Python Fundamentals and Data Science Essentials

This course starts with an introduction to Python programming, covering everything from installation and setup of Python and Anaconda to fundamental concepts such as variables, numeric and logical operations, control structures like if-else and loops, and defining functions. The journey continues with in-depth modules on strings and lists, ensuring a solid understanding of these core components. Building on Python fundamentals, you will explore data analysis with NumPy and Pandas. You will learn about array operations in NumPy, manipulating and analyzing data using Pandas, including working with DataFrames, performing data operations, indexing, and merging datasets. These modules are designed to provide you with a strong foundation in data manipulation and analysis, critical for any data science role. The course culminates with an introduction to basic machine learning concepts. You will delve into linear regression, understanding its mathematical foundations and practical applications. Furthermore, you will explore gradient descent, a crucial optimization technique, and KNN classification, one of the simplest machine learning algorithms. Each topic is reinforced with case studies, ensuring you can apply theoretical knowledge to real-world scenarios. This course is ideal for beginners in programming and data science. No prior experience in Python or data analysis is required, but a basic understanding of mathematics will be beneficial.

• Prerequisite - Python Fundamentals
• In this module, we will cover the essential Python programming concepts needed as a foundation for advanced topics. Starting from installation and basic syntax to detailed explorations of various data structures, this section ensures you have a solid grounding in Python.
• Prerequisite - NumPy
• In this module, we will introduce NumPy, a powerful library for numerical computing in Python. Through a series of hands-on videos, you'll learn to perform essential NumPy operations and leverage its capabilities for data analysis.
• Prerequisite - Pandas
• In this module, we will dive into Pandas, a key library for data manipulation and analysis in Python. You will learn how to work with Series and DataFrames, perform various operations, and handle real-world data sets efficiently.
• Prerequisite - Some Fun with Math
• In this module, we will cover essential linear algebra concepts that are foundational for machine learning. From vectors and matrices to multi-dimensional spaces, you'll gain the mathematical skills necessary for advanced algorithms.
• Prerequisite - Data Visualization
• In this module, we will explore data visualization techniques using Matplotlib and Seaborn. Through practical examples and a case study, you'll learn how to create compelling visual representations of data to uncover insights.
• Prerequisite - Simple Linear Regression
• In this module, we will cover the basics of simple linear regression, a key statistical technique. Starting from machine learning concepts, you'll learn how linear regression works, the math behind it, and how to apply it through case studies.
• Prerequisite - Gradient Descent
• In this module, we will focus on gradient descent, a crucial optimization algorithm. From understanding cost functions to applying gradient descent in practical scenarios, you'll gain a deep understanding of this essential technique.
• Prerequisite - Classification: KNN
• In this module, we will delve into the K-Nearest Neighbors (KNN) algorithm for classification. You'll learn the theory behind KNN, its practical applications, and how to measure its performance through various case studies.
• Prerequisite - Logistic Regression
• In this module, we will cover logistic regression, a fundamental classification technique. You'll learn about the Sigmoid function, log odds, and how to apply logistic regression in real-world scenarios through case studies.
• Prerequisite - Advanced Machine Learning Algorithms
• In this module, we will explore advanced machine learning algorithms, focusing on regularization techniques and model selection. Through detailed examples and case studies, you'll learn how to apply these advanced methods to improve model performance.