DATA SCIENCE

Description

Introduction to BIG Data Science/Data Analytics

•  What background is required? What is Data Science?
• Why Data Science?
• BIG Data Science/Analytics trend What is Machine Learning?
• Data Science Life Cycle

Tools for Data Science/Analytics

• Anaconda Distribution package Open Source: Python/R
• Visualization tools: Matplotlib, Seaborn, introduction of Tableau

Data Analytics Problems/Use-cases

• From Kaggle competitions
• Types of Data: Structured, Unstructured (Image, Text…..) Predictive Analytics Problems: Classification, Regression, Recommenders
• Descriptive Analytics Problems: Clustering, Market Basket Analysis, PCA
• Business Verticals: Retail, Real Estate, Banking, Financial, Social, Web, Medical, Scientific, Logistics,

Visualization tools:

• Matplotlib, Seaborn,
• Introduction of Tableau

Statistics for Data Scientist

• Descriptive Statistics for single variables Mean, Median, Mode, Quartile, Percentile Interquartile Range
• Standard Deviation Variance
• Descriptive Statistics for two variables Z-Score
• Co-variance/ Co-relation
• Chi-squared Analysis / Hypothesis Testing
• Limits Derivatives
• Partial Derivatives Gradients
• Significance of Gradients

Probability for Data Scientist

• Basic Probability Conditional Probability
• Properties of Random Variables Expectations
• Variance
• Entropy and cross-entropy Covariance and correlation
• Estimating probability of Random variable Understanding standard random processes

Data Distributions

• Normal Distribution Binomial Distribution Multinomial Distribution Bernoulli Distribution
• Probability, Prior probability, Posterior probability Bayes Theorem
• Naive Bayes
• Naive Bayes Algorithm Normal Distribution

Mastering Python/R Language

• How to install python (Anaconda) How to install sciKit Learn (Anaconda) How to work with Jupyter Notebook How to work with Spyder IDE
• Strings Lists Tuples Sets
• Dictionaries Control Flows Functions
• Formal/Positional/Keyword arguments
• Predefined functions (range, len, enumerates etc…) Data Frames
• Packages required for data Science in R/Python Lab/Coding

Introduction to NumPy  

• One-dimensional Array Two-dimensional Array
• Pr-defined functions (arrange, reshape, zeros, ones, empty) Basic Matrix operations
• Scalar addition, subtraction, multiplication, division
• Matrix addition, subtraction, multiplication, division and transpose Slicing
• Indexing Looping
• Shape Manipulation Stacking

Introduction to Pandas

• Series DataFrame df.GroupBy df.crosstab df.apply df.map

Decision Trees

• What are Decision Trees? Gini, Entropy criterions Decision trees in Classification Decision trees in Regression Ensembles
• Random Forest
• Boosting (Ada, Gradient, Extreme Gradient) SVM
• Ensembles
• Understand what is overfitting and under fitting model Visualize the overfitting and under fitting model
• How do you handle overfitting?

Data Preparation Techniques

• Structured Data Preparation Data Type Conversion
• Category to Numeric Conversion Numeric to Category Conversion Data Normalization: 0-1, Z-Score
• Handling Skew Data: Box-Cox Transformation Handling Missing Data

Re-sampling Techniques

• K-fold
• Repeated Hold-out Data Bootstrap aggregation sampling

Exploratory Data Analysis (EDA) 

• Statistical Data Analysis
• Data Visualization (Matplotlib, Seaboarn) Exploring Individual Features
• Exploring Bi-Feature Relationships Exploring Multi-feature Relationships Feature/Dimension Reduction: PCA Intuition behind PCA
• Covariance & Correlation
• Relating PCA to Covariance/Correlation Intuition to math
• Applications of PCA: Dimensionality Reduction

Feature Engineering (FE)

• Combine Features Split Features

Data Visualization 

• Bar Chart Histogram
• Box whisker plot Line plot
• Scatter Plot Heat Map

Tree Based Algorithms

• Gini Index Entropy Information Gain Tree Pruning

Classification (Supervised Learning) 

• What is Classification?
• Finding Patterns/Fixed Patterns Problems with Fixed Patterns
• Machine learning approach over fixed pattern approach Decision Tree based classification
• Ensemble Based Classification Logistic Regression (SGD Classifier) Accuracy measurements
• Confusion Matrix ROC Curve
• AUC Score
• Multi-class Classification Softmax Regression Classifier Multi-label Classification
• Multi-output Classification

Ensemble models

• Random Forest Bagging Boosting
• Adaptive Boosting Gradient Boosting
• Extreme Gradient Boosting Heterogeneous Ensemble Models Stacking / Voting

Regression (Supervised Learning)

• What is regression?
• Regression example in business verticals Solution strategies for Regression
• Linear Regression Explanation of statistics Evaluation metrics
• Root Mean Squeare(RMSE) R-Squre,
• Adj R-Squre
• Feature selection methods Linear regression

Multiple/Polynomial Regression (scikit learn)                                         

• Multiple Linear Regressions (SGD Regressor)
• Gradient Descent (Calculus way of solving linear equation) Feature Scaling (Min-Max vs Mean Normalization)
• Feature Transformation Polynomial Regression
• Matrix addition, subtraction, multiplication and transpose Optimization theory for data scientist

Optimisation Theory (Gradient Descent Algorithm)

• Modelling ML problems with optimization requirements Solving unconstrained optimization problems
• Solving optimization problems with linear constraints Gradient descent ideas
• Gradient descent Batch gradient descent
• Stochastic gradient descent

Model Evaluation and Error Analysis

• Train/Validation/Test split K-Fold Cross Validation
• The Problem of Over-fitting (Bias-Variance tread-off) Learning Curve
• Regularization (Ridge, Lasso and Elastic-Net) Hyper Parameter Tuning (GridSearchCV)

Recommendation Problem

• What is Recommendation System? Top-N Recommender
• Rating Prediction
• Content based Recommenders
• Limitations of Content based recommenders Machine Learning Approaches for Recommenders User-User KNN model, Item-Item KNN model Factorization or latent factor model
• Hybrid Recommenders
• Evaluation Metrics for Recommendation Algorithms Top-N Recommnder: Accuracy, Error Rate
• Rating Prediction: RMSE

 

Clustering (Unsupervised Learning)

• Finding pattern and Fixed Pattern Approach Limitations of Fixed Pattern Approach
• Machine Learning Approaches for Clustering Iterative based K-Means Approaches
• Density based DB-SCAN Approach Evaluation Metrics for Clustering
• Cohesion, Coupling Metrics Correlation Metric

Support Vector Machine (SVM)

• SVM Classifier (Soft/Hard – Margin) Linear SVM
• Non-Linear SVM Kernel SVM SVM Regression

PCA (Unsupervised Learning)

• Dimensionality Reduction
• Choosing Number of Dimensions or Principal Components Incremental PCA
• Kernel PCA
• When to apply PCA? Eigen vectors
• Eigen values

Model Deployment    

• Pickle (pkl file)
• Model load from pkl file and prediction

Association Rules     

• A priori Algorithm
• Collaborative Filtering (User-Item based) Collaborative Filtering (User-User based)
• Collaborative Filtering (Item-Item based)

Deep Learning:

• Introduction to Deep Learning Tensorflow
• Keras
• Setting up new environment for Deep Learning Perceptron model for classification and regression Perceptron Learning
• Limitations of Perceptron model
• Multi-layer FF NN model for classification and regression ML-FF-NN Learning with backpropagation
• Applying ML-FF-NN and parameter tuning Pros and Cons of the Model

Image classification

• Image Data Preparation Converting to gray scale Pixel Value Normalization
• Building Pixel Intensity Matrix Neural Networks
• Fully connected Neural Networks Feed Forward Neural Networks Convolution Neural Networks Filters, Max Pooling
• Functional APIs

Text analytics:

• Bag of words Glove Dictionary
• Text Data Preparation Normalizing Text Stop word Removal Whitespace RemovalStemming
• Building Document Term Matrix NLP (Natural Language Processing)