🟥 Course Description
Introduction:
This course introduces data science lifecycle and Python programming, highlighting key libraries such as NumPy, Pandas, Matplotlib, and Seaborn. Participants will learn data preprocessing techniques, including cleaning, handling missing values and outliers, and data transformation. The course covers exploratory data analysis (EDA) methods for visualizing and summarizing data insights. An overview of machine learning concepts and types (supervised, unsupervised, reinforcement) will be provided, including supervised learning algorithms (linear regression, decision trees) and unsupervised clustering techniques (K-means, hierarchical clustering). Case studies and practical applications will be included. Delivery methods consist of interactive lectures, group discussions, and case study analysis.
Objectives:
– Understand the fundamentals of data science and machine learning concepts.
– Gain proficiency in using Python for data analysis and machine learning.
– Apply machine learning algorithms to solve real-world problems.
