Elevate Your Data Skills with Python, NumPy & Pandas

Enroll in this Free Udemy Course to master Python, NumPy, and Pandas for data analysis and unlock your data insights today!

Are you ready to take your data analysis and manipulation skills to the next level? Welcome to “Mastering Data Manipulation with Python: A Comprehensive Guide to NumPy and Pandas.” In this hands-on course, you’ll embark on a journey to become a proficient data wrangler and analyst using the powerful tools at your disposal.

NumPy is a fundamental library in Python essential for performing complex mathematical operations and enhancing execution speed with its multi-dimensional array objects. It provides built-in functions that simplify array manipulation, enabling seamless integration with data science, data analysis, and machine learning tasks. This course will guide you through creating and managing arrays, finding their dimensions, and performing operations like slicing, filtering, and sorting to effectively manipulate your data with NumPy.

In addition to mastering NumPy, you will dive deep into the Pandas library, another essential tool for data manipulation in Python. You’ll learn to work with Series and DataFrames, filter and reshape data, and conduct aggregation to extract valuable insights. Engage in real-world projects that will enable you to apply your new skills practically, reinforcing your ability to analyze and clean datasets that speak to business needs and scientific inquiries.

What you will learn:

  • Python Basics: A solid foundation in Python programming, including data types, loops, conditionals, functions
  • NumPy Fundamentals: Understanding the NumPy library to efficiently work with arrays, matrices, and perform mathematical operations.
  • Pandas Essentials: Exploring the Pandas library in-depth, covering Series and DataFrames, data importing/exporting, data cleaning, filtering, sorting, grouping

Course Content:

  • Sections: 10
  • Lectures: 97
  • Duration: 6h 26m

Requirements:

  • Python Programming

Who is it for?

  • Beginners in Data Analysis
  • Data Enthusiasts
  • Aspiring Data Scientists
  • Business Analysts
  • Students and Researchers
  • Professionals Upgrading Skills
  • Entrepreneurs
  • Self-Learners

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