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About this project

This repository contains post-elementary math course in form of interactive notebooks. The goal of this course is to deepen understanding of mathematics and develop mathematical intuition based on visual representations of more abstract objects. The level of the course is aligned to the Polish maturity exam and overlaps with AS/A level.

Table of content

Linear function
Quadratic function
Cubic, quartic and reciprocal functions
Exponential and logarithmic functions (release: 15.01.2023)
Trigonometric functions (release: 22.01.2023)
Vectors in 2D (release: 29.01.2023)
Vectors in 3D (release: 05.02.2023)

How to run?

This project is written in python 3.10. The required libraries include, but are note limited to: numpy, sympy, pandas, matplotlib and seaborn. All necessary libraries are listed in requirements.txt file.

Instruction for Windows:

Download Python 3.10 from: https://www.python.org/psf-landing/ and install it
Clone this repository (using git terminal):

git clone https://github.com/Milwa97/A-level-math.git

or download zip file from: https://github.com/Milwa97/A-level-math.git and unpack

Open Windows terminal in the repository folded and create a virtual environment via typing:

python3 -m venv path\to\venv

Activate the virtual environment:

path\to\venv\Scripts\activate

Install python packages from requirements.txt:

pip install -r requirements.txt

Run jupyter notebook:

jupyter notebook

Instruction for Linux:

If You are a linux user, most probably Ypu can copy repository and create virtual environment without any instructions :P

Clone this repository (using git terminal):

git clone https://github.com/Milwa97/A-level-math.git

or download zip file from: https://github.com/Milwa97/A-level-math.git and unpack

Open terminal in the repository folded and check, if You have pip and virtualenv installed:

$ pip --version
$ virtualenv --version

if not, install pip and virtualenv:

$ sudo apt-get install python-pip
$ pip install virtualenv

Create a virtual environment via typing:

$ virtualenv virtualenv_name

Activate the virtual environment:

$ source virtualenv_name/bin/activate

Install python packages from requirements.txt:

pip install -r requirements.txt

Run jupyter notebook:

jupyter notebook

If You want to enable interactive graphics (better option, but may require installing manually some dependencies according to this instruction: Render interactive plots with matplotlib, use:

%matplotlib widget

For static graphics use (safe, always working option):

%matplotlib inline

Comment or delete option that You don't use.

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# Pandas Homework - Pandas, Pandas, Pandas ## Background The data dive continues! Now, it's time to take what you've learned about Python Pandas and apply it to new situations. For this assignment, you'll need to complete **one of two** (not both) Data Challenges. Once again, which challenge you take on is your choice. Just be sure to give it your all -- as the skills you hone will become powerful tools in your data analytics tool belt. ### Before You Begin 1. Create a new repository for this project called `pandas-challenge`. **Do not add this homework to an existing repository**. 2. Clone the new repository to your computer. 3. Inside your local git repository, create a directory for the Pandas Challenge you choose. Use folder names corresponding to the challenges: **HeroesOfPymoli** or **PyCitySchools**. 4. Add your Jupyter notebook to this folder. This will be the main script to run for analysis. 5. Push the above changes to GitHub or GitLab. ## Option 1: Heroes of Pymoli ![Fantasy](Images/Fantasy.png) Congratulations! After a lot of hard work in the data munging mines, you've landed a job as Lead Analyst for an independent gaming company. You've been assigned the task of analyzing the data for their most recent fantasy game Heroes of Pymoli. Like many others in its genre, the game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. As a first task, the company would like you to generate a report that breaks down the game's purchasing data into meaningful insights. 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Having spent years analyzing financial records for big banks, you've finally scratched your idealistic itch and joined the education sector. In your latest role, you've become the Chief Data Scientist for your city's school district. In this capacity, you'll be helping the school board and mayor make strategic decisions regarding future school budgets and priorities. As a first task, you've been asked to analyze the district-wide standardized test results. You'll be given access to every student's math and reading scores, as well as various information on the schools they attend. Your responsibility is to aggregate the data to and showcase obvious trends in school performance. 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Use 4 reasonable bins to group school spending. Include in the table each of the following: * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Scores by School Size * Repeat the above breakdown, but this time group schools based on a reasonable approximation of school size (Small, Medium, Large). ### Scores by School Type * Repeat the above breakdown, but this time group schools based on school type (Charter vs. District). As final considerations: * Use the pandas library and Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of at least two observable trends based on the data. * See [Example Solution](PyCitySchools/PyCitySchools_starter.ipynb) for a reference on the expected format. ## Hints and Considerations * These are challenging activities for a number of reasons. For one, these activities will require you to analyze thousands of records. Hacking through the data to look for obvious trends in Excel is just not a feasible option. The size of the data may seem daunting, but pandas will allow you to efficiently parse through it. * Second, these activities will also challenge you by requiring you to learn on your feet. Don't fool yourself into thinking: "I need to study pandas more closely before diving in." Get the basic gist of the library and then _immediately_ get to work. When facing a daunting task, it's easy to think: "I'm just not ready to tackle it yet." But that's the surest way to never succeed. Learning to program requires one to constantly tinker, experiment, and learn on the fly. You are doing exactly the _right_ thing, if you find yourself constantly practicing Google-Fu and diving into documentation. There is just no way (or reason) to try and memorize it all. Online references are available for you to use when you need them. So use them! * Take each of these tasks one at a time. Begin your work, answering the basic questions: "How do I import the data?" "How do I convert the data into a DataFrame?" "How do I build the first table?" Don't get intimidated by the number of asks. Many of them are repetitive in nature with just a few tweaks. Be persistent and creative! * Expect these exercises to take time! Don't get discouraged if you find yourself spending hours initially with little progress. Force yourself to deal with the discomfort of not knowing and forge ahead. Consider these hours an investment in your future! * As always, feel encouraged to work in groups and get help from your TAs and Instructor. Just remember, true success comes from mastery and _not_ a completed homework assignment. So challenge yourself to truly succeed! ### Copyright Trilogy Education Services © 2019. All Rights Reserved.

A-level-math