Data Science Course for Beginners
Lecture 1: Introduction to Data Science
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What is data science?
The different components of data science
The importance of data science in today's world
The skills needed to be a data scientist
Lecture 2: Python Programming
● Introduction to Python
● Basic Python syntax
● Working with data in Python
Lecture 3-4: Data Cleaning and Preparation
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Data cleaning basics
Data wrangling techniques
Data preprocessing methods
Handling missing values
Lecture 5: Exploratory Data Analysis
● Introduction to EDA
● Data visualization techniques
● Statistical analysis for EDA
Lecture 6: Machine Learning
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Introduction to machine learning
Supervised learning
Unsupervised learning
Reinforcement learning
Lecture 7: Deep Learning
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Introduction to deep learning
Neural networks
Convolutional neural networks
Recurrent neural networks
Lecture 8-9: Natural Language Processing
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Introduction to natural language processing
Text preprocessing
Machine learning for NLP
Deep learning for NLP
Lecture 10: Data Visualization
● Introduction to data visualization
● Choosing the right visualization for the data
Lecture 11-12: Generative AI
● Introduction to generative AI
● Generative adversarial networks
Lecture 13: Ethics in Data Science
● The ethical challenges of data science
● Fairness, bias, and transparency in data science
● The responsible use of data science
Lecture 14: Capstone Project
● Apply the skills learned in the course to a real-world data science project
● Present the results of the project.