Week 2 Learning Journal Post (2-3 Hours)

Week 2 Learning Journal Post (2-3 Hours)

 

Part 1:

Top 3 items that I am good at would be effective reading, taking good notes & effective thinking skills. Post reading the google doc, I tend to reflect on my reading and make sure I understand it. Depending on the topic I will take notes accordingly, if it’s conceptual I might write notes if it’s programming related I will type them. Post to reading I tend to write a reflection on what I’ve read even if it’s a few sentences. The goal is to explain as if I’m teaching someone. The top 3 weak points are effective scheduling, finding a place to study, and extracting important details. Being transparent my schedule has always been canvas but I recently started using notion to make a week todo list. It could be better. Finding a place to study is a challenge. Sometimes being at home is the most convenient but it can be distracting. Lastly, extracting effective info from readings sometimes is difficult as I fall into thinking everything is important.

Part 2:

Weekly log

Sheet

Part 3:

When partaking in a project it's important to thoroughly understand the goals behind the project. This means understanding how it will affect stakeholders which is alternatively known as the project's scope.

Deliverables can be tangible or intangible when it comes to a project. The key is these deliverables are pre determined by a project designer and determine the outcome of the project. Executing these deliverables comes from the work breakdown structure which is how process and sub goals are distributed amongst a team.

Gantt Charts are a project management tool that has been around for quite some time and is still used in today's work. It has an overview structure where project milestones are set across a horizontal timeline. Each milestone has a vertical bar that has a list of sub tasks that are to be completed. In today's tools we have complex software that can help build a large Gantt chart to match the growth of projects today.

Part 4:

OtterParsing:

This is a tool that is designed to parse resumes and build data about each candidate. It uses a library called prodigy to help with parsing using regex to focus on important information like schools, work experience, name, location etc. This project was well done as it showed real code snippets of the finalized project and had a chart of how the model workflow was gone about. I think it was sufficient and overall was done well. The database was straightforward and easy to understand.

Identifying Brain Tumors:

This was a project where a student took an existing algorithm that trains a model to assist with identifying brain tumors through MRI scans. Surprisingly the tools used were some popular ones I myself have even used like PyTorch, Torch Vision and other python libraries. What's used is Image segmentation and an Improved baseline model to an improved model using U-Net. I enjoy the project and its goal to improve medical technology as this would be a potential field I would be interested in. My suggestion would be to add more content that breaks down the models training process alongside data collection and preprocessing. I feel the presenter spoke well and was very knowledgeable but visually it could have had a bit more. Overall good presentation 

dKOMP:

dKOMP is an organizational tool that helps employers manage roles and positions amongst the team. It uses real time collaboration so users can view changes in real time. It uses Express JS with Microsoft API to allow usage with Microsoft ecosystem. This tool also builds a data base that allows employers to see potential gaps for growth and repositioning suggestions using internal data. The application and presentation was done very well explaining its tools and how to use this application from a business perspective. My suggestion would be to improve content on slides to better showcase the app. It felt the app and presenters outperformed the slides. I enjoyed this project as I am also interested in full stack development. 

Part 5:

This week I learned about the skills it takes to be a full stack developer in machine learning. It does take a lot of data analysis and technical skills that require extra time and effort which is what I am after. Looking at the capstone project was beneficial and interesting as we can prepare putting more effort into thought behind this project. I feel this week was good for personal reflection on career goals.