Overview

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his course provides a solid foundation in Python for data science, focusing on NumPy, Matplotlib, Pandas, and a touch of machine learning. Learners will gain practical experience with essential data science tools, enhancing their ability to manipulate data, visualize it, and perform basic machine learning tasks. By the end of the course, students will be prepared to tackle more advanced data science topics with a strong understanding of how Python is used in real-world applications. In the first section, you will get an introduction to NumPy, focusing on its powerful array operations and speed advantages over traditional Python lists. You'll explore matrices, dot products, and linear systems to understand the foundation of numerical computing. Practical exercises will reinforce these concepts, making sure you are comfortable working with NumPy in data science. Next, you'll move to Matplotlib, where you'll learn how to visualize data effectively. Through hands-on practice with line charts, scatterplots, histograms, and image plotting, you'll become proficient in presenting data in various graphical formats. This section will equip you with the tools to visually analyze data and communicate insights clearly. In the final section, you'll dive into Pandas, one of the most widely used libraries for data manipulation. You'll master techniques like loading data, selecting rows and columns, and applying functions to dataframes. You'll also explore plotting capabilities within Pandas. As a bonus, you'll be introduced to SciPy and basic machine learning concepts to understand how these tools integrate into data science workflows. This course is ideal for anyone starting their data science journey or looking to strengthen their Python skills for data analysis. A basic understanding of Python is required, and the course is designed for beginners. If you are interested in learning how to use Python for data manipulation, visualization, and introductory machine learning, this course will set you up for success.

Syllabus

Welcome and Logistics

In this module, we will introduce the course structure and explain the available resources. This will help you navigate the learning process smoothly and maximize your course experience.

NumPy

In this module, we will dive into NumPy, a powerful library for numerical computing. You'll learn how to work with arrays, solve linear algebra problems, and generate data, with hands-on examples to reinforce each concept.

Matplotlib

In this module, we will explore Matplotlib, a library used to create a variety of visualizations. You'll gain practical experience in generating charts and plots, helping you present data clearly and effectively.

Pandas

In this module, we will explore the Pandas library, a key tool for data manipulation. You will learn how to work with data frames, filter data, and create visualizations, enhancing your ability to analyze real-world datasets.

SciPy

In this module, we will introduce SciPy, a library built for scientific and technical computing. You'll learn about statistical distributions, convolution, and how to apply these techniques to real-world problems.

Machine Learning Basics

In this module, we will provide a foundational overview of machine learning, including core algorithms like classification and regression. You’ll gain hands-on experience with code and learn how to apply these techniques effectively.