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This Repo contains tools that allow us to import, clean, manipulate, and visualize data —Includes Python libraries, like pandas, NumPy, Matplotlib, and many more to work with real-world datasets to learn the statistical and machine learning techniques.

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mohd-faizy/CAREER-TRACK-Data-Scientist-with-Python

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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

Data Science Repository

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.

Track Overview

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:

➤ ⭐Python Essentials

# Project Link
1 Introduction to Python Open
2 Intermediate Python Open

➤ ⭐Data Manipulation and Visualization

# 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

➤ ⭐ Exploratory Data Analysis (EDA) and Statistics

# 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

➤ ⭐ Data Importing and Cleaning

# 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

➤ ⭐ Advanced Topics

# 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

➤ ⭐ Projects

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 Open in Colab
2 Investigating Netflix Movies Open in Colab
3 What and Where are the World's Oldest Businesses Open in Colab
4 Google Play Store Apps and Reviews Open in Colab
5 The GitHub History of the Scala Language Open in Colab
6 A Visual History of Nobel Prize Winners Open in Colab
7 Dr. Semmelweis and the Discovery of Handwashing Open in Colab
8 Predicting Credit Card Approvals Open in Colab
9 School Budgeting with Machine Learning in Python Open in Colab
10 Analyzing Police Activity with pandas Open in Colab
11 Exploring NYC Public School Test Result Scores Open in Colab
12 Analyzing Crime in Los Angeles Open in Colab
13 Preparing Data for Customer Analytics Modeling Open in Colab
14 Modeling Car Insurance Claim Outcomes(EDA) Open in Colab
Modeling Car Insurance Claim Outcomes(ML) Open in Colab
Modeling Car Insurance Claim Outcomes(GB-RF) Open in Colab
Modeling Car Insurance Claim Outcomes(Ensemble_Method) Open in Colab
15 Hypothesis Testing Soccer Matches Open in Colab
16 Predictive Modeling for Agriculture Open in Colab
17 Clustering Antarctic Penguin Species Open in Colab
18 Predicting Movie Rental Durations Open in Colab
19 🔜 Open in Colab

🗂️ ➤ Additional Resources

RoadMap OLD

map_img

📊📈📉 STATISTICS

map_img


📄 ➤ STATEMENT OF ACCOMPLISHMENT

➤ ⭐1. Data Scientist Professional with Python

➤ ⭐2. Associate Data Scientist

⚖ ➤ License

This project is licensed under the MIT License. See LICENSE for details.

❤️ ➤ Support

If you find this repository helpful, show your support by starring it! For questions or feedback, reach out on Twitter(X).

$\color{skyblue}{\textbf{Connect with me:}}$

🔃 ➤ If you have questions or feedback, feel free to reach out!!!


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This Repo contains tools that allow us to import, clean, manipulate, and visualize data —Includes Python libraries, like pandas, NumPy, Matplotlib, and many more to work with real-world datasets to learn the statistical and machine learning techniques.

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