By the end of this course, learners will:

1. Understand the fundamentals of data science, its applications, and real-world use cases.
2. Learn how to collect, clean, and analyze data using Python and Pandas.
3. Perform exploratory data analysis (EDA) and visualize data using Matplotlib and Seaborn.
4. Gain insights into statistical concepts such as mean, median, standard deviation, and correlation.
5. Learn the basics of supervised and unsupervised machine learning models.
6. Work on hands-on projects using real-world datasets.
7. Develop an understanding of feature engineering, model evaluation, and deployment basics.