Course Outline

• Introduction to Data Science: Overview of data science concepts, tools, and applications.
• Data Wrangling and Preprocessing: Techniques for cleaning, transforming, and preparing raw data.
• Exploratory Data Analysis (EDA): Analyzing data to uncover patterns, trends, and insights
• Statistical Foundations for Data Science: Fundamentals of statistics including probability, distributions, and hypothesis testing.
• Machine Learning Fundamentals: Introduction to supervised, unsupervised, and reinforcement learning algorithms.
• Model Evaluation and Validation: Techniques for assessing and improving the performance of machine learning models.
• Feature Engineering and Selection: Methods for creating new features and selecting the most relevant ones for modeling.
• Advanced Machine Learning Techniques: Deep dive into advanced algorithms like neural networks and ensemble methods.
• Natural Language Processing (NLP): Basics of text processing, sentiment analysis, and document classification.
• Big Data Analytics: Introduction to handling and analyzing large-scale datasets using tools like Spark and Hadoop.
• Capstone Project: Applying learned skills to solve a real-world data science problem and build a comprehensive portfolio.
• Ethics and Responsible Data Science: Considerations for ethical data handling, privacy, and bias mitigation in data science projects.
  
• Deployment and Presentation: Strategies for deploying data science solutions and effectively communicating results to stakeholders.