Course Details
This Data Science course provides comprehensive training in data analysis, machine learning, and data visualization. You'll gain hands-on experience with real datasets and build a professional portfolio of data science projects.
- Python for Data Science
- Data Cleaning & Preprocessing
- Exploratory Data Analysis
- Statistical Analysis & Hypothesis Testing
- Data Visualization Techniques
- Machine Learning Fundamentals
- Supervised & Unsupervised Learning
- Deep Learning Basics
- Natural Language Processing
- Real-world Data Science Projects
Course Content
- Python Programming Basics
- NumPy for Numerical Computing
- Pandas for Data Manipulation
- Data Structures for Data Science
- Working with APIs
- Python Data Science Ecosystem
- Object-Oriented Programming
- Exploratory Data Analysis (EDA)
- Data Cleaning Techniques
- Feature Engineering
- Statistical Analysis Methods
- Hypothesis Testing
- Working with Time Series Data
- Handling Missing Data
- Matplotlib & Seaborn
- Plotly for Interactive Visuals
- Geospatial Data Visualization
- Dashboard Creation
- Storytelling with Data
- Visualization Best Practices
- Tableau Basics
- Supervised Learning Algorithms
- Unsupervised Learning Techniques
- Model Evaluation & Validation
- Hyperparameter Tuning
- Ensemble Methods
- Feature Selection Techniques
- Model Deployment Basics
What you'll learn
- Master Python for data analysis and visualization
- Clean, transform, and analyze complex datasets
- Build and evaluate machine learning models
- Create compelling data visualizations
- Apply statistical methods to real-world problems
- Work with big data technologies
- Communicate data insights effectively
- Build a professional data science portfolio