Data Science

Course content

01 Introduction to Data Science 3 lectures

What is Data Science?

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History of Data Science

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Role of a Data Scientist

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02 Data Collection and Cleaning 3 lectures

Types of Data

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Data Collection Methods

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Data Cleaning Techniques

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03 Exploratory Data Analysis (EDA) 3 lectures

Importance of EDA

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Data Visualization Techniques

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Statistical Summary

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04 Statistical Foundations 3 lectures

Descriptive Statistics

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Inferential Statistics

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Probability Distributions

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05 Data Visualization 3 lectures

Principles of Visualization

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Tools for Visualization

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Creating Dashboards

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06 Machine Learning Basics 3 lectures

Introduction to Machine Learning

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Types of Machine Learning

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Common Algorithms

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07 Supervised Learning 3 lectures

Regression Techniques

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Classification Techniques

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Model Evaluation Metrics

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08 Unsupervised Learning 3 lectures

Clustering Methods

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Dimensionality Reduction

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Association Rules

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09 Deep Learning 3 lectures

Introduction to Neural Networks

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Convolutional Neural Networks

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Recurrent Neural Networks

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10 Big Data Technologies 3 lectures

Introduction to Big Data

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Hadoop Ecosystem

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Apache Spark

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11 Deployment of Data Science Models 3 lectures

Model Deployment Strategies

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APIs in Data Science

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Monitoring and Maintenance

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12 Ethics in Data Science 3 lectures

Understanding Data Privacy

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Bias and Fairness in Models

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Responsible AI Practices

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13 Capstone Project 3 lectures

Project Planning

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Data Analysis and Modeling

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Presentation and Documentation

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14 Testing AI Text 1 lecture

Testing AI Text section

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Instructors