This course is an intensive guide that aids the students to learn what Data Analysis is and working with complex data analysis problems. You will learn to use Pandas for data analysis and Seaborn for data visualization, with JupyterLab as your IDE. Additionally, you’ll learn how to get, clean, prepare, and analyze data, including time-series data. Moreover, you’ll learn to use linear regression models to predict unknown and future values. This course introduces the critical tasks of data collection and data wrangling, presentation and understanding of descriptive statistics and basics of visualization. Hence, the course first discusses tasks such as data collection and sampling, generalization from the sample to the population and it also presents some of the key methods of data analysis. And in the last section of this course, we will discuss supervised and unsupervised Machine Learning models used in Classification, regression, and clustering, and how to evaluate the ML models. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models through hands-on projects using Python.