import matplotlib.pyplot as plt
import numpy as np
# Data
libraries = [
"NumPy", "Pandas", "Matplotlib", "Seaborn", "SciPy", "Scikit-learn", "TensorFlow", "Keras",
"PyTorch", "Statsmodels", "XGBoost", "LightGBM", "CatBoost", "NLTK", "SpaCy", "Gensim",
"Plotly", "Bokeh", "Dash", "H2O.ai", "PyCaret", "Dask", "Orange3"
]
# Parameters for circular layout
num_libs = len(libraries)
angles = np.linspace(0, 2 * np.pi, num_libs, endpoint=False).tolist()
# Plot
fig, ax = plt.subplots(figsize=(12, 12), subplot_kw={'projection': 'polar'})
bars = ax.bar(angles, np.ones(num_libs), width=0.3, bottom=2.5, color='skyblue', edgecolor='black')
# Add library names and URLs
for bar, angle, lib in zip(bars, angles, libraries):
rotation = np.rad2deg(angle)
alignment = 'left' if angle < np.pi else 'right'
ax.text(angle, bar.get_height() + 3.0, lib, rotation=rotation, ha=alignment, va='center', fontsize=12, color='black')
# Customize plot
ax.set_yticklabels([])
ax.set_xticks([])
ax.spines['polar'].set_visible(False)
plt.show()
# Output
Welcome to the Data Science Repository! This repository is designed to help you learn Python for data science and develop the essential skills needed to succeed as a data scientist. From data manipulation to machine learning, you'll gain the knowledge required to excel in this field.
This track is a comprehensive journey through Python for data science. It consists of various libraries and tools to import, clean, manipulate, visualize data, and build predictive models. Here's an overview of the contents in this repository:
| # | Project | Link |
|---|---|---|
| 1 | Introduction to Python | Open |
| 2 | Intermediate Python | Open |
| # | Project | Link |
|---|---|---|
| 1 | Data Manipulation with pandas | Open |
| 2 | Joining Data with pandas | Open |
| 3 | Introduction to Statistics in Python | Open |
| 4 | Introduction to Data Visualization with Matplotlib | Open |
| 5 | Introduction to Data Visualization with Seaborn | Open |
| 6 | Python-data-science-toolbox-(part-1) | Open |
| 7 | Python-data-science-toolbox-(part-2) | Open |
| 8 | Intermediate Data Visualization with Seaborn | Open |
| # | Project | Link |
|---|---|---|
| 1 | Exploratory Data Analysis in Python Part - 1 | Open |
| 2 | Exploratory Data Analysis in Python Part - 2 | Open |
| 3 | Working with Categorical Data in Python | Open |
| 4 | Data Communication Concepts | Open |
| # | Project | Link |
|---|---|---|
| 1 | Introduction to Importing Data in Python-(part-1) | Open |
| 2 | Intermediate Importing Data in Python-(part-2) | Open |
| 3 | Cleaning Data in Python [Part - 1] | Open |
| 4 | Cleaning Data in Python [Part - 2] | Open |
| 5 | Working with Dates and Times in Python | Open |
| # | Project | Link |
|---|---|---|
| 1 | Writing Functions in Python | Open |
| 2 | Introduction to Regression with statsmodels in Python | Open |
| 3 | Sampling in Python | Open |
| 4 | Hypothesis Testing in Python | Open |
| 5 | Statistical-Thinking-in-Python-[Part -1] | Open |
| 6 | Statistical-Thinking-in-Python-[Part -2] | Open |
| 7 | Supervised Learning with scikit-learn | Open |
| 8 | Unsupervised Learning in Python | Open |
| 9 | Cluster Analysis in Python | Open |
| 10 | Machine Learning with Tree-Based Models in Python | Open |
| 11 | Preprocessing for Machine Learning | Open |
| 12 | Developing Python Packages | Open |
| 13 | Machine Learning for Business | Open |
| 14 | Introduction to SQL | Open |
| 15 | Intermediate SQL | Open |
| 16 | Joining Data in SQL | Open |
| 17 | Introduction to Git | Open |
In addition to the comprehensive learning materials, this repository offers various projects to apply and reinforce your data science skills. Here is a list of the projects available:
| # | Project | Link |
|---|---|---|
| 1 | Analyzing TV Data |  |
| 2 | Investigating Netflix Movies | |
| 3 | What and Where are the World's Oldest Businesses | |
| 4 | Google Play Store Apps and Reviews | |
| 5 | The GitHub History of the Scala Language | |
| 6 | A Visual History of Nobel Prize Winners | |
| 7 | Dr. Semmelweis and the Discovery of Handwashing | |
| 8 | Predicting Credit Card Approvals | |
| 9 | School Budgeting with Machine Learning in Python | |
| 10 | Analyzing Police Activity with pandas | |
| 11 | Exploring NYC Public School Test Result Scores | |
| 12 | Analyzing Crime in Los Angeles | |
| 13 | Preparing Data for Customer Analytics Modeling | |
| 14 | Modeling Car Insurance Claim Outcomes(EDA) | |
| Modeling Car Insurance Claim Outcomes(ML) | |
|
| Modeling Car Insurance Claim Outcomes(GB-RF) | |
|
| Modeling Car Insurance Claim Outcomes(Ensemble_Method) | |
|
| 15 | Hypothesis Testing Soccer Matches | |
| 16 | Predictive Modeling for Agriculture | |
| 17 | Clustering Antarctic Penguin Species | |
| 18 | Predicting Movie Rental Durations | |
| 19 | 🔜 | |
- ➤ Python
- ➤ Machine Learning
- ➤ Machines leaning and Data science in one Notebook
- ➤ Feature Engineering & Feature selection
- ➤ Statistics for Data Science
- ➤ Data Preprocessing
- ➤ NumPy
- ➤ Matplotlib
- ➤ Pandas
- ➤ Seaborn
➤ ⭐1. Data Scientist Professional with Python
➤ ⭐2. Associate Data Scientist
This project is licensed under the MIT License. See LICENSE for details.
If you find this repository helpful, show your support by starring it! For questions or feedback, reach out on Twitter(X).
🔃 ➤ If you have questions or feedback, feel free to reach out!!!
