Page 6 - 1102 - Data Science Workbook - Full Version - Ind

INDEX
» Outlier Treatment

» Winsorization

Ingredients of AI
» Alpha Trimmed

- Artificial Intelligence
» Missing Values

- Data Science
» Imputation

- Data Mining
» Transformation

- Machine Learning

» Normalization/Standardization
- Deep Learning
» Dummy Variables

- Reinforcement Learning
» Type Casting

» Handling Duplicates

Stages of Analytics

» String Manipulation
CRISP - DM
- CRISP - DM Business Understanding
- CRISP - DM Exploratory Data Analysis
- CRISP - DM Data Collection
» Measures of Central Tendency

» Measures of Dispersion
» Data Types

» Measure of Skewness

» Different Scales of Measurement

» Measure of Kurtosis
» Data Understanding

» Graphical Representations

» Qualitative vs Quantitative
› Histogram
» Structured vs Unstructured

› Box Plot

» Big Data vs Non-Big Data
› Q-Q Plot

» Cross Sectional vs Time Series vs Longitudinal Data
› Bivariate Analysis

» Balanced vs Unbalanced
- Scatter Plot
» Data Collection Sources

- Correlation Coefficient
› Primary Data
» Multivariate Analysis

› Secondary Data

» Data Quality Analysis
» Preliminaries for Data Analysis

» Four Errors to be Avoided During Data Collection

» Probability
» Data Integration

» Base Equation

» Feature Engineering

» Random Variables
» Feature Extraction

» Probability Distributions
» Feature Selection

» Sampling Techniques
» Inferential Statistics

» Non-Probability Sampling

» Probability Sampling
» Sampling Funnel

» Association Rules

› Support
» Supervised Learning

› Confidence
» Supervised Learning has Four Broad Problems to Solve:

› Lift
› Predict a Categorical Class: Classification
» Recommender Systems

› Predict a Numerical Value: Prediction
› Predict User Preference from a Large Pool of Options: Recommendation
› Types of Recommendation Strategies
› Collaborative Filtering
› Predict Relevance of an Entity to a "Query": Retrieval

» Data Mining Unsupervised
› Similarity Measures
› A few of the Algorithms are:
› Disadvantages
› Alternative Approaches
- Clustering
- Dimension Reduction
› Recommendations vs Association Rules
› New Users and New Items
- Network Analysis
» Network Analysis

- Association Rules
› Applications
- Online Recommendation Systems
› Degree Centrality

» Unsupervised Preliminaries
› Distance Calculation
› Closeness Centrality
› Betweenness Centrality
› Linkages

› Eigenvector Centrality
» Clustering / Segmentation
› K-Means Clustering
› Edge / Link Properties
› Cluster Coefficient
› Disadvantages of K-Means
» Text Mining

› K-Means++ Clustering
› K-Medians Clustering
› Examples of Sources
› K-Medoids
› Pre-Process the Data
› Document Term Matrix / Term Document Matrix
› Partitioning Around Medoids (PAM)
› Word Cloud
› CLARA

› Natural Language Processing (NLP)
» Hierarchical Clustering
› Disadvantages of Hierarchical Clustering
› Natural Language Understanding (NLU)

› Natural Language Generation (NLG)
» Density Based Clustering: DBSCAN

» OPTICS
› Parts of Speech Tagging (Pos)
» Grid-Based Clustering Methods

› Named Entity Recognition (NER)

» Three Broad Categories of Measurement in Clustering
› Topic Modelling
» Most Common Measures
- LSA / LSI

» Clustering Assessment Methods
- LDA

» Finding K Value
- Text Summarization

» Data Mining Supervised Learning
» Mathematical Foundations

» Dimension Reduction

» Machine Learning Primer
› Key Challenges
› PCA
› SVD
› LDA
» Multi-Layers Perceptron (MLP) / Artificial Neural Network (ANN)

» Model Evaluation Techniques
- CRISP - DM Model Building Using Data Mining - CRISP - DM Data Cleansing / Data Preparation
› Errors › Non-Linear Patterns
› Confusion Matrix - Integration Function
› Cross Table - Activation Function
› ROC Curve › Regularization Techniques Used for Overfitting
» K-Nearest Neighbor - Error-Change Criterion
› Choosing K Value - Weight-Change Criterion
› Pros and Cons › Dropout
» Naive Bayes Algorithm › Drop Connect
» Decision Tree › Noise
› Three Types of Nodes › Batch Normalization
› Greedy Algorithm › Shuffling Inputs
› Information Theory 101 › Weight Initialization Techniques
› Entropy » Forecasting
› Pros and Cons of Decision Tree › Time Series vs Cross Sectional Data
» Scatter Diagram › EDA - Components of Time Series
» Correlation Analysis - Systematic Part
» Linear Regression Level
º
Ordinary Least Squares Trend
º
› Model Assumptions Seasonality
º
» Logistic Regression - Non-Systematic Part
» Support Vector Machine Noise/Random
º
› Hyperplane › Data Partition
› Non-Linear Spaces › Forecast Model
› Kernel Tricks - Model-Driven Techniques
› Kernel Functions - Data-Driven Techniques
» Deep Learning Primer › Smoothing Techniques
› Image Recognition - Moving Average
› Speech Data - Exponential Smoothing
› Text Data › De-Trending and De-Seasoning
› Shallow Machine Learning Models - Regression
» Perceptron Algorithm - Differencing
› Biological Neuron - Moving Average
› Simple Neural Network Components
› Perceptron Algorithm
› Learning Rate
› Gradient Primer
› Gradient Descent Algorithms Variants
› Empirically Determined Components