faustinoaq / The-Data-Science-Learning-Hub

This repository contains resources and materials for a data science bootcamp. The bootcamp is designed to teach individuals the fundamentals of data science.

The Data Science Learning Hub

This repository contains resources and materials for a data science bootcamp. The bootcamp is designed to teach individuals the fundamentals of data science. In this bootcamp, you'll learn the foundational skills and tools necessary to work with data, including: Introduction to Data Science, Data Preprocessing, Exploratory Data Analysis, Statistical Inference, Machine Learning, Deep Learning, Natural Language Processing, Big Data and Distributed Computing, Data Visualization and Communication

Curriculum

The curriculum for the data science bootcamp is organized into the following modules:

1. Python Programming Language
   • Data Types
   • Control Statements
   • Functions
   • Object Oriented Programming
   • Modules and Packages
   • Exception Handling
   • Advanced Python Concepts
2. Mathematics for Data Science
   • Linear Algebra
   • Calculus
   • Probability
   • Statistics
3. Data Engineering
   • Primal Concepts
   • Data Storage and Management
   • Exploratory Data Analysis
   • Data Preprocessing
   • Feature Engineering
   • Big Data and Distributed Computing
4. Machine Learning Algorithms
   • Supervised Learning
   • Unsupervised Learning
5. Deep Learning and Neural Networks
   • Neural Networks
   • Deep Learning ToolKits
   • Convolutional Neural Networks (CNNs)
   • Sequence Models
6. Miscellaneous Topics
   • Git
   • SQL
   • Docker
   • Streamlit

Each module includes a set of lectures and exercises to help you learn and apply the concepts covered in the module.

Getting Started

To get started with the bootcamp, you'll need to install the following tools:

• Python 3.7+
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• NLTK
• Scipy
• Simpy
• Scikit-learn
• Tensorflow
• Pytorch
• Streamlit

You can install these tools using Anaconda, a popular distribution of Python that includes all of these packages or you can follow along pip as well for the installation. You may copy the code below and run it it the terminal to install all the required packages.

pip install -r requirements.txt

Besides Python and realted packages, The sixth chapter under the title Miscellaneous includes different surronding resources that a data scientist should be familiar with. The installation of each of those topics are provided with guidelines in their respective locations.

Contributing

If you find any issues or errors in the material provided in this repository, please feel free to open an issue or submit a pull request.

License

This project is created for educational purpose. Any kind of abuse can be subjected to copyright issues.