Introduction to Python: Basics and Anaconda Setup

Related lectures (30)

Page 3 of 3

Introduces Python modules, scoping, lambdas, and pandas for data manipulation and analysis.

Covers Python basics, Pandas, data manipulation, visualization, and machine learning.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.

Explores recursive functions, including factorials and Fibonacci sequences, and their scope and namespaces.

Provides a Python crash course covering basic arithmetics, complex numbers, strings, lists, dictionaries, and more.

Introduces Python programming basics using Anaconda and JupyterLab, with practical exercises on syntax and libraries.

Introduces data science topics, evaluation methods, and course organization for the semester.

Introduces setting up the environment, Git basics, and Jupyter Notebook features with practical examples.

Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.