What You Will Learn

This course covers essential skills in data science using Python. You will learn key concepts and techniques in a structured manner, starting from basic to advanced topics.

• Data Acquisition: Understand what data acquisition is and explore various sources, such as databases, APIs, and web scraping. Learn to work with data formats like CSV, JSON, and XML. Practice using Python tools, including requests and BeautifulSoup, for data collection.
• Data Preprocessing and Cleaning: Identify data quality issues, including missing values and duplicates. Learn methods for handling these issues, such as imputation and encoding techniques. You will also explore dimensionality reduction methods like PCA.
• Data Analysis (Descriptive Analytics): Conduct exploratory data analysis (EDA) using univariate and multivariate summary statistics. Use visualization techniques such as histograms and scatterplots to interpret data trends.
• Predictive Analytics – Machine Learning: Learn about model evaluation and core algorithms for regression and classification. You will practice feature engineering and build machine learning models using Python libraries.
• Predictive Analytics – Deep Learning: Understand the basics of neural networks, including activation functions and loss functions. Learn to build and train models using PyTorch or TensorFlow.
• Prescriptive Analytics: Explore optimization and simulation techniques. Learn to apply decision rules to support resource allocation and risk control.
• Generative AI: Familiarize yourself with Python libraries for generative AI applications. Learn to build tools for data analysis using pre-trained models.

Throughout the course, you will engage in hands-on projects that reinforce your learning. Each project is designed to apply the concepts you have learned, allowing you to build practical skills in data science.