soft skills. The first being their passion for continuous learning. Because AI and data science change very fast, professionals have to stay up to date. The second one is consistency. Progressing in this field and many others means that one has to be practicing regularly and get as much hands-on experience as they can. The third is problem-solving and analytical thinking. Data science is all about making sense of big data and information.
For technical expertise, he said that mastering the language of Python and key data science libraries such as NumPy, Pandas, and Scikit-learn for data manipulation and analysis is vital for this field. He also mentioned Matplotlib for visualization, PyTorch and TensorFlow for AI model training and Databricks since it is a cloud-based platform that simplifies data storage, retrieval, and visualization, which makes it much easier to manage the bigger AI projects.
Advice for Students Aspiring to Enter the Industry
The expert advised that us students should develop a strong foundation in at least one of the programming languages such as Python, Java, C++, or R. When one is well versed in one language, learning others becomes much easier. He also emphasized mathematical skills, mainly in probability and statistics, as they are essential for AI and data science.
Lastly, communication and presentation skills are crucial. A professional needs to make sure they can present well to other professionals and even non-technical workers as these roles mostly require this.
Reflection
After conducting this interview, I now have a deeper understanding of how AI and data science are even changing industries like the automotive industry. I knew that AI had been streamlining workflows and speeding up everything from coding to debugging and documentation but had no idea it would be at this scale. Secondly, I learned that these industries heavily rely on data accuracy since business insights gathered from data influence large decision making. A big takeaway for me was the need for data scientists to constantly evolve with the new technologies. Today’s AI tools look very different from a few years ago and success in this field means you will need to commit yourself for a lifetime journey of learning. The interview also confirmed that technical and soft skills are crucial, because even the most accurate AI models are useless if communication of the results cannot be articulated to decision makers.
Future Steps
Inspired by this interview, I am planning to continue learning Python and deepen my understanding of AI-focused libraries like TensorFlow and PyTorch. I also want to practice working with datasets to improve my data analysis skills. I need to enhance my mathematical skills, especially in probability and statistics. A big weakness of mine is my communication and presentation skills so working on that will help me better show technical insights. And lastly, I want to look more deeply into the different software data scientists use for their work. This interview reinforced my interest in AI and data science, and I am now even more motivated to pursue this path professionally.
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