Module #1 What is Data Science? Introduction to the field of data science, its importance, and its applications.
Module #2 Types of Data Overview of different types of data, including numerical, categorical, and text data.
Module #3 Data Visualization Introduction to data visualization, its importance, and popular data visualization tools.
Module #4 Python for Data Science Introduction to Python programming language, its popularity in data science, and basic syntax.
Module #5 NumPy and Pandas Introduction to NumPy and Pandas libraries in Python, and their applications in data science.
Module #6 Data Preprocessing Introduction to data preprocessing, including data cleaning, handling missing values, and data normalization.
Module #7 Introduction to Statistics Overview of descriptive statistics, inferential statistics, and probability theory.
Module #8 Data Visualization with Matplotlib and Seaborn Using Matplotlib and Seaborn libraries in Python for data visualization.
Module #9 Working with CSV and Excel Files Importing and working with CSV and Excel files in Python using Pandas.
Module #10 Introduction to Machine Learning Overview of machine learning, its types, and popular machine learning algorithms.
Module #11 Supervised Learning Introduction to supervised learning, including regression and classification problems.
Module #12 Unsupervised Learning Introduction to unsupervised learning, including clustering and dimensionality reduction.
Module #13 Model Evaluation Metrics Introduction to model evaluation metrics, including accuracy, precision, recall, and F1 score.
Module #14 Overfitting and Underfitting Understanding overfitting and underfitting in machine learning, and techniques to prevent them.
Module #15 Introduction to Scikit-learn Introduction to Scikit-learn library in Python, and its applications in machine learning.
Module #16 Working with Text Data Introduction to working with text data, including text preprocessing and text visualization.
Module #17 Introduction to Natural Language Processing Overview of natural language processing, including text processing and sentiment analysis.
Module #18 Data Storytelling Introduction to data storytelling, including communicating insights and results effectively.
Module #19 Big Data and NoSQL Databases Introduction to big data and NoSQL databases, including Hadoop and MongoDB.
Module #20 Data Science Workflow Introduction to the data science workflow, including problem definition, data collection, and deployment.
Module #21 Collaboration and Communication in Data Science Importance of collaboration and communication in data science, including working with stakeholders.
Module #22 Data Science Tools and Technologies Overview of popular data science tools and technologies, including Jupyter, Git, and Tableau.
Module #23 Ethics in Data Science Introduction to ethics in data science, including bias, privacy, and accountability.
Module #24 Course Wrap-Up & Conclusion Planning next steps in Introduction to Data Science career
