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:
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Introduction to Data Science View slide
- What is data science?
- How ML, DS, and AI are related
- 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
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Machine Learning Basics
- Introduction to supervised and unsupervised learning
- Linear regression
- Logistic regression
- Decision trees
- Clustering algorithms
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Model Evaluation and Validation
- Cross-validation techniques
- Evaluation metrics (accuracy, precision, recall, etc.)
- Overfitting and underfitting
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Introduction to Deep Learning
- Neural networks basics
- Deep learning frameworks (TensorFlow, Keras)
- Building and training neural networks
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Introduction to Natural Language Processing (NLP)
- Text preprocessing
- Text classification
- Sentiment analysis
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Web Scraping with BeautifulSoup
- Web scraping with bs4
- Web Scraping HTML Tables Without BeautifulSoup or Any Scraping Tool View blog
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Extras
- Design Thinking for Data Science
- Lean Canvas for Data Science
- Design Psychology for product development
- CV/Resume making
- Digital Persona
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.
Find the student's project in Notion
Instructions to students- click
Editand add your project details accordingly.
- 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.