Data Science Training

Scatter diagram

• 1. Correlation analysis
  2. Correlation coefficient
• Ordinary least squares
• Principles of regression
• Simple Linear Regression
• Exponential Regression, Logarithmic Regression, Quadratic or Polynomial Regression
• Confidence Interval versus Prediction Interval
• Heteroscedasticity / Equal Variance

LINE assumption

• 1. Linearity
  2. Independence
  3. Normality
  4. Equal Variance / Homoscedasticity
• Collinearity (Variance Inflation Factor)
• Multiple Linear Regression
• Model Quality metrics
• Deletion Diagnostics
• Understanding Overfitting (Variance) vs. Underfitting (Bias)
• Generalization error and Regularization techniques
• Different Error functions or Loss functions or Cost functions
• Lasso Regression
• Ridge Regression
• Principles of Logistic regression
• Types of Logistic regression
• Assumption & Steps in Logistic regression
• Analysis of Simple logistic regression results
• Multiple Logistic regression
• Confusion matrix
  1. False Positive, False Negative
  2. True Positive, True Negative
  3. Sensitivity, Recall, Specificity, F1
• Receiver operating characteristics curve (ROC curve)
• Precision Recall (P-R) curve
• Lift charts and Gain charts
• Logit and Log-Likelihood
• Category Baselining
• Modeling Nominal categorical data
• Handling Ordinal Categorical Data
• Interpreting the results of coefficient values
• Poisson Regression
• Poisson Regression with Offset
• Negative Binomial Regression
• Treatment of data with Excessive Zeros
  1. Zero-inflated Poisson
  2. Zero-inflated Negative Binomial
  3. Hurdle Model
• Hierarchical • Supervised vs Unsupervised learning • Data Mining Process • Hierarchical Clustering / Agglomerative Clustering • Dendrogram • Measure of distance

Numeric

Euclidean, Manhattan, Mahalanobis

Categorical

Binary Euclidean

1. Simple Matching Coefficient
2. Jaquard’s Coefficient

Mixed

• Gower’s General Dissimilarity Coefficient

Types of Linkages

1. Single Linkage / Nearest Neighbour
2. Complete Linkage / Farthest Neighbour
3. Average Linkage
4. Centroid Linkage

K-Means Clustering

Measurement metrics of clustering

1. Within the Sum of Squares
2. Between the Sum of Squares
3. Total Sum of Squares

Choosing the ideal K value using Scree Plot / Elbow Curve

Other Clustering Techniques

1. K-Medians
2. K-Medoids
3. K-Modes
4. Clustering Large Application (CLARA)
5. Partitioning Around Medoids (PAM)
6. Density-based spatial clustering of applications with noise (DBSCAN)

• Why Dimension Reduction
• Advantages of PCA
• Calculation of PCA weights
• 2D Visualization using Principal components
• Basics of Matrix Algebra
• Factor Analysis
• What is Market Basket / Affinity Analysis

Measure of Association

• 1. Support
  2. Confidence
  3. Lift Ratio
• Apriori Algorithm
• Sequential Pattern Mining
• User-based Collaborative Filtering
• A measure of distance/similarity between users
• Driver for Recommendation
• Computation Reduction Techniques
• Search based methods/Item to Item Collaborative Filtering
• SVD in recommendation
• The vulnerability of recommendation systems

• Definition of a network (the LinkedIn analogy)

The measure of Node strength in a Network

1. Degree centrality
2. Closeness centrality
3. Eigenvector centrality
4. Adjacency matrix
5. Betweenness centrality
6. Cluster coefficient

Introduction to Google page ranking

• Deciding the K value
• Thumb rule in choosing the K value
• Building a KNN model by splitting the data
• Checking for Underfitting and Overfitting in KNN
• Generalization and Regulation Techniques to avoid overfitting in KNN
• Elements of classification tree – Root node, Child Node, Leaf Node, etc.
• Greedy algorithm
• Measure of Entropy
• Attribute selection using Information gain
• Ensemble techniques – Stacking, Boosting and Bagging
• Decision Tree C5.0 and understanding various arguments
• Checking for Underfitting and Overfitting in Decision Tree
• Generalization and Regulation Techniques to avoid overfitting in Decision Tree
• Random Forest and understanding various arguments
• Checking for Underfitting and Overfitting in Random Forest
• Generalization and Regulation Techniques to avoid overfitting in Random Forest
• Overfitting
• Underfitting
• Pruning
• Boosting
• Bagging or Bootstrap aggregating
• AdaBoost / Adaptive Boosting Algorithm
• Checking for Underfitting and Overfitting in AdaBoost
• Generalization and Regulation Techniques to avoid overfitting in AdaBoost
• Gradient Boosting Algorithm<
• Checking for Underfitting and Overfitting in Gradient Boosting
• Generalization and Regulation Techniques to avoid overfitting in Gradient Boosting
• Extreme Gradient Boosting (XGB) Algorithm
• Checking for Underfitting and Overfitting in XGB
• Generalization and Regulation Techniques to avoid overfitting in XGB

• Sources of data
• Bag of words
• Pre-processing, corpus Document Term Matrix (DTM) & TDM
• Word Clouds

Corpus level word clouds

1. Sentiment Analysis
2. Positive Word clouds
3. Negative word clouds
4. Unigram, Bigram, Trigram

• Semantic network
• Clustering
• Extract user reviews of the product/services from Amazon, Snapdeal and trip advisor
• Install Libraries from Shell
• Extraction and text analytics in Python
• LDA / Latent Dirichlet Allocation
• Topic Modelling
• Sentiment Extraction
• Lexicons & Emotion Mining
• Probability – Recap
• Bayes Rule
• Naïve Bayes Classifier
• Text Classification using Naive Bayes
• Checking for Underfitting and Overfitting in Naive Bayes
• Generalization and Regulation Techniques to avoid overfitting in Naive Bayes
• Neurons of a Biological Brain
• Artificial Neuron
• Perceptron
• Perceptron Algorithm
• Use case to classify a linearly separable data
• Multilayer Perceptron to handle non-linear data
• Integration functions
• Activation functions
• Weights
• Bias
• Learning Rate (eta) – Shrinking Learning Rate, Decay Parameters
• Error functions – Entropy, Binary Cross Entropy, Categorical Cross Entropy, KL Divergence, etc.
• Artificial Neural networks
• ANN structure
• Gradient Descent Algorithms – Batch GD, SGD, Mini-batch SGD
• Backward propagation
• Network Topology
• Principles of Gradient descent (Manual Calculation)
• Momentum, Nesterov Momentum
• Optimization methods: Adam, Adagrad, Adadelta, RMSProp
• CNN – Convolutional Neural Network
• RNN – Recurrent Neural Network

1. Classification Hyperplanes
2. Best fit “boundary”
3. Kernel Tricks – Linear, RBF, etc.
4. Non-Linear Kernel Tricks
5. Avoiding overfitting in SVM
6. Regularization techniques in SVM

• Examples of Survival Analysis
• Time to event
• Censoring
• Survival, Hazard, Cumulative Hazard Functions
• Introduction to Parametric and non-parametric functions
• Introduction to time series data
• Steps to forecasting
• Components to time series data
• Scatter plot and Time Plot
• Lag Plot
• ACF – Auto-Correlation Function / Correlogram
• Visualization principles
• Naïve forecast methods
• Errors in the forecast and it metrics – ME, MAD, MSE, RMSE, MPE, MAPE
• Model-Based approaches

1. Linear Model
2. Exponential Model
3. Quadratic Model
4. Additive Seasonality
5. Multiplicative Seasonality

• Model-Based approaches Continued
• AR (Auto-Regressive) model for errors
• Random walk
• ARMA (Auto-Regressive Moving Average), Order p and q
• ARIMA (Auto-Regressive Integrated Moving Average), Order p, d, and q
• A data-driven approach to forecasting

Smoothing techniques

1. Moving Average
2. Exponential Smoothing
3. Holt’s / Double Exponential Smoothing
4. Winters / Holt-Winters

• De-seasoning and de-trending
• Econometric Models
• Forecasting using Python
• Forecasting using R