INTRODUCTION TO DATA SCIENCE
- Understanding the data science lifecycle.
- Role and responsibilities of a data scientist.
- Overview of data-driven decision-making.
DATA ACQUISITION AND PREPROCESSING
- Sources of data and data collection methods.
- Data cleaning, transformation, and feature engineering.
EXPLORATORY DATA ANALYSIS (EDA)
- Descriptive statistics and data summarization.
- Data visualization techniques
INTRODUCTION TO PROGRAMMING WITH PYTHON
- Basics of Python programming.
- Data structures, control flow, and functions
DATA VISUALIZATION
- Creating static and interactive visualizations using Matplotlib and Seaborn.
- Best practices in data visualization
INTRODUCTION TO MACHINE LEARNING
- Fundamentals of machine learning.
- Supervised vs. unsupervised learning.
MACHINE LEARNING ALGORITHMS
- Linear and logistic regression.
- Decision trees and random forests.
- Model training, validation, and evaluation.
CASE STUDIES AND APPLICATIONS
- Applying data science techniques to real-world problems.
- Presenting findings and insights.
ETHICAL CONSIDERATIONS IN DATA SCIENCE
- Data privacy and security.
- Bias and fairness in machine learning.
CAPSTONE PROJECT
- Hands-on project applying data science concepts to a real dataset.
- Presentation of project results.
