{"id":28334387,"url":"https://github.com/joshuathadi/data-science","last_synced_at":"2026-04-17T13:05:37.430Z","repository":{"id":262271557,"uuid":"886729778","full_name":"JoshuaThadi/Data-Science","owner":"JoshuaThadi","description":"Assignments and notes from the IBM Data Science Professional Certificate. 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It lies at the intersection of mathematics, computer science, and domain expertise.\n\n\u003c/h4\u003e\n\n\u003ca href=\"https://github.com/JoshuaThadi/Data-Science/blob/main/DS-roadmap.md\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Data%20Science%20Roadmap-View-grey?style=for-the-badge\u0026logo=github\u0026logoColor=white\" alt=\"Data Science Roadmap\"\u003e\u003c/a\u003e\n\u003ca href=\"https://youtu.be/LHBE6Q9XlzI?list=PLAoJfvFSn6qi_8eTKMXdKGMQGQfYOV54n\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/YouTube-Data_science-BF616A?style=for-the-badge\u0026logo=youtube\u0026logoColor=white\" alt=\"YouTube Thumbnail\"\u003e\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n\n\u003e[!IMPORTANT]\n\u003e## \u003cimg width=\"50px\" src=\"https://github.com/JoshuaThadi/Data-Science/blob/main/assests/IBM_logo.svg.png\"\u003e Data Science Assignment\n\u003eWelcome to the Data Science assignment repository! This assignment, developed as part of a Coursera course, covers key data science concepts and practical coding exercises in Jupyter Notebook. Below is a summary of what you will find in this repository.\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://www.coursera.org/professional-certificates/ibm-data-science\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Coursera-Join%20Coursera%20Course-blue?style=for-the-badge\u0026logo=coursera\u0026logoColor=white\" alt=\"Coursera\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://github.com/JoshuaThadi/Data-Science/tree/main/IBM-Data-Science\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/IBM-Data%20Science%20Assignments-red?style=for-the-badge\u0026logo=coursera\u0026logoColor=white\" alt=\"IBM_badge\"\u003e\n\u003c/a\u003e\n\n\n### Objectives\n\u003cp\u003e - Understand the role of a Data Scientist and the data science lifecycle\u003cbr\u003e - Learn Python, SQL, and data science tools such as Jupyter Notebooks, Git, and Watson Studio\u003cbr\u003e - Perform data collection, cleaning, and preparation for analysis\u003cbr\u003e - Conduct Exploratory Data Analysis to uncover trends and insights\u003cbr\u003e - Visualize data using Matplotlib, Seaborn, and interactive dashboards\u003cbr\u003e - Apply basic machine learning techniques for prediction and classification\u003cbr\u003e - Evaluate model performance and interpret results\u003cbr\u003e - Complete hands-on projects and a capstone to build a job-ready portfolio\u003cbr\u003e \u003c/p\u003e\n\n---\n\n## Data Science and Data Analysis Projects\n\n\u003e [!IMPORTANT]\n\u003e\u003ch3\u003e\u003ca href=\"https://www.ibm.com/think/topics/exploratory-data-analysis\"\u003e1] EDA - Exploratory Data Analysis\u003c/a\u003e\u003c/h3\u003e\n\u003e\u003cb\u003eExploratory Data Analysis (EDA)\u003c/b\u003e is a crucial step in the data science lifecycle where raw data is explored, summarized, and visualized \n\u003eto understand its structure and characteristics before applying any machine learning or statistical models.\n\u003cdetails\u003e\n    \u003csummary\u003e\n        \u003cb\u003eExploratory Data Analysis on Olympics\u003c/b\u003e\n    \u003c/summary\u003e\n\nThis project involves performing EDA on a dataset containing information about Olympic athletes, events, and medal counts. \nThe goal is to uncover insights about athlete performance, country participation, and trends over time.\n\n* [Kaggle - Olympic_dataset](https://www.kaggle.com/datasets/bhanupratapbiswas/olympic-data)\n* [Python source code for EDA-olympic program](https://github.com/JoshuaThadi/Data-Science/tree/main/EDA)\n\n\n## Project Overview\n\nThis project focuses on **Exploratory Data Analysis (EDA)** of the **Olympics dataset** to uncover meaningful patterns, trends, and insights \nfrom historical Olympic data. By applying data analysis and visualization techniques, this project aims to better understand athlete performance, \ncountry-wise dominance, medal distributions, and the evolution of the Olympic Games over time.\n\nThe analysis is performed using Python-based data science tools and follows a structured, professional EDA workflow.\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n\u003e## About the Olympics Dataset\n\u003e\n\u003eThe Olympics dataset contains historical records of Olympic Games, including:\n\u003e\n\u003e* Athlete details (name, gender, age)\n\u003e* Country / National Olympic Committee (NOC)\n\u003e* Sport and event categories\n\u003e* Medal counts (Gold, Silver, Bronze)\n\u003e* Year, season, and host city\n\u003e\n\u003eThis dataset provides rich opportunities to analyze sports trends across decades.\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n## Key Objectives of This Project\n\n* Analyze medal distribution across countries\n* Identify top-performing nations and athletes\n* Study gender participation trends over time\n* Compare performance across different sports\n* Explore the evolution of the Olympics across years\n* Detect missing values, duplicates, and inconsistencies\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n## Tools \u0026 Technologies Used\n\n* **Python** - High level programming language\n* **Pandas** – data manipulation and cleaning\n* **NumPy** – numerical operations\n* **Matplotlib** – data visualization\n* **Seaborn** – advanced statistical plots\n* **Jupyter Notebook** – interactive analysis\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n## EDA Workflow Followed\n\n1. **Data Loading \u0026 Inspection**\n   * Understanding shape, columns, and data types\n\n2. **Data Cleaning**\n   * Handling missing values\n   * Removing duplicates\n   * Fixing inconsistencies\n\n3. **Univariate Analysis**\n   * Distribution of medals, athletes, and events\n\n4. **Bivariate \u0026 Multivariate Analysis**\n   * Country vs medals\n   * Gender vs participation\n   * Sports vs medal counts\n\n5. **Data Visualization**\n   * Bar charts, histograms, heatmaps, line plots\n\n6. **Insights \u0026 Conclusions**\n   * Key findings and observations\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n## Key Insights (Sample)\n\n* Certain countries consistently dominate specific sports\n* Male participation was higher historically, with a steady rise in female participation\n* Medal distribution is highly skewed toward a few top-performing nations\n* Some sports contribute disproportionately to total medal counts\n\n\u003e Detailed insights are available inside the notebook.\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n## Future Improvements\n\n* Apply **statistical analysis** for deeper insights\n* Perform **time-series analysis** on medal trends\n* Build **machine learning models** for medal prediction\n* Create **interactive dashboards** using Plotly or Power BI\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n## Project Structure\n\n```\n├── EDA/\n│   └── EDA-olympics/\n│       ├── EDA-olympic.ipynb\n│       └── dataset_olympics.csv\n```\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n## Author\n\n**Joshua Thadi**\nAI/ML \u0026 Data Science Enthusiast\nFounder \u0026 CEO – Yehoarc\n\n\u003ch1\u003e\n    \u003cp\u003e\u003c/p\u003e\n\u003c/h1\u003e\n\n## Conclusion\n\nThis project demonstrates how **Exploratory Data Analysis** transforms raw Olympic data into meaningful insights. \nEDA is not just a step—it is a mindset that enables analysts and data scientists to ask the right questions and build reliable, high-impact solutions.\n\nIf you find this project useful, feel free to star the repository and explore further!\n\n\u003c/details\u003e\n\n\n\n---\n\n\u003e [!NOTE]\n\u003e### Data Science resources and information\n\u003e * Topic and subjects to learn about data science and data analysis\n\u003cdetails\u003e\n    \u003csummary\u003eData science - details\u003c/summary\u003e\n    \u003ch3\u003e☆ Key Components of Data Science\u003c/h3\u003e\n\n1] \u003cb\u003eData Collection:\u003c/b\u003e Gathering data from various sources: databases, APIs, sensors, web scraping, etc.\u003cbr\u003e\n2] \u003cb\u003eData Cleaning and Preprocessing:\u003c/b\u003e Handling missing data, removing duplicates, fixing errors, normalizing formats.\u003cbr\u003e\n3] \u003cb\u003eExploratory Data Analysis (EDA:\u003c/b\u003e Using statistics and visualization to understand patterns, trends, and anomalies.\u003cbr\u003e\n4] \u003cb\u003eFeature Engineering:\u003c/b\u003e Creating meaningful variables from raw data to improve model performance.\u003cbr\u003e\n5] \u003cb\u003eModel Building:\u003c/b\u003e Applying machine learning algorithms (e.g., regression, classification, clustering.\u003cbr\u003e\n6] \u003cb\u003eModel Evaluation:\u003c/b\u003e Testing model accuracy using metrics like precision, recall, F1-score, RMSE, etc.\u003cbr\u003e\n7] \u003cb\u003eDeployment:\u003c/b\u003e Integrating the model into a real-world application using tools like Flask, Docker, or cloud services\u003cbr\u003e\n8] \u003cb\u003eMonitoring and Maintenance:\u003c/b\u003e Tracking model performance over time and retraining when necessary.\u003cbr\u003e\n\n\u003ca href=\"https://www.ibm.com/think/topics/exploratory-data-analysis\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Exploratory_Data_Analysis-Trends_\u0026_Anomalies-EBCB8B?style=for-the-badge\u0026logo=plotly\u0026logoColor=white\" alt=\"Exploratory Data Analysis\"\u003e\u003c/a\u003e \n\u003ca href=\"https://www.ibm.com/think/topics/feature-engineering\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Feature_Engineering-Create_New_Features-8FBCBB?style=for-the-badge\u0026logo=scikitlearn\u0026logoColor=white\" alt=\"Feature Engineering\"\u003e\u003c/a\u003e \n\u003ca href=\"https://en.wikipedia.org/wiki/Data_collection\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Data_Collection-Databases,_APIs,_Scraping-5E81AC?style=for-the-badge\u0026logo=databricks\u0026logoColor=white\" alt=\"Data Collection\"\u003e\u003c/a\u003e \n\u003ca href=\"https://www.linkedin.com/pulse/monitoring-data-science-lifecycle-types-challenges-dmo9c/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Monitoring_\u0026_Maintenance-Track_\u0026_Retrain-BF616A?style=for-the-badge\u0026logo=mlflow\u0026logoColor=white\" alt=\"Monitoring and Maintenance\"\u003e\u003c/a\u003e \n\u003ca href=\"https://www.ibm.com/topics/data-cleaning\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Data_Cleaning_\u0026_Preprocessing-Missing_Data-D08770?style=for-the-badge\u0026logo=pandas\u0026logoColor=white\" alt=\"Data Cleaning\"\u003e\u003c/a\u003e \n\u003ca href=\"https://www.coursera.org/articles/model-evaluation\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Model_Evaluation-Precision,_Recall-4C566A?style=for-the-badge\u0026logo=scikitlearn\u0026logoColor=white\" alt=\"Model Evaluation\"\u003e\u003c/a\u003e \n\u003ca href=\"https://www.secoda.co/glossary/data-deployment\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Deployment-Flask,_Docker,_Cloud-B48EAD?style=for-the-badge\u0026logo=docker\u0026logoColor=white\" alt=\"Deployment\"\u003e\u003c/a\u003e \n\u003ca href=\"https://www.geeksforgeeks.org/model-building-for-data-analytics/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Model_Building-ML_Algorithms-A3BE8C?style=for-the-badge\u0026logo=scikitlearn\u0026logoColor=white\" alt=\"Model Building\"\u003e\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n\u003ch3\u003e✪ Core Python Libraries / Modules\u003c/h3\u003e\n\n\u003cb\u003e Data Manipulation \u0026 Analysis\u003c/b\u003e – NumPy, Pandas, Dask \u003cbr\u003e\n\u003cb\u003e Data Visualization\u003c/b\u003e – Matplotlib, Seaborn, Plotly, Altair \u003cbr\u003e\n\u003cb\u003e Machine Learning\u003c/b\u003e – scikit-learn, XGBoost, LightGBM, CatBoost, Hugging Face Transformers, TensorFlow, PyTorch \u003cbr\u003e\n\u003cb\u003e Deep Learning\u003c/b\u003e – Keras, PyTorch Lightning, ONNX \u003cbr\u003e\n\u003cb\u003e Model Deployment\u003c/b\u003e – Flask, FastAPI, Streamlit, Gradio, Docker \u003cbr\u003e\n\n\u003ca href=\"https://pandas.pydata.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Pandas-150458?style=for-the-badge\u0026logo=pandas\u0026logoColor=white\" alt=\"Pandas\"\u003e\u003c/a\u003e\n\u003ca href=\"https://numpy.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/NumPy-013243?style=for-the-badge\u0026logo=numpy\u0026logoColor=white\" alt=\"NumPy\"\u003e\u003c/a\u003e\n\u003ca href=\"https://matplotlib.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Matplotlib-11557C?style=for-the-badge\u0026logo=plotly\u0026logoColor=white\" alt=\"Matplotlib\"\u003e\u003c/a\u003e\n\n\u003ca href=\"https://www.python.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Python-3776AB?style=for-the-badge\u0026logo=python\u0026logoColor=white\" alt=\"Python\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.r-project.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/R-276DC3?style=for-the-badge\u0026logo=r\u0026logoColor=white\" alt=\"R\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.w3schools.com/sql/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/SQL-4479A1?style=for-the-badge\u0026logo=mysql\u0026logoColor=white\" alt=\"SQL\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://azure.microsoft.com/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Azure-0078D4?style=for-the-badge\u0026logo=microsoftazure\u0026logoColor=white\" alt=\"Azure\"\u003e\u003c/a\u003e\n\n  \u003ca href=\"https://www.tableau.com/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Tableau-E97627?style=for-the-badge\u0026logo=tableau\u0026logoColor=white\" alt=\"Tableau\"\u003e\u003c/a\u003e\n\u003ca href=\"https://powerbi.microsoft.com/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Power%20BI-F2C811?style=for-the-badge\u0026logo=powerbi\u0026logoColor=black\" alt=\"Power BI\"\u003e\u003c/a\u003e\n\u003ca href=\"https://seaborn.pydata.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Seaborn-4B8BBE?style=for-the-badge\u0026logo=python\u0026logoColor=white\" alt=\"Seaborn\"\u003e\u003c/a\u003e\n\n\u003ca href=\"https://scikit-learn.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Scikit--learn-F7931E?style=for-the-badge\u0026logo=scikitlearn\u0026logoColor=white\" alt=\"Scikit-learn\"\u003e\u003c/a\u003e\n\u003ca href=\"https://www.tensorflow.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge\u0026logo=tensorflow\u0026logoColor=white\" alt=\"TensorFlow\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pytorch.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge\u0026logo=pytorch\u0026logoColor=white\" alt=\"PyTorch\"\u003e\u003c/a\u003e\n\n\u003ca href=\"https://jupyter.org/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Jupyter-F37626?style=for-the-badge\u0026logo=jupyter\u0026logoColor=white\" alt=\"Jupyter Notebooks\"\u003e\u003c/a\u003e\n\u003ca href=\"https://colab.research.google.com/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Google_Colab-F9AB00?style=for-the-badge\u0026logo=googlecolab\u0026logoColor=white\" alt=\"Google Colab\"\u003e\u003c/a\u003e\n\u003ca href=\"https://aws.amazon.com/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/AWS-232F3E?style=for-the-badge\u0026logo=amazonaws\u0026logoColor=white\" alt=\"AWS\"\u003e\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n\u003ch3\u003e📚 Core Subjects in Data Science\u003c/h3\u003e\n\n1] \u003cb\u003eStatistics \u0026 Probability\u003c/b\u003e – Foundational math for inference and predictions \u003cbr\u003e\n2] \u003cb\u003eLinear Algebra\u003c/b\u003e – Vectors, matrices — core of ML models \u003cbr\u003e\n3] \u003cb\u003eCalculus\u003c/b\u003e – Gradient descent, optimization \u003cbr\u003e\n4] \u003cb\u003eMachine Learning\u003c/b\u003e – Algorithms to learn from data \u003cbr\u003e\n5] \u003cb\u003eDeep Learning\u003c/b\u003e – Neural networks and deep architectures \u003cbr\u003e\n6] \u003cb\u003eNLP (Natural Language Processing)\u003c/b\u003e – Working with text and language \u003cbr\u003e\n7] \u003cb\u003eComputer Vision\u003c/b\u003e – Image and video analysis \u003cbr\u003e\n8] \u003cb\u003eBig Data\u003c/b\u003e – Working with large-scale data \u003cbr\u003e\n9] \u003cb\u003eData Engineering\u003c/b\u003e – Pipelines, ETL, data storage \u003cbr\u003e\n10] \u003cb\u003eModel Deployment\u003c/b\u003e – Turning models into APIs/apps \u003cbr\u003e\n11] \u003cb\u003eMLOps\u003c/b\u003e – Production lifecycle of ML models \u003cbr\u003e\n12] \u003cb\u003eData Visualization\u003c/b\u003e – Communicating insights effectively \u003cbr\u003e\n13] \u003cb\u003eCloud \u0026 DevOps\u003c/b\u003e – Using AWS, Azure, GCP for scalable data solutions \u003cbr\u003e\n\n\u003ca href=\"https://www.ibm.com/think/topics/data-visualization\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Data_Visualization-Insight_Communication-A3BE8C?style=for-the-badge\u0026logo=tableau\u0026logoColor=white\" alt=\"Data Visualization\"\u003e\u003c/a\u003e \n    \n\u003ca href=\"https://www.geeksforgeeks.org/probability-and-statistics/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Statistics_\u0026_Probability-Foundations-5E81AC?style=for-the-badge\u0026logo=R\u0026logoColor=white\" alt=\"Statistics \u0026 Probability\"\u003e\u003c/a\u003e \n    \n\u003ca href=\"https://www.geeksforgeeks.org/linear-algebra/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Linear_Algebra-Vectors_\u0026_Matrices-BF616A?style=for-the-badge\u0026logo=Numpy\u0026logoColor=white\" alt=\"Linear Algebra\"\u003e\u003c/a\u003e\n    \n\u003ca href=\"https://en.wikipedia.org/wiki/Calculus\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Calculus-Optimization_\u0026_Gradients-4C566A?style=for-the-badge\u0026logo=marketo\u0026logoColor=white\" alt=\"Calculus\"\u003e\u003c/a\u003e\n    \n\u003ca href=\"https://www.ibm.com/think/topics/big-data\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Big_Data-Large_Scale_Processing-B48EAD?style=for-the-badge\u0026logo=apachespark\u0026logoColor=white\" alt=\"Big Data\"\u003e\u003c/a\u003e \n    \n\u003ca href=\"https://www.ibm.com/devops\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Cloud_\u0026_DevOps-Scalable_Solutions-EBCB8B?style=for-the-badge\u0026logo=azuredevops\u0026logoColor=white\" alt=\"Cloud \u0026 DevOps\"\u003e\u003c/a\u003e\n\n\u003ca href=\"https://www.ibm.com/think/topics/deep-learning\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Deep_Learning-Neural_Networks-D08770?style=for-the-badge\u0026logo=tensorflow\u0026logoColor=white\" alt=\"Deep Learning\"\u003e\u003c/a\u003e \n\n\u003ca href=\"https://www.ibm.com/think/topics/computer-vision\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Computer_Vision-Image_\u0026_Video-5E81AC?style=for-the-badge\u0026logo=opencv\u0026logoColor=white\" alt=\"Computer Vision\"\u003e\u003c/a\u003e \n\n\u003ca href=\"https://www.ibm.com/think/topics/data-engineering\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Data_Engineering-ETL_\u0026_Pipelines-EBCB8B?style=for-the-badge\u0026logo=airflow\u0026logoColor=white\" alt=\"Data Engineering\"\u003e\u003c/a\u003e \n\n\u003ca href=\"https://www.ibm.com/think/topics/machine-learning\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Machine_Learning-Algorithms-8FBC8F?style=for-the-badge\u0026logo=scikitlearn\u0026logoColor=white\" alt=\"Machine Learning\"\u003e\u003c/a\u003e \n\n\u003ca href=\"https://www.ibm.com/think/topics/model-deployment\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Model_Deployment-APIs_\u0026_Apps-EAA06A?style=for-the-badge\u0026logo=docker\u0026logoColor=white\" alt=\"Model Deployment\"\u003e\u003c/a\u003e \n\n\u003ca href=\"https://www.ibm.com/think/topics/mlops\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/MLOps-Production_Lifecycle-81A1C1?style=for-the-badge\u0026logo=mlflow\u0026logoColor=white\" alt=\"MLOps\"\u003e\u003c/a\u003e \n\n\u003ca href=\"https://www.ibm.com/think/topics/natural-language-processing\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/NLP-Text_\u0026_Language-A3BE8C?style=for-the-badge\u0026logo=spacy\u0026logoColor=white\" alt=\"Natural Language Processing\"\u003e\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n\u003ch3\u003e📌 Topics to Cover\u003c/h3\u003e\n\n1] \u003cb\u003eExploratory Data Analysis (EDA)\u003c/b\u003e – Missing data, outliers, visualization \u003cbr\u003e\n2] \u003cb\u003eFeature Engineering\u003c/b\u003e – Encoding, scaling, transformations \u003cbr\u003e\n3] \u003cb\u003eModel Evaluation\u003c/b\u003e – Accuracy, precision, recall, ROC, AUC \u003cbr\u003e\n4] \u003cb\u003eHyperparameter Tuning\u003c/b\u003e – GridSearch, RandomSearch, Optuna \u003cbr\u003e\n5] \u003cb\u003eDimensionality Reduction\u003c/b\u003e – PCA, t-SNE, UMAP \u003cbr\u003e\n6] \u003cb\u003eTime Series Analysis\u003c/b\u003e – ARIMA, LSTM, Prophet \u003cbr\u003e\n7] \u003cb\u003eUnsupervised Learning\u003c/b\u003e – Clustering (KMeans, DBSCAN), PCA \u003cbr\u003e\n8] \u003cb\u003eSupervised Learning\u003c/b\u003e – Regression, classification \u003cbr\u003e\n9] \u003cb\u003eNeural Networks\u003c/b\u003e – CNN, RNN, GAN, transformers \u003cbr\u003e\n10] \u003cb\u003eRecommendation Systems\u003c/b\u003e – Collaborative filtering, content-based \u003cbr\u003e\n11] \u003cb\u003eData Cleaning \u0026 Wrangling\u003c/b\u003e – Imputation, normalization, data types \u003cbr\u003e\n\n\u003cdiv\u003e\n  \u003ca href=\"https://www.ibm.com/think/topics/supervised-learning\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Supervised_Learning-Regression,_Classification-EBCB8B?style=for-the-badge\u0026logo=scikitlearn\u0026logoColor=white\" alt=\"Supervised Learning\"\u003e\u003c/a\u003e\n\n  \u003ca href=\"https://www.ibm.com/think/topics/neural-networks\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Neural_Networks-CNN,_RNN,_GAN,_Transformers-88C0D0?style=for-the-badge\u0026logo=pytorch\u0026logoColor=white\" alt=\"Neural Networks\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://www.ibm.com/think/topics/recommendation-engine\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Recommendation_Systems-Collaborative_Content_Based-E78284?style=for-the-badge\u0026logo=algolia\u0026logoColor=white\" alt=\"Recommendation Systems\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://www.cdata.com/blog/data-wrangling-vs-data-cleaning\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Data_Cleaning_\u0026_Wrangling-Imputation,_Normalization-7C8B96?style=for-the-badge\u0026logo=dataiku\u0026logoColor=white\" alt=\"Data Cleaning\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://www.ibm.com/think/topics/feature-engineering\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Feature_Engineering-Encoding,_Scaling,_Transforms-BF616A?style=for-the-badge\u0026logo=scikitlearn\u0026logoColor=white\" alt=\"Feature Engineering\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://www.ibm.com/think/topics/hyperparameter-tuning\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Hyperparameter_Tuning-GridSearch,_Optuna-A3BE8C?style=for-the-badge\u0026logo=optuna\u0026logoColor=white\" alt=\"Hyperparameter Tuning\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://www.ibm.com/think/topics/dimensionality-reduction\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Dimensionality_Reduction-PCA,_tSNE,_UMAP-D08770?style=for-the-badge\u0026logo=pandas\u0026logoColor=white\" alt=\"Dimensionality Reduction\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://www.ibm.com/think/topics/unsupervised-learning\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Unsupervised_Learning-Clustering,_PCA-B48EAD?style=for-the-badge\u0026logo=databricks\u0026logoColor=white\" alt=\"Unsupervised Learning\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://domino.ai/data-science-dictionary/model-evaluation\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Model_Evaluation-Accuracy,_ROC,_AUC-D8DEE9?style=for-the-badge\u0026logo=metrics\u0026logoColor=black\" alt=\"Model Evaluation\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://www.ibm.com/think/topics/exploratory-data-analysis\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/EDA-Missing_Data,_Outliers,_Viz-5E81AC?style=for-the-badge\u0026logo=chartdotjs\u0026logoColor=white\" alt=\"EDA\"\u003e\u003c/a\u003e\n  \n  \u003ca href=\"https://www.tableau.com/analytics/what-is-time-series-analysis\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Time_Series_ARIMA,_LSTM,_Prophet-81A1C1?style=for-the-badge\u0026logo=clockify\u0026logoColor=white\" alt=\"Time Series Analysis\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n\u003ch3\u003e★ Why is Data Science Important?\u003c/h3\u003e\n\u003cp\u003e \nData Science enables organizations to:\u003cbr\u003e\n1] Make data-driven decisions\u003cbr\u003e\n2] Predict future trends\u003cbr\u003e\n3] Automate processes using machine learning\u003cbr\u003e\n4] Improve customer experiences and optimize operations\u003cbr\u003e\n\u003c/p\u003e\n\n\n\u003ch3\u003e🌐 Datasets \u0026 Practice\u003c/h3\u003e\n1] \u003cb\u003eKaggle Datasets\u003c/b\u003e \u003cbr\u003e\n2] \u003cb\u003eUCI Machine Learning Repository\u003c/b\u003e \u003cbr\u003e\n3] \u003cb\u003eGoogle Dataset Search\u003c/b\u003e \u003cbr\u003e\n4] \u003cb\u003eData.gov\u003c/b\u003e \u003cbr\u003e\n\n\u003ca href=\"https://www.kaggle.com/datasets\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Kaggle_Datasets-Practice_\u0026_Projects-5E81AC?style=for-the-badge\u0026logo=kaggle\u0026logoColor=white\" alt=\"Kaggle Datasets\"\u003e\u003c/a\u003e \n\u003ca href=\"https://datasetsearch.research.google.com/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Google_Dataset_Search-Searchable_Data-4C566A?style=for-the-badge\u0026logo=google\u0026logoColor=white\" alt=\"Google Dataset Search\"\u003e\u003c/a\u003e \n\u003ca href=\"https://archive.ics.uci.edu/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/UCI_Repository-Classic_Datasets-BF616A?style=for-the-badge\u0026logo=databricks\u0026logoColor=white\" alt=\"UCI Repository\"\u003e\u003c/a\u003e \n\u003ca href=\"https://www.data.gov/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Data.gov-US_Open_Data-A3BE8C?style=for-the-badge\u0026logo=govtech\u0026logoColor=white\" alt=\"Data.gov\"\u003e\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n\u003ch3\u003e📖 Learning Resources\u003c/h3\u003e\n1] \u003cb\u003ePython for Data Science\u003c/b\u003e – freeCodeCamp \u003cbr\u003e\n2] \u003cb\u003eCoursera Data Science Specialization\u003c/b\u003e \u003cbr\u003e\n3] \u003cb\u003eFast.ai Courses\u003c/b\u003e \u003cbr\u003e\n4] \u003cb\u003eHarvard CS109\u003c/b\u003e – Data Science \u003cbr\u003e\n\n\u003ca href=\"https://www.freecodecamp.org/learn/data-analysis-with-python/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Python_for_Data_Science-freeCodeCamp-D08770?style=for-the-badge\u0026logo=python\u0026logoColor=white\" alt=\"Python for Data Science\"\u003e\u003c/a\u003e \n\u003ca href=\"https://www.coursera.org/specializations/jhu-data-science\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Coursera_Data_Science_JHU-Specialization-B48EAD?style=for-the-badge\u0026logo=coursera\u0026logoColor=white\" alt=\"Coursera JHU\"\u003e\u003c/a\u003e \n\u003ca href=\"https://cs109.org/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Harvard_CS109-Data_Science_Course-88C0D0?style=for-the-badge\u0026logo=Harvard\u0026logoColor=white\" alt=\"Harvard CS109\"\u003e\u003c/a\u003e\n\u003ca href=\"https://course.fast.ai/\" target=\"_blank\"\u003e \n    \u003cimg src=\"https://img.shields.io/badge/Fast.ai-Cutting_Edge_Courses-EBCB8B?style=for-the-badge\u0026logo=fastapi\u0026logoColor=white\" alt=\"Fast.ai\"\u003e\u003c/a\u003e \n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n\u003ch3\u003e✫ Applications of Data Science\u003c/h3\u003e\n\n1] \u003cb\u003eDrug Discovery \u0026 Personalized Medicine\u003c/b\u003e\u003cbr\u003e\nUse Case: Analyzing genetic data and molecular structures to discover new drugs faster and more effectively.\u003cbr\u003e\nHow: Machine learning models predict how a drug will interact with human proteins, reducing the need for trial-and-error in labs.\u003cbr\u003e\n\u003ca href=\"https://en.wikipedia.org/wiki/Drug_discovery\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Drug%20Discovery-%26%20Personalized%20Medicine-5E81AC?style=for-the-badge\u0026logo=databricks\u0026logoColor=white\" alt=\"Drug Discovery \u0026 Personalized Medicine\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n2] \u003cb\u003eSatellite Image Analysis \u0026 Earth Observation\u003c/b\u003e\u003cbr\u003e\nUse Case: Monitoring deforestation, urban expansion, and climate change from space.\u003cbr\u003e\nHow: Computer vision applied to satellite imagery to track environmental changes in near real-time.\u003c/br\u003e\n\u003ca href=\"https://en.wikipedia.org/wiki/Satellite_imagery\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Satellite%20Image%20Analysis-%26%20Earth%20Observation-5E81AC?style=for-the-badge\u0026logo=googleearth\u0026logoColor=white\" alt=\"Satellite Image Analysis \u0026 Earth Observation\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n3] \u003cb\u003eNeuroinformatics \u0026 Brain-Computer Interfaces (BCIs)\u003c/b\u003e\u003cbr\u003e\nUse Case: Interpreting brain signals to control external devices or assist people with disabilities.\u003cbr\u003e\nHow: ML models decode EEG/fMRI data to enable mind-controlled prosthetics or communication devices.\u003cbr\u003e\n\n\u003ca href=\"https://en.wikipedia.org/wiki/Neuroinformatics\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Neuroinformatics-%26%20BCIs-B48EAD?style=for-the-badge\u0026logo=neovim\u0026logoColor=white\" alt=\"Neuroinformatics \u0026 Brain-Computer Interfaces (BCIs)\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n4] \u003cb\u003eLegal Analytics \u0026 Predictive Judging\u003c/b\u003e\u003cbr\u003e\nUse Case: Predicting the outcome of legal cases or analyzing judge rulings.\u003cbr\u003e\nHow: NLP and ML models analyze vast amounts of case law and court data to assist legal research and strategy.\u003cbr\u003e\n\n\u003ca href=\"https://en.wikipedia.org/wiki/Legal_analytics\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Legal%20Analytics-%26%20Predictive%20Judging-4C588A?style=for-the-badge\u0026logo=hackthebox\u0026logoColor=white\" alt=\"Legal Analytics \u0026 Predictive Judging\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n5] \u003cb\u003eContent Generation \u0026 Scriptwriting\u003c/b\u003e\u003cbr\u003e\nUse Case: Assisting in writing movie scripts or generating realistic dialogue.\u003cbr\u003e\nHow: NLP and generative models trained on film scripts, books, or dialogues to suggest or generate creative writing.\u003cbr\u003e\n\n\u003ca href=\"https://en.wikipedia.org/wiki/Natural-language_generation\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Content%20Generation-%26%20Scriptwriting-BF616A?style=for-the-badge\u0026logo=writedotas\u0026logoColor=white\" alt=\"Content Generation \u0026 Scriptwriting\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n6] \u003cb\u003eGame Analytics \u0026 Dynamic Difficulty Adjustment\u003c/b\u003e\nUse Case: Making video games adapt to player skill in real time for better engagement.\u003cbr\u003e\nHow: Analyzing gameplay data to adjust difficulty, recommend challenges, or predict player churn.\u003cbr\u003e\n\n\u003ca href=\"https://en.wikipedia.org/wiki/Game_analytics\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Game%20Analytics-%26%20Dynamic%20Difficulty%20Adjustment-4C599A?style=for-the-badge\u0026logo=steam\u0026logoColor=white\" alt=\"Game Analytics \u0026 Dynamic Difficulty Adjustment\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n7] \u003cb\u003eSmart City Optimization\u003c/b\u003e\nUse Case: Managing traffic, energy consumption, and emergency response in real time.\u003cbr\u003e\nHow: Integrating IoT sensor data with predictive analytics to optimize urban infrastructure.\u003cbr\u003e\n\n\u003ca href=\"https://en.wikipedia.org/wiki/Smart_city\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Smart%20City-Optimization-BF616A?style=for-the-badge\u0026logo=home-assistant\u0026logoColor=white\" alt=\"Smart City Optimization\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n8] \u003cb\u003eSynthetic Biology \u0026 Genomic Sequencing\u003c/b\u003e\u003cbr\u003e\nUse Case: Designing synthetic organisms or editing genes more efficiently.\u003cbr\u003e\nHow: Data science models help map and understand genetic patterns to identify gene targets for editing (CRISPR, etc.)\u003cbr\u003e\n\n\u003ca href=\"https://en.wikipedia.org/wiki/Synthetic_biology\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Synthetic%20Biology-%26%20Genomic%20Sequencing-B48EAD?style=for-the-badge\u0026logo=dna\u0026logoColor=white\" alt=\"Synthetic Biology \u0026 Genomic Sequencing\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n9] \u003cb\u003eAdaptive Learning Systems in EdTech\u003c/b\u003e\u003cbr\u003e\nUse Case: Personalizing learning paths for students.\u003cbr\u003e\nHow: Tracking student performance data and recommending content or pace adjustment using ML.\u003cbr\u003e\n\u003ca href=\"https://en.wikipedia.org/wiki/Educational_technology\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Adaptive%20Learning-Systems%20in%20EdTech-4C599A?style=for-the-badge\u0026logo=edmodo\u0026logoColor=white\" alt=\"Adaptive Learning Systems in EdTech\"\u003e\n\u003c/a\u003e\n\n\u003cdiv\u003e\n  \u003cp\u003e\n    \u003ch1\u003e\u003c/h1\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\n10] \u003cb\u003eSocial Good \u0026 Policy Simulation\u003c/b\u003e\u003cbr\u003e\nUse Case: Simulating the outcome of policy changes (e.g., taxation, healthcare).\u003cbr\u003e\nHow: Data models trained on socio-economic datasets to project real-world impact of policies.\u003cbr\u003e\n\n\u003ca href=\"https://en.wikipedia.org/wiki/Policy_analysis\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Social%20Good-%26%20Policy%20Simulation-BF616A?style=for-the-badge\u0026logo=unicef\u0026logoColor=white\" alt=\"Social Good \u0026 Policy Simulation\"\u003e\n\u003c/a\u003e\n\n\n\n\n\n\n\n\n  \u003c!--\u003cdiv align=\"center\" class=\"header\"\u003e\n    \u003cimg src=\"https://upload.wikimedia.org/wikipedia/commons/d/d0/Google_Colaboratory_SVG_Logo.svg\" alt=\"Google Colab Icon\" width=350\u003e\n  \u003c/div\u003e\n\u003cdiv align=\"center\" class=\"badge\"\u003e\n    \u003ca align=\"center\" href=\"https://colab.research.google.com/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Google%20Colab-Open-orange?style=for-the-badge\u0026logo=googlecolab\" alt=\"Open in Google Colab\"\u003e\n\u003c/a\u003e\n\n  \u003c/div\u003e\n      \u003ch1\u003eData Science Notes\u003c/h1\u003e\n\n\u003cdiv align=\"left\"\u003e\n  \u003ch4\u003e\u003cp\u003eData Science is an interdisciplinary field that uses methods, algorithms, and systems to extract knowledge and insights from data. \n    It combines aspects of \u003cstrong\u003estatistics\u003c/strong\u003e, \u003cstrong\u003ecomputer science\u003c/strong\u003e, and \u003cstrong\u003edomain expertise\u003c/strong\u003e.\u003c/p\u003e\u003c/h4\u003e\n    \u003cp\u003eData science is the study of large quantities of data, which can reveal insights that help organizations make strategic choices.\u003c/p\u003e\n\n  \u003ch3\u003eWhy Take Notes?\u003c/h3\u003e\n \u003cp\u003eStructured notes enhance your understanding, reinforce key concepts, and serve as a valuable reference for future projects and research.\u003c/p\u003e\n\n\n  \u003ch3\u003e Tools Used in Data Science\u003c/h3\u003e\n  \u003ca href=\"https://www.python.org/\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Python-3776AB?style=for-the-badge\u0026logo=python\u0026logoColor=white\" alt=\"Python\"\u003e\u003c/a\u003e\n\n\u003ca href=\"https://www.r-project.org/\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/R-276DC3?style=for-the-badge\u0026logo=r\u0026logoColor=white\" alt=\"R\"\u003e\u003c/a\u003e\n\n\u003ca href=\"https://www.postgresql.org/\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/SQL-4479A1?style=for-the-badge\u0026logo=postgresql\u0026logoColor=white\" alt=\"SQL\"\u003e\u003c/a\u003e\n\n\u003ca href=\"https://scikit-learn.org/\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/ML_Frameworks-FF6F00?style=for-the-badge\u0026logo=tensorflow\u0026logoColor=white\" alt=\"Machine Learning Frameworks\"\u003e\u003c/a\u003e\n\n\u003c/div\u003e--\u003e\n\n\u003c/details\u003e\n\n---\n\n\u003cdiv align=\"center\"\u003e\n⚠️ This repository is uniquely designed by \u003cstrong\u003e@JoshuaThadi.\u003c/strong\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoshuathadi%2Fdata-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjoshuathadi%2Fdata-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoshuathadi%2Fdata-science/lists"}