A Gentle Introduction to Data Science

Übersicht

Explore data science fundamentals, from AI's influence to practical machine learning in Python. Covers key concepts, problem-solving applications, and essential Python tools for aspiring data scientists.

Lehrplan

• Introduction to Data Science
Overview of Data Science and its impact
AI's role within Data Science
Real-world applications of Data Science
• Data Science Methodology
Understanding the data science process
Problem formulation and hypothesis generation
Data-driven decision making
• Data Collection and Cleaning
Types of data: structured and unstructured
Data collection techniques
Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
Descriptive statistics
Data visualization techniques
Identifying patterns and insights
• Introduction to Python for Data Science
Python basics and setup
Jupyter notebooks
• Essential Python Libraries
NumPy for numerical data
Pandas for data manipulation
Matplotlib and Seaborn for data visualization
• Introduction to Machine Learning
Supervised vs. Unsupervised learning
Model selection and evaluation
Overfitting and underfitting
• Practical Machine Learning in Python
Scikit-learn basics
Building simple linear regression models
Classification with decision trees and logistic regression
• Data Science Ethics
Importance of ethics in data science
Data privacy and security
Bias and fairness in AI
• Capstone Project
Define a data-driven problem
Apply the complete data science process
Present findings and insights

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Fachgebiete

Conference Talks