CERTIFICATES

This module introduces Python, exploring its varied uses and the Python interpreter for program execution. It covers the interactive prompt, guiding students through starting and running interactive code sessions to facilitate hands-on learning with Python.

In this module, students explore Python's conceptual hierarchy, emphasizing the significance of built-in types and their usage. The curriculum covers data types, focusing on string manipulation, control flow, immutability, type-specific methods, and garbage collection. Additionally, it delves into numeric types, expression operators, variables, basic expressions, and numeric display formats in Python.

This module introduces fundamental concepts in Python: strings, lists, and dictionaries. It covers string basics including quoting methods and escape sequences, fundamental string operations like indexing, slicing, and conversion tools. Additionally, it explores list operations, iteration, and modifications, along with dictionaries, nesting, and basic dictionary operations, providing a foundational understanding of these data structures in Python.

In this module, students explore tuples, sets, and file operations in Python. It covers opening and utilizing files, various assignment statements including sequence, multiple-target, and augmented assignments, along with rules for variable naming. Additionally, it involves expression statements, print operations, and a Hangman project, offering practical insights into Python's diverse functionalities and projects.

This module in Python covers fundamental aspects of conditional and looping statements. It introduces 'if' statements for conditional execution and explores the usage of 'while' and 'for' loops for repetitive tasks. The curriculum includes control flow modifiers like 'break,' 'continue,' and 'pass,' offering comprehensive insights into managing program flow and decision-making structures.

This module introduces functions and generators in Python, covering their basics, definition, calls, and showcasing polymorphism. It details local variables, Python scope basics, the LEGB rule for name resolution, built-in scope, and the 'global' statement. Additionally, it explores nested functions and their scopes, providing a comprehensive understanding of Python's function structure and scoping rules.

This module focuses on Python's modular approach using modules and packages. It covers importing modules, organizing code into packages, and utilizing namespaces for accessing module contents. Additionally, it explores 'init.py' files in packages, offering insights into creating, importing, and effectively using modular structures in Python programming.

This module delves into exceptions handling in Python, addressing error management and recovery strategies. It covers try-except blocks, raising exceptions, and utilizing tools for effective debugging and error resolution. Students explore exception handling techniques, debugging tools, and error recovery methods, enhancing their skills in managing errors and ensuring robust code performance.

This module covers intro to the GroupBy Module, First Operations with groupby Object, Retrieve a group from a GroupBy object with the get_group Method, Methods on the Groupby Object and DataFrame Columns, Grouping by Multiple Columns and Iterating through Groups.

In this module, students delve into data analysis using the Numpy library in Python. It covers array creation, manipulation, mathematical operations, and statistical functionalities provided by Numpy. The curriculum explores Numpy's array manipulation, mathematical capabilities, and statistical tools, offering a comprehensive understanding of data analysis with this powerful library.

This module centers on data analysis utilizing the Pandas library in Python. It covers data manipulation, handling, and analysis using Pandas' DataFrame and Series structures. Students explore Pandas' functionalities for data ingestion, cleaning, manipulation, and analysis, gaining a robust toolkit for comprehensive data handling and exploration.

This module explores comprehensive data visualization using Pandas, covering advanced data selection, time series analysis, and exploratory data analysis (EDA) techniques. It delves into creating  multi-indexing, handling large datasets, and applying Pandas to real-world case studies.

This module introduces data visualization using Matplotlib in Python, covering plot creation for line, scatter, and bar plots. It explores plot customization, including labels, titles, legends, and annotations, and delves into subplots, saving plots, and exporting them. Additionally, it includes visualization of categorical data, distributions, relationships, and geographic data using various techniques and plot types within Matplotlib, offering a comprehensive understanding of data visualization in Python.

This module introduces Seaborn for exploratory data analysis (EDA) in Python. It covers Seaborn's features, focusing on visualizing data distributions using histograms, KDE plots, and rug plots. It explores visualizing relationships via scatter plots, pair plots, and joint plots, as well as categorical data visualization using bar plots, count plots, and box plots. Additionally, it includes styling and customization features within Seaborn, providing a comprehensive toolkit for effective EDA using Seaborn.