Welcome to the Data Science with Python Training! This training program aims to provide you with a comprehensive introduction to the field of data science using the Python programming language.
Through a combination of theory and hands-on exercises, you will learn the fundamental concepts, techniques, and tools used in data science to extract valuable insights from data.
This training is designed to cover the following topics:
- Introduction to Data Science View slide
- What is data science?
- How ML, DS, AI are realted
- Role of Python in data science
- Python data science libraries (NumPy, Pandas, Matplotlib, etc.)
- Data Manipulation and Analysis with Pandas
- Working with data structures (Series, DataFrame)
- Data cleaning and preprocessing
- Data exploration and visualization
- Exploratory Data Analysis (EDA) View EDA
- Descriptive statistics
- Data visualization techniques
- Handling missing data and outliers
- Machine Learning Basics
- Introduction to supervised and unsupervised learning
- Linear regression
- Logistic regression
- Decision trees
- Clustering algorithms
- Model Evaluation and Validation
- Cross-validation techniques
- Evaluation metrics (accuracy, precision, recall, etc.)
- Overfitting and underfitting
- Introduction to Deep Learning
- Neural networks basics
- Deep learning frameworks (TensorFlow, Keras)
- Building and training neural networks
- Introduction to Natural Language Processing (NLP)
- Text preprocessing
- Text classification
- Sentiment analysis
- Web Scraping with BeautifulSoup
- Web scraping with bs4
- Web Scraping HTML Tables Without BeautifulSoup or Any Scraping Tool View blog
As part of this training, you will work on a capstone project that allows you to apply the knowledge and skills acquired throughout the course. The capstone project is designed to simulate a real-world data science scenario, where you will be given a dataset and a specific problem to solve using the techniques learned.
The capstone project will involve the following steps:
- Problem understanding and formulation: You will analyze the given problem statement and identify the key objectives and requirements of the project.
- Data exploration and preprocessing: You will explore the provided dataset, perform data cleaning and preprocessing tasks, and gain insights into the data.
- Model selection and training: Based on the problem requirements, you will select appropriate machine learning or deep learning models, train them on the dataset, and tune their hyperparameters.
- Model evaluation and validation: You will evaluate the performance of your trained models using appropriate evaluation metrics and validation techniques.
- Results and presentation: Finally, you will summarize your findings, draw conclusions, and present your project results in a clear and concise manner.
- The capstone project will allow you to showcase your data science skills and demonstrate your ability to solve real-world problems using Python and data science techniques.
As part of this training, you will also have the opportunity to explore a specific topic or area of interest within data science and write a research paper. The research paper will require you to delve deeper into a particular concept, algorithm, or application related to data science.
You are encouraged to choose a research topic that aligns with your interests and career goals. It could be an emerging trend in data science, a novel approach to a common problem, or an in-depth analysis of an existing algorithm or technique.
- Topic selection and literature review: Choose a research topic and conduct a thorough literature review to understand the existing work and research in that area.
- Problem statement and hypothesis formulation: Clearly define the problem statement and formulate a hypothesis or research question to address in your paper.
- Methodology and experimentation: Describe the methodology or approach you will follow to investigate the problem or validate your hypothesis. Perform experiments or simulations if necessary.
- Data analysis and results: Analyze the data collected or obtained from experiments and present the results in a meaningful and interpretable manner. -Discussion and conclusion: Discuss the findings of your research, draw conclusions, and provide insights into the implications and potential future directions of the study.
Writing a research paper will enhance your critical thinking, research, and communication skills, and allow you to contribute to the broader data science community by sharing your knowledge and findings.
- To get started with the training, follow these steps:
Clone the repository to your local machine:
git clone https://github.com/your-username/data-science-python-training.git
- Navigate to the appropriate lesson or topic directories and open the Jupyter notebooks (.ipynb files) in your preferred Python IDE or Jupyter Notebook environment.
- Follow the instructions and complete the exercises provided in each lesson. The notebooks are designed to guide you through the concepts and provide code snippets and exercises for hands-on practice.
- For the capstone project, refer to the project folder and follow the instructions provided in the project README file.
- For the research paper, choose a topic of interest, conduct research, and follow standard academic writing guidelines to compose your paper.
- Feel free to explore additional resources, such as external readings, research papers, or online tutorials, to deepen your understanding of the topics covered.
- Python Documentation: Official documentation for the Python programming language. NumPy Documentation: Documentation for the NumPy library, which provides powerful numerical computing capabilities in Python.
- Pandas Documentation: Documentation for the Pandas library, which offers flexible data manipulation and analysis tools.
- Matplotlib Documentation: Documentation for the Matplotlib library, which is widely used for data visualization in Python.
- Scikit-learn Documentation: Documentation for the scikit-learn library, which provides a wide range of machine learning algorithms and tools.
- TensorFlow Documentation: Documentation for the TensorFlow library, a popular open-source framework for deep learning
Contributions to this project are welcome. If you find any issues or would like to suggest improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more information.