## Course Overview

Advanced Analytics helps in finding patterns in big data sets, getting insights, and making predictions based on those insights. As more and more companies have large datasets from various sources at their disposal, they are opting for advanced analytics to get value out of them. Advanced Analytics helps in extracting error-free, data-based insights on consumer behavior and predilections by discerning patterns and correlations in those enormous data sets. These insights help companies to understand their customers better and make strategies that have very positive business impacts.

Typically, advanced analytics involves using mathematical, statistical, as well as machine-driven techniques and tools to process and find patterns in enormous data sets. It also needs very skilled data professionals to interpret those patterns and correlations and make predictions or forecasts.

As such there is a huge demand for skilled data professionals all over the world in all kinds of industries. However, it is very difficult to find talent with the right skill-sets as per market requirements.

Our comprehensive course in Advanced Analytics covers all the skills and tools (Take a look at the course detail given below) needed in the industry. It will make you confident in Deep Learning and Advanced Analytics tools and techniques at the conceptual as well as the practical level. This course is taught by faculty (IIT, ISB alumni) who are experienced Data Science professionals in the industry with years of experience in providing AI, Deep Learning solutions.

Not only that, there are options for Live projects and placements too. Don’t think. Join the best Advanced Analytics training course in Kolkata now!

## Things You Will Learn

Boosting & Bagging

Intro

What is Bagging and Boosting

Comparing the results of Boosting and a single model

Parameters in Boosting

Gradient Descent

Intro

Concepts of Gradient Descent

Cost function

Learning rate

Extreme Gradient Boosting (XGBM)

Intro

Concepts of XGBM

Parameters in XGBM

Implementation of XGBM

C5.0

Intro

Concepts of C5.0

Entropy

Information Gain

Forward Pruning

Backward Pruning

Implementation of C5.0

Bias & Variance

- Regularization

- Deep feed forward networks or Multilayer Perceptrons
Intro

Neurons

Neuron Weights

Activation Function

Networks of Neurons

Input or Visible Layers

Hidden Units

Output Layer

Architecture Design

Gradient-Based Learning

Performance of Deep Learning Models

Empirically Evaluate Network Configurations

Data Splitting

Use an Automatic Verification Dataset

Use a Manual Verification Dataset

Manual k-Fold Cross-Validation

Advanced Multilayer Perceptron

Image Processing models: Convolutional Networks

Convolutional Layers

Filters

Feature Maps

Pooling Layers

Downsampling

Fully Connected Layers

Sequence Modeling: Recurrent and recursive networks

Long Short-Term Memory (LSTM) Networks

Time Series Prediction with Multilayer Perceptrons

Time Series Prediction with LSTM

- Maths behind Optimization
Introduction to derivatives

Derivatives in optimization – Maxima & Minima

Application of optimization in arriving at Linear Least Squares

Gradient Descent Optimization

Linear Programming

Introduction to Linear programming

Formulating linear programming models

Solving linear programming models

Understand resource allocation problems

Understand cost-benefit analysis problems

Duality & other analysis

Decision variables, constraints & objective function

Duality problems

Sensitivity analysis

Network Analysis

Transportation, Shortest path, Maximal flow problems

Introduction to integer linear programming

Introduction to Non-linear optimization

Introduction to Probability

Review of probability

Conditional Probability

Bayes theorem

Permutations & Combinations

Introduction to Probability Distributions

Bernoulli

Binomial

Geometric

Negative Binomial

Poisson

Uniform Distribution

Triangular

Exponential

Normal

Introduction to Simulation

Basics of simulation

Statistical sampling

The case study on the application of simulation

Bidding

Marketing

Fitting distributions to data

Decision Tree Simulation

Discrete Event Simulation

- Queuing Theory

Introduction to DOE

Introduction of DOE terms

Factor, Level, Treatment, Treatment combination

Blocking, Center points, Repetition, Replication

Main effects, Interaction effects

Types of experiments

Trial & Error

One-Factor-At-A-Time (OFAT)

Full factorial design

Fractional factorial design

Phases of DOE

Screening

Characterization

The 7-step process

Balanced DOE

Calculation of main & interaction effects

Creation of designed experiments

Power & Sample size

Blocking

Defining a custom design

Checking model assumptions

Full factorial results analysis

DOE model reduction

DOE main effect & interaction effect plots

Cube plot, Contour & surface plots

Fractional factorial design

Confounding

Folding

Randomized blocks & Latin square

- Implementation plan

- Introduction to Text Mining & NLP
Factorizing Data

Introduction to topic models

Latent topic modeling

Introduction to parts-of-speech tagging

Perceptual map/bi-plot

Trend tracking – topics across time

Sentence & Word annotations

Named entity annotations

Content Analysis

Lexicons

Emotion Mining – Arcs & emotions

- Use of machine learning in text classification

Introduction to survival analysis

Time-to-event data

Censoring & types of censoring

Survival Analysis Techniques

Single group (Nonparametric methods)

Life Table

Kaplan-Meier

Nelson-Aalen cumulative hazard estimation

Comparison of groups

Log-rank test

Wilcoxon test

Semi-parametric estimation mode

Cox proportional hazard model

Survivor function & Hazard function

Bathtub curve

Comparison of survival curves

Failure time distributions

Weibull

Gompertz

Log-logistic

Accelerated event-time

- Customer lifetime value

Installing & setting up Spark locally

Spark programming in Python

Designing a machine learning system

Obtaining, processing & preparing data with Spark

Building a recommendation engine with Spark

Building a classification model with Spark

Building a regression model with Spark

Building a clustering model with Spark

Dimensionality reduction with Spark

Advanced text processing with Spark

- Real-time machine learning with Spark streaming