Course info

This course provides a comprehensive introduction to data science concepts, techniques, and tools, using Python as the programming language. Students will learn how to gather, clean, analyze, and visualize data, as well as apply machine learning algorithms to extract insights from real-world datasets. The course emphasizes a thorough understanding of the data science workflow, which includes data collection, preprocessing, exploratory analysis, modeling, evaluation, and model deployment.

Through hands-on exercises and projects, students will gain proficiency in using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn for data manipulation, analysis, and modeling. Additionally, students will learn how to deploy machine learning models for real-world applications, ensuring their solutions are scalable and accessible. By the end of the course, students will be able to implement the full data science workflow, including model deployment, and apply it to tackle data-driven challenges.

Course Learning Outcomes:

Upon successful completion of the course, students will be able to:

1. Understand the data science workflow and its key stages, including data collection, cleaning, exploratory analysis, modeling, and evaluation.
2. Manipulate and preprocess datasets using Python's data science libraries.
3. Perform exploratory data analysis (EDA) to uncover patterns and trends.
4. Build and evaluate machine learning models for predictive analysis.
5. Visualize data insights using Python's plotting libraries.
6. Understand and apply concepts of supervised and unsupervised learning.
7. Work with various data formats and large-scale datasets.
8. Deploy machine learning models to production environments.
   

Recommended Textbooks:

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
  
• Python Data Science Handbook by Jake VanderPlas
• Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller
  
• An Introduction to Statistical Learning by Gareth James et al. (They have Python version)