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“Data science” is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components: Data exploration & analysis. Included here: Pandas; NumPy; SciPy; a helping hand from Python's Standard Library.

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Name		Name	Last commit message	Last commit date
Latest commit History 123 Commits
1 Data-Sciecne-Basics		1 Data-Sciecne-Basics
1.1 NumPy		1.1 NumPy
1.2 Pandas		1.2 Pandas
1.3 Plotting		1.3 Plotting
2 Linear Models for Classification & Regression		2 Linear Models for Classification & Regression
2.1 Pre Processing		2.1 Pre Processing
2.2 Decision Tree		2.2 Decision Tree
2.3 Naive Bayes		2.3 Naive Bayes
2.4 Composite Estimators using Pipelines & FeatureUnions		2.4 Composite Estimators using Pipelines & FeatureUnions
2.5 Model Selection & Evaluation		2.5 Model Selection & Evaluation
2.6 Feature Selection Techniques		2.6 Feature Selection Techniques
2.7 Nearest Neighbors		2.7 Nearest Neighbors
3.1 Anomaly Detection		3.1 Anomaly Detection
3.2 Support Vector Machines		3.2 Support Vector Machines
3.3 Dealing with Imballanced classes		3.3 Dealing with Imballanced classes
3.4 Ensemble Methods		3.4 Ensemble Methods
3.5 Association Rules		3.5 Association Rules
Case Study-Ozone Level Detection		Case Study-Ozone Level Detection
Data		Data
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md

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Python-with-Data-Science

Data Science: Data Science is a detailed study of the flow of information from the colossal amounts of data present in an organization's repository. It involves obtaining meaningful insights from raw and unstructured data which is processed through analytical, programming, and business skills.