Introduction to Data Science
- Understanding the Data Science landscape
- Importance of data in modern decision-making
Data Wrangling and Exploration
- Data collection and cleaning
- Exploratory data analysis techniques
Fundamentals of Statistics
- Descriptive and inferential statistics
- Probability distributions and hypothesis testing
Machine Learning Foundations
- Supervised vs. unsupervised learning
- Feature engineering and selection
Regression and Classification
- Linear regression
- Logistic regression
Clustering and Dimensionality Reduction
- K-means clustering
- Principal Component Analysis (PCA)
Introduction to Neural Networks
- Basics of artificial neural networks
- Activation functions and architecture
Natural Language Processing (NLP)
Time Series Analysis
Capstone Project
- Applying data science techniques to solve a real-world problem
This course focuses on providing a comprehensive understanding of data science concepts and techniques.
Exploratory data analysis helps uncover patterns and insights in data that can guide further analysis.
Common hypothesis testing techniques include t-tests and ANOVA for comparing means.
Machine learning allows systems to learn from data and improve performance over time without being explicitly programmed.
Classification algorithms aim to assign input data points to predefined categories or classes.