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BEST DATA SCIENCE, ML, AI, DIGITAL MARKETING COURSE IN KOLKATA

The Best Advanced Analytics Training Course in Kolkata

  • 60 hours of exhaustive training
  • More than 1500 students trained 
  • Taught by SMEs with Industry experience
  • Hands-On Training, assignments 
  • Record placements!

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

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