Data Science with Python Course

Objectives

• Gain a solid understanding of Python programming fundamentals, including data types, control structures, functions, and object-oriented programming.
• Learn how to manipulate, clean, and preprocess data using Python libraries such as NumPy and Pandas, enabling you to prepare data for analysis effectively.
• Develop proficiency in data visualization using libraries like Matplotlib and Seaborn, allowing you to create insightful charts, graphs, and plots to communicate data effectively.
• Master essential statistical concepts and techniques, including probability, hypothesis testing, and regression analysis, to analyze and draw insights from data.
• Acquire knowledge of machine learning algorithms and techniques, such as classification, regression, clustering, and dimensionality reduction, enabling you to build predictive models.
• Learn how to evaluate and validate machine learning models using techniques like cross-validation, performance metrics, and model selection to ensure robust and accurate predictions.
• Understand the concepts of deep learning and neural networks, exploring popular libraries like TensorFlow or PyTorch to build and train deep learning models.
• Gain hands-on experience with real-world data science projects, allowing you to apply your Python skills and knowledge to solve practical problems and extract valuable insights.