Objective: Gain a solid understanding of the fundamentals of data science, its various applications and the stages involved in a data science project. Learn about the key roles, responsibilities and skills required in the field of data science.

Objective: Explore different methods of data collection, including retrieving data from APIs, web scraping and databases. Learn how to acquire both structured and unstructured data for analysis.

Objective: Learn techniques for cleaning and transforming raw data into a usable format. This includes handling missing values, dealing with outliers and applying normalization to prepare data for analysis.

Objective: Conduct exploratory data analysis to understand the distribution of data, detect patterns and generate insights using statistical methods and visualizations.

Objective: Master the creation of effective visualizations using tools such as Matplotlib and Seaborn. Learn how to communicate key insights and trends through charts, graphs and plots.

Objective: Acquire foundational knowledge in statistics, including concepts like probability, distributions, hypothesis testing and correlation analysis, which are essential for interpreting data.

Objective: Learn to use Python for data manipulation, focusing on libraries like Pandas and NumPy to process, clean and analyze data effectively.

Objective: Understand the basics of machine learning, including the distinction between supervised and unsupervised learning, key algorithms and how to evaluate machine learning models.

Objective: Learn about supervised learning algorithms, including linear regression, logistic regression and decision trees. Understand how to build, train and evaluate these models.

Objective: Explore unsupervised learning techniques, particularly clustering methods such as K-means and hierarchical clustering, to identify patterns in unlabeled data.

Objective: Learn how to evaluate machine learning models using various performance metrics, including accuracy, precision, recall, F1 score and cross-validation techniques to ensure robust model performance.

Objective: Apply all the skills learned in the course to a hands-on data science project. Students will analyze a real-world dataset, perform necessary preprocessing, apply machine learning models and present their findings.

This syllabus provides a comprehensive introduction to data science, equipping students with both theoretical knowledge and practical skills necessary to pursue advanced studies or begin a career in data science confidently.