Welcome to the world of data analytics with Python! This is a repository that I have created to showcase my skills, share projects and track my progress in Data Science and Machine learning related topics from the Data Science and Machine Learning course I have completed. I have also included my work in programming languages such as Python, SQL. These projects showcase my ability to extract valuable insights from large and complex data sets, and to combine data from multiple sources to create a comprehensive view of the data.
Python is a popular programming language and software environment for data analysis. It is widely used by statisticians, data scientists, and researchers because of its powerful tools for data manipulation, visualization, and statistical modelling.
Data Science and Machine Learning Course
Module 1: Python Programming
This module covers the fundamentals of Python programming for data science.
1: Language Introduction and Installation
• Python history, features
• Python and PyCharm installation
2: Python Basics
• Print command, comments, escape sequences
• Variables, data types, operators
3: Conditional and Looping Statements
• Selection statements, control statements
• Break and continue statements, nested loops
4: Data Structures in Python
• Introduction to user-defined data structures
• Non-primitive data structures: list, dictionaries, set, tuples, strings, sequences
• Accessing and modifying elements in data structures
• Comprehension: list, set, and dictionary
5: Functions in Python
• Defining functions, passing arguments
• Different types of arguments
• Returning values from functions
• Local and global namespace, lambda functions
• Recursion, filter, map, reduce, eval
• Generators and decorators
6: File Handling and Exception Handling
• File processing, reading and writing files using 'with' statements
• Exception handling: What is an exception?
• Raising and catching exceptions
• Handling errors gracefully using try-catch-finally
7: Object-Oriented Programming (OOP)
• Introduction to OOPs, classes, and objects
• Inheritance, polymorphism, encapsulation, and abstraction
8: Modules in Python
• Introduction to modules, importing modules
• Creating and using modules
9: Regular Expressions
• Defining regular expressions
• Using regular expressions with Python
10: Pandas and Numpy
• Introduction to Pandas library
• Reading and writing data with Pandas
• Data cleaning and exploration with Pandas
• Introduction to Numpy, Numpy basic operations
11: Data Visualization with Matplotlib and Seaborn
• Introduction to Matplotlib and Seaborn
• Basic and advanced plotting techniques