Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.
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Introduction to Data Science
Overview of Data Science
Data Science Process
Tools and Technologies
Data Science Applications
Data Science Ethics
Data Collection and Preprocessing
Data Collection Techniques
Data Preprocessing Techniques
Data Cleaning and Transformation
Data Integration and Aggregation
Data Wrangling
Exploratory Data Analysis
Descriptive Statistics
Data Visualization
Correlation and Regression
Hypothesis Testing
Dimensionality Reduction
Machine Learning Fundamentals
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
Ensemble Learning
Regression Analysis
Linear Regression
Multiple Regression
Logistic Regression
Ridge Regression
Lasso Regression
Classification and Clustering
Decision Trees
Random Forest
Naive Bayes
K-Nearest Neighbors
K-Means Clustering
Natural Language Processing
Text Preprocessing
Text Classification
Named Entity Recognition
Sentiment Analysis
Topic Modeling
Deep Learning
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Autoencoders
Generative Adversarial Networks
Big Data Analytics
Introduction to Big Data
Hadoop and Spark
MapReduce
Hive and Pig
Apache Cassandra
Data Science Tools and Technologies
Python Programming
R Programming
Data Science Libraries
Data Science Platforms
Data Science in Cloud
Data Science Project
Project Planning
Data Collection and Preprocessing
Data Analysis and Visualization
Machine Learning Modeling
Project Deployment and Presentation
Data Science Ethics and Privacy
Data Privacy
Data Ownership
Data Governance
Data Security
Ethical Considerations
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