Course Overview
The Python for Data Science course is designed for beginners and professionals who want to gain hands-on experience in data analysis, visualization, and machine learning using Python. This course covers the fundamental concepts of Python programming, data manipulation with Pandas, data visualization with Matplotlib and Seaborn, and an introduction to machine learning with Scikit-Learn.
Whether you’re looking to start a career in data science, enhance your analytical skills, or automate data processing, this course provides all the necessary tools to work with real-world datasets effectively.
What You’ll Learn
By the end of this course, you will be able to:
✅ Understand the basics of Python and its libraries for data science.
✅ Work with Pandas for data manipulation and handling large datasets.
✅ Visualize data using Matplotlib and Seaborn for better insights.
✅ Perform data cleaning and preprocessing to handle missing values and inconsistencies.
✅ Apply statistical analysis to draw meaningful conclusions from data.
✅ Build machine learning models using Scikit-Learn.
✅ Automate data workflows and work with real-world datasets.
This course is structured with hands-on exercises, projects, and quizzes to reinforce learning.
Curriculum Overview
📌 Module 1: Introduction to Python & Data Science
- Overview of Python and its applications in data science
- Setting up Python and Jupyter Notebook
- Basic Python programming (variables, loops, functions)
📌 Module 2: Working with Data in Python
- Introduction to NumPy and array operations
- Using Pandas for data manipulation (DataFrames, Series)
- Handling missing values and data transformation
📌 Module 3: Data Visualization Techniques
- Introduction to Matplotlib and Seaborn
- Creating bar charts, histograms, scatter plots, and heatmaps
- Customizing plots for better representation
📌 Module 4: Data Cleaning & Preprocessing
- Handling missing values and duplicates
- Feature scaling and normalization
- Encoding categorical data
📌 Module 5: Statistical Analysis & Data Insights
- Descriptive statistics (mean, median, mode, standard deviation)
- Hypothesis testing and probability distributions
- Correlation and regression analysis
📌 Module 6: Introduction to Machine Learning with Python
- Understanding supervised vs. unsupervised learning
- Building and evaluating machine learning models with Scikit-Learn
- Implementing linear regression, decision trees, and clustering
📌 Module 7: Working with Real-World Datasets
- Loading and analyzing datasets from Kaggle
- Performing exploratory data analysis (EDA)
- Automating data processing tasks
📌 Module 8: Capstone Project & Certification
- Solving a real-world data science problem
- Presenting insights and findings
- Course completion certification
Who Should Enroll?
📊 Beginners in Data Science looking for a structured learning path
🖥️ Students & Professionals transitioning into a data science career
📚 Researchers & Analysts working with large datasets
🚀 Developers & Engineers interested in AI and machine learning
Instructor Information
This course is taught by experienced data scientists from School of AI, ensuring in-depth learning with practical case studies.
