Course Overview
- Course Title: Exploratory Data Analysis (EDA) & Visualization with Python
- Instructor: Muhammad Shafiq (Data Scientist, AI/ML Engineer, Lecturer, Researcher)
- Target Audience:
- Aspiring data scientists, data analysts, and researchers
- Professionals seeking data-driven decision-making skills
- Python beginners with interest in data exploration & visualization
- Prerequisites:
- Basic Python programming knowledge
- Familiarity with Jupyter Notebooks (recommended but not mandatory)
Curriculum Highlights
- Key Topics Covered:
- Data Cleaning: Handling missing values, outliers, and data type corrections
- Statistical Analysis: Descriptive statistics, distributions, correlation analysis
- Data Visualization:
- Static plots (histograms, scatter plots, box plots) with Matplotlib & Seaborn
- Interactive visualizations with Plotly
- Feature Engineering: Techniques for creating insightful data features
- Real-World Case Studies: Hands-on EDA projects with practical datasets
- Key Skills Learned:
- Perform end-to-end exploratory data analysis in Python
- Clean and preprocess raw datasets for analysis
- Create publication-ready visualizations for data storytelling
- Apply statistical techniques to uncover data patterns
- Build interactive dashboards for dynamic data presentation
Course Format
- Duration: N/A (Self-paced; includes 3 practice tests)
- Format: Online video lectures (accessible on mobile & desktop)
- Resources:
- Practice tests (3 included)
- Downloadable code templates & datasets
- Mobile-accessible content for learning on the go
Special Offer (If Applicable)
- Limited Time Coupon Code: N/A