#engineering college

The last week has been a lot. I was studying for the exam I took today, and I can finally say that I've studied way too much. Some of the results are already out, and while I did not get 100%, nor I will not get the best score, the questions were much easier than I thought. Like, I did learn most of the human bones and major muscles names just so I'd get closed questions with the definitions of biomechanics? Am I mad about it? Yeah, because I didn't have enough time to study for other parts, but also no, because I'm way ahead with the material for the next semester. Now I'm only lacking 1 point to pass and I'm still awaiting answers to 5 questions.

Today I'm just chilling out, did Duolingo, went home to grab a dress and tomorrow I'm going with my best friend to a college ball. I still have to submit and correct some reports from last week or month, respectively, and finally start studying biology for matura. The only thing this exam made me realise is that I really want to study medicine, and a near-medicine degree may not be the best replacement. Anyways, here are the snippets from Forest from the last two weeks:

Learning to code and becoming a data scientist without a background in computer science or mathematics is absolutely possible, but it will require dedication, time, and a structured approach. ✨👌🏻 🖐🏻Here’s a step-by-step guide to help you get started:

1. Start with the Basics:

- Begin by learning the fundamentals of programming. Choose a beginner-friendly programming language like Python, which is widely used in data science.

- Online platforms like Codecademy, Coursera, and Khan Academy offer interactive courses for beginners.

2. Learn Mathematics and Statistics:

- While you don’t need to be a mathematician, a solid understanding of key concepts like algebra, calculus, and statistics is crucial for data science.

- Platforms like Khan Academy and MIT OpenCourseWare provide free resources for learning math.

3. Online Courses and Tutorials:

- Enroll in online data science courses on platforms like Coursera, edX, Udacity, and DataCamp. Look for beginner-level courses that cover data analysis, visualization, and machine learning.

4. Structured Learning Paths:

- Follow structured learning paths offered by online platforms. These paths guide you through various topics in a logical sequence.

5. Practice with Real Data:

- Work on hands-on projects using real-world data. Websites like Kaggle offer datasets and competitions for practicing data analysis and machine learning.

6. Coding Exercises:

- Practice coding regularly to build your skills. Sites like LeetCode and HackerRank offer coding challenges that can help improve your programming proficiency.

7. Learn Data Manipulation and Analysis Libraries:

- Familiarize yourself with Python libraries like NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization.

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Material Science Prof: "Engineers who know politics can do anything. Is politics harder than calculus? No! So why aren't engineers taking over the world?!"

Student A: "Cause we don't care."

Mediation is an alternative method of resolving disputes without resorting to the courts. It is a structured, voluntary and interactive negotiation process where a neutral third-party uses specialized communication and negotiation techniques to help the parties in fulfilling their stated objectives. As a party-centred process, it focuses on the interests, needs and rights of the parties.

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