Data Preparation and Analysis

This course introduces the necessary concepts and common techniques for analyzing data. The primary emphasis is on the process of data analysis, including data preparation, descriptive analytics, model training, and result interpretation. The process starts with removing distractions and anomalies, followed by discovering insights, formulating propositions, validating evidence, and finally building professional-grade solutions. Following the process properly, regularly, and transparently brings credibility and increases the impact of the results. This course will cover topics including Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. Besides, this course will review statistical theory, matrix algebra, and computational techniques as necessary. This course prepares students ready for and capable of the data preparation and analysis process. Besides developing Python codes for carrying out the process, students will learn to tune the software tools for the most efficient implementation and optimal performance. At the end of this course, students will have built their inventory of data analysis codes and their confidence in advocating their propositions to the business stakeholders. Required Textbook: This course does not mandate any textbooks because the lecture notes are self-contained. Optional Materials: A Practitioner's Guide to Machine Learning (abbreviated PGML for Reading) Software Requirements: Python version 3.11 or above with the latest compatible versions of NumPy, SciPy, Pandas, Scikit-learn, and Statsmodels libraries. To succeed in this course, learners should possess a basic knowledge of linear algebra and statistics, basic set theory and probability theory, and have basic Python and SQL skills. A few courses that can help equip you with the database knowledge needed for this course are: Introdu