This course is designed to provide students with a deep understanding of the fundamental concepts and tools used in Data Science. It covers the complete data science pipeline, from data collection and cleaning to analysis, modeling, and visualization. Students will gain hands-on experience with real-world datasets and will learn how to use industry-standard tools such as Python, R, and SQL.

Throughout the course, students will explore core topics like data preprocessing, exploratory data analysis (EDA), and feature engineering. They will also delve into machine learning techniques, including supervised and unsupervised learning, and explore advanced topics like deep learning and big data. The course concludes with a capstone project where students will apply their knowledge to solve a real-world problem using data science methodologies.

Course Objectives: By the end of this course, students will:

Understand the fundamental concepts of Data Science, its applications, and industry trends.
Gain proficiency in using Python, R, and SQL for data manipulation and analysis.
Learn to apply statistical methods and probability theory for data-driven decision-making.
Conduct Exploratory Data Analysis (EDA) to uncover insights from data.
Implement machine learning algorithms such as linear regression, decision trees, and clustering techniques.
Understand key concepts of deep learning, including neural networks and AI applications.
Work with big data technologies and cloud computing platforms like Hadoop, Spark, and AWS.
Apply theoretical knowledge to practical problems through hands-on projects.

Tools and Resources:

Programming: Python, R, SQL
Data visualization: Matplotlib, Seaborn, Plotly
Machine learning: Scikit-Learn, TensorFlow, PyTorch
Cloud: AWS, Google Cloud, Microsoft Azure