Data Science for All
Schedule:
Week 1: Introduction to Data Science
What is Data Science?
Overview of Data Science applications in various industries.
Introduction to Python/R (depending on chosen programming language)
Week 2: Data Wrangling and Cleaning
Importing datasets.
Data cleaning techniques.
Handling missing values and outliers.
Week 3: Exploratory Data Analysis
Descriptive statistics.
Data visualization tools (Matplotlib, Seaborn).
Identifying trends and patterns.
Week 4-5: Introduction to Machine Learning
Supervised vs. Unsupervised learning.
Key algorithms (e.g., Linear Regression, K-Nearest Neighbors, K-Means).
Hands-on projects with simple datasets.
Week 6: Advanced Topics
Introduction to Deep Learning.
Natural Language Processing (NLP).
Time-series analysis.
Week 7-8: Capstone Project
Students work on a real-world dataset.
Final presentation of findings and methods used.