Python for MATLAB Users

Starting at USD 39.00
4 hours duration

Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.

Transitioning from Matlab to Python is a valuable skill in today's data science landscape. Python has gained widespread popularity among companies worldwide for its versatility in extracting insights from data and gaining a competitive advantage. This course is specifically designed to assist Matlab users in seamlessly transitioning to Python for data analysis and visualization. Throughout this course, you will gain a solid understanding of Python syntax and its application in data science. You will learn how to effectively utilize NumPy arrays to store and manipulate data, as well as harness the power of the matplotlib library to uncover trends, correlations, and patterns within real datasets. Examples include analyzing bicycle traffic in Seattle and tracking avocado prices across the United States. Additionally, this course will equip you with the necessary skills to control the flow of your Python code. You will explore the usage of conditional statements such as if, else, and elif, along with comparison operators, to determine the execution of specific code lines. By the conclusion of this course, you will possess a strong foundation in Python, including its popular dictionaries and libraries. Through hands-on experience, you will gain the confidence to apply your newfound skills to your professional endeavors, personal projects, or further educational pursuits.

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DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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