In this course, you will learn how to use Python for data analysis. We will cover a range of topics, including data cleaning, data visualization, and statistical analysis. You will also learn how to use popular Python libraries such as NumPy, Pandas, and Matplotlib to manipulate and analyze data. Throughout the course, we will work on real-world data analysis projects to help you apply what you have learned. By the end of the course, you will have a solid understanding of how to use Python for data analysis and be able to use your newfound skills to tackle your own data analysis projects. Course Objectives: Understand the fundamentals of data analysis with Python Learn how to use Python libraries for data manipulation and analysis Practice data cleaning and visualization techniques Develop statistical analysis skills using Python Gain experience working on real-world data analysis projects Course Outline: Introduction to Data Analysis with Python Overview of Python for data analysis Introduction to Jupyter Notebooks Installing necessary libraries Data Manipulation with NumPy and Pandas Arrays and data frames Indexing and slicing data Cleaning data Data Visualization with Matplotlib Plotting basics Customizing plots Creating visualizations for data exploration and presentation Statistical Analysis with Python Probability and statistical distributions Hypothesis testing Regression analysis Real-World Data Analysis Projects Working with real-world datasets Applying Python data analysis techniques to solve problems Presenting findings Prerequisites: Basic knowledge of Python programming Familiarity with programming concepts such as variables, functions, and loops. Note: This course is suitable for anyone interested in data analysis with Python, including beginners who want to learn the basics of data analysis, as well as those with some programming experience who want to expand their skills in this area.