Overview
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.
Syllabus
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- 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
Taught by
Tags
On-Demand
