Course Description

This course is designed for beginners who want to quickly learn Python essentials for data science. In a short duration, you’ll master fundamental Python concepts, work with essential libraries like NumPy, Pandas, and Matplotlib, and perform data manipulation, visualization, and basic machine learning. By the end of this course, you'll be able to analyze datasets and apply core data science techniques using Python.

🎯 Why Take This Course?

✅ Fast & Focused: Covers key Python concepts in a short time
✅ Hands-on Learning: Work with real-world datasets
✅ Essential Tools: Learn top libraries (NumPy, Pandas, Matplotlib, Scikit-learn)
✅ Beginner-Friendly: No prior programming experience required
✅ Practical Skills: Apply knowledge to real-world data problems

🚀 By the end of this course, you'll be ready to start working on data science projects with Python!

📚 Course Modules Module 1: Python Basics for Data Science

• Introduction to Python and setting up the environment (Jupyter Notebook, VS Code)
• Variables, data types, operators, and basic I/O
• Control flow (if-else, loops) and functions

Module 2: Data Handling with NumPy & Pandas

• Introduction to NumPy: Arrays, indexing, slicing, and operations
• Introduction to Pandas: DataFrames and Series
• Data cleaning: Handling missing values, filtering, and sorting

Module 3: Data Visualization with Matplotlib & Seaborn

• Creating basic plots (line, bar, scatter) using Matplotlib
• Statistical visualization (histograms, box plots, heatmaps) using Seaborn
• Customizing plots for better insights

Module 4: Exploratory Data Analysis (EDA) & Data Preprocessing

• Understanding dataset structure
• Identifying trends, patterns, and outliers
• Feature selection and engineering for better analysis

Module 5: Introduction to Machine Learning with Scikit-learn

• Basics of supervised learning (Regression & Classification)
• Implementing Linear Regression and Decision Trees
• Model evaluation: Accuracy, precision, recall, F1-score