Course Description

This course provides an introduction into the world of data science, walking students through the entire process of data analysis. It emphasizes practical skills in working with data, such as acquisition, cleaning, exploration, feature engineering, and the use of machine learning algorithms to develop predictive models. The course is a hands-on learning experience, conducted primarily through Jupyter notebooks, which integrate code, images, and interactive elements in a single document.

What Students Will Learn

• How to utilize the Jupyter Notebook environment for data science projects.
• Application of essential Python libraries like pandas, numpy, matplotlib, and scikit-learn.
• The complete data science workflow, from acquiring and cleaning data to making predictions.
• Practical machine learning, including feature engineering and model training.
• Skills for continuing the learning journey in data science and machine learning beyond this course.

Prerequisites

No prior knowledge is required to take this introductory course. However, a basic familiarity with programming concepts could be beneficial.

Course Coverage

• Introduction to the Jupyter Notebook environment
• Overview of Python data science libraries: pandas, numpy, matplotlib, scikit-learn
• Data acquisition methods
• Data cleaning techniques
• Data exploration and insight derivation
• Feature engineering for machine learning
• Machine learning model creation and training
• Application of models for prediction and analysis

Who This Course is For

This course is ideal for beginners who are looking to start their journey in data science, professionals interested in data-driven decision making, and hobbyists who enjoy data exploration and analysis.

Real-World Application

Skills acquired from this course can be applied in various real-world settings such as:

• Business analytics to drive strategic decisions
• Scientific research for data analysis
• Enhancing IT operational efficiency via data-driven insights
• Fraud detection with predictive modeling
• Improving customer experiences through behavior analysis