Data Science for Engineers: Part 2

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Related lectures (31)

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Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.

Data Science Visualization with Pandas

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Data Science: Python for Engineers - Part II

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

General Introduction to Data Science

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Collaborative Data Science: Tools and Techniques

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective learning.

Data Science Essentials: Pandas, Numpy, Matplotlib

Introduces Pandas, Numpy, and Matplotlib for data analysis and visualization in Python.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods

Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.