DATA SCIENCE

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Introduction to Data Science 4 Topics

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What is Data Science?

Data Science vs. other fields (e.g. Statistics, Computer Science)

Applications of Data Science

Tools and technologies used in Data Science

Data Collection and Cleaning 4 Topics

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Data sources and types

Data collection techniques (e.g. scraping, APIs)

Data cleaning and preprocessing

Data quality assessment and validation

Exploratory Data Analysis 3 Topics

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Data visualization

Exploratory data analysis techniques (e.g. clustering, dimensionality reduction)

Descriptive statistics

Statistical Inference 4 Topics

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Probability theory and distributions

Hypothesis testing

Confidence intervals

Regression analysis

Machine Learning 3 Topics

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Supervised learning (e.g. regression, classification)

Unsupervised learning (e.g. clustering, dimensionality reduction)

Deep learning (e.g. neural networks, convolutional neural network

Data Management and Storage 1 Topic

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Data management system (ex. database)

Data Visualization and Communication 3 Topics

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Effective data visualization techniques

Communicating insights and results to stakeholders

Presenting data and analysis in a clear and concise manner

Project 3 Topics

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Apply Data Science techniques to a real-world problem

Develop a project from data collection and cleaning to analysis and visualization

Present the results to stakeholders in a clear and concise manner.

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