Data Science

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What is Data Science

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. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results. (Definition by Amazon)

 

 Under the under big umbrella "Science of Intelligence", Data Science usually refers to Data Mining and Machine Learning

 

What is the difference between Machine Learning and Data Mining

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy.

 

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Introduction to Data Science

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Data Mining

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Machine Learning

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Data Visualization

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Useful Math Skills

Mathematics for Machine Learning Local download

Statistical Thinking for the 21st Century github

 

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Data Visualization

Python Data Visualization Cookbook

 

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Ethical problems in data science

Before we start

Introduction

 

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Data Collection   Data Preprocessing Link   Data Cleaning Data Integration Data Reduction			Intro to python Python Crawler Intro to R
Data Exploration / Understand Data   Data Summaries Link   Data Central Tendency Standard Deviation / Variance    Data Plots / Visualized Summarization Link Basic Plots Boxplot Histogram Quantile plots Heatmap / Mesh   Data Visualization Link Simpson’s Paradox			WordClouds
Computer Science	Math/Statistics	Visualization
Optimization Problems   Classic Optimization Link   Brute Force Greedy Algorithm Dynamic Programming   Stochastic Algorithms Link GA ES DE PSO ACO   Learning Types Link Supervised Unsupervised Semi-supervised Data Spliting Overfitting   Classification Link Decision trees Logistic regression K-Nearest Neighbours Naive Bayes SVM Artificial neural networks   Clustering Link Hierarchical Clustering K-means Clustering Mean Shift Clustering DBSCAN Agglomerative Clustering Affinity Propagation Soft Clustering   Network Analysis Link Graph Mining Random Walk PageRank Web mining Infor-network analysis   Trend Analysis Link Time-series Prediction Deviation Analysis Sequential PatternMining Periodicity Analysis   Biological Data Analysis Link Motif Finding Bio Sequence Analysis Bio Network Analysis Pathway Analysis	Basic Tools   Measurements Link   Distance Measure Similarity/Corrolation   Statistical Analysis Link Z-test t-test U-test Statistical Dependence p-value Confidence Interval ANOVA table   Data Preprocessing Link Normalization Data Sampling Data Cleaning    Evaluations Link Regression Problems Classification Problems Clustering Problems        Regression Link Simple Linear Regression Polynomial Regression Curve Fitting Logistic Regression     Statistical Modeling Link Hidden Markov Model Bayesian Network   Anomaly Analysis Link Anomaly detection Anomaly/Outlier analysis   Find Features Link Feature Selection Dimension Reduction Feature Abstraction Similarity-based Analysis   Pattern Discovery Link Pattern evaluation Pattern selection Pattern interpretation Pattern visualization	Python Data Visualization Cookbook Result Presentation / Result Visualization Popular Tools	Pyplot / Plotly KNIME Tableau SeaTable Power BI Dialogflow

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