Data Analysis with Python

Course: Data Analysis with Python 📘 Course Overview Python has become one of the most powerful and widely used programming languages for data analysis, machine learning, and artificial intelligence. This course introduces learners to the fundamentals of data analysis with Python, equipping them with practical skills to clean, analyze, visualize, and interpret datasets for informed decision-making. Whether you are a beginner or looking to advance your analytical skills, this course will help you build confidence in using Python for real-world data projects. 🗂 Course Modules 1. Introduction to Python for Data Analysis Why Python for data analysis? Installing Python and Jupyter Notebook Python basics: data types, operators, and control flow Working with libraries (NumPy, Pandas, Matplotlib, Seaborn) 2. Data Handling with Pandas Importing and exporting datasets (CSV, Excel, SQL, APIs) Data cleaning: handling missing values, duplicates, and errors Filtering, grouping, and aggregating data Working with time-series data 3. Exploratory Data Analysis (EDA) Descriptive statistics (mean, median, mode, variance, correlations) Data visualization with Matplotlib & Seaborn Identifying trends, patterns, and outliers Storytelling with data 4. Advanced Data Analysis Techniques Merging, joining, and reshaping datasets Feature engineering for better insights Automating analysis workflows with Python scripts 5. Introduction to Machine Learning with Python (Optional/Advanced) Understanding supervised vs unsupervised learning Using Scikit-learn for simple predictive models Evaluating model performance 6. Capstone Project Apply everything you’ve learned to a real-world dataset Build an end-to-end data analysis project using Python Present your findings through reports and visualizations Learning Outcomes By the end of this course, learners will be able to: ✔ Use Python libraries (NumPy, Pandas, Matplotlib, Seaborn) for data analysis ✔ Clean, organize, an