i2tutorials provides the best Data Science training helps you to excel your skill on  of data acquisition, project life cycle, deploying machine learning and statistical methods

You will be working on real time projects that have high relevance in the corporate world, step by step assignments and curriculum designed by industry experts. Upon completion of the training course you can apply for some of the best jobs in top MNCs around the world at top salaries.

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Statistics (Mathematics for Data Science)                                                                   Module – 1 (25 hrs)

Understanding the Data

• Data, Data Types
• Meaning of variables
• Central Tendency
• Measures of Dispersion
• Measures of Variability
• Measures of Shape
• Data Distribution
• Correlation, Covariance
• Practical Examples

Probability Distributions

• Mean, Expected value
• Binomial Random Variable
• Normal Distribution
• Poisson Random Variable
• Continuous Random Variable
• Discrete Random Variable
• Practical Examples

Sampling Distributions

• Central Limit Theorem
• Sampling Distributions for Sample Proportion, p-hat
• Sampling Distributions for Sample Mean, x-bar
• Z- Scores
• Practical Examples

Hypothesis Testing

• Type I and Type II Errors
• Decision Making
• Power
• Testing for mean, variance, proportion
• Practical Examples

Association between Categorical Variables

• Contingency Tables
• Independent and Dependent
• Pearson’s Chi-Square Test
• Misuses of Chi-Squared Test
• Measures of Association
• Practical Examples

ANOVA Analysis

• Analysis of Variance & Co-Variance
• ANOVA Assumptions & Comparisons
• F-Tests
• Practical Examples

Matrices

• One – Dimensional
• Multi Dimensional
• Arithmetic Operations
• Examples

Vectors

• Understanding Vectors
• Scalar Vs Vector
• Arithmetic Operations
• Examples

Machine Learning                                                                                      Module – 2 (40 hrs)

Supervised Learning

• An Approach to Prediction
• Least Squares and Nearest Neighbors
• Statistical Decision
• Regression Models

Linear Methods for Regression

• The Gauss–Markov Theorem
• Multiple Regression
• Forward- and Backward-Stepwise Selection
• Ridge Regression
• Lasso Regression
• Example using R / Python

Linear Methods for Classification

• Linear Regression of an Indicator Matrix
• Linear Discriminant Analysis
• Logistic Regression
• Rosenblatt’s Perceptron Learning Algorithm
• Example using R / Python

Kernel Smoothing Methods

• One-Dimensional Kernel Smoothers
• Local Linear Regression
• Local Polynomial Regression
• Mixture Models for Density Estimation and Classification
• Example using R / Python

Model Selection

• Bias, Variance and Model Complexity
• Optimism of the Training Error Rate
• Vapnik–Chervonenkis Dimension
• Cross-Validation

Model Inference & Averaging

• Bootstrap and Maximum Likelihood Methods
• Relationship Between the Bootstrap and Bayesian Inference
• The EM Algorithm
• Bagging
• Example using R / Python

Tree-Based Methods

• Regression Trees
• Classification Trees
• Bump Hunting
• MARS: Multivariate Adaptive Regression Splines
• Example using R / Python

Boosting

• Steepest Descent
• Gradient Boosting
• Regularization
• Interpretation
• Example using R / Python

Neural Networks

• Fitting Neural Networks
• Over fitting
• Hidden Units
• Multiple Minima
• Single, Multi-Layer Perceptron
• Example using R / Python

Support Vector Machines ( SVM )

• Support Vector Classifier
• Generalizing Linear Discriminant Analysis
• Flexible Discriminant Analysis
• Penalized Discriminant Analysis
• Example using R / Python

K-Nearest-Neighbor Classifiers

• Prototype Methods
• K-means Clustering
• Vector Quantization
• Gaussian Mixtures
• k-nearest Neighbors
• Example using R / Python

Unsupervised Learning

• The Apriori Algorithm
• Unsupervised as Supervised Learning
• Generalized Association Rules
• K-means Cluster Analysis
• Hierarchical Clustering
• Principal Components, Curves and Surfaces
• Non-Linear Dimension Reduction
• The Google Page Rank Algorithm
• Example using R / Python

Random Forests

• Variable Importance
• Random Forests and Over fitting
• Bias
• Adaptive Nearest Neighbors
• Example using R / Python

Python              Module -3 (20 hrs)              

Introduction to Python

• Installation of Python framework and packages: Anaconda & pip
• Working with Jupyter notebooks
• Creating Python variables
• Numeric , strings
• logical operations
• Lists
• Dictionaries
• Tuples
• sets
• Practice assignment

Iterative Operations & Functions in Python

• Writing for loops in Python
• While loops and conditional blocks
• List/Dictionary comprehensions with loops
• Writing your own functions in Python
• Writing your own classes and functions
• Practice assignment

Data Handling in Python using Packages

• Numpy
• Pandas
• SymPy
• SciPy
• Matplotlib

Data Visualization in Python

• Need for data summary & visualization
• Summarising numeric data in pandas
• Summarising categorical data
• Group wise summary of mixed data
• Basics of visualisation with ggplot & Seaborn
• Inferential visualisation with Seaborn
• Visual summary of different data combinations
• Practice assignment

Data preparation using Python

• Needs & methods of data preparation
• Handling missing values
• Outlier treatment
• Transforming variables
• Data processing
• Practice

R – Programming           Module -4 (20 hrs)

Fundamentals of R

• Installation of R & R Studio
• Getting started with R
• Basic and Advanced Data types in R
• Variable operators in R
• Working with R data frames
• Reading and writing data files to R
• R functions and loops
• Special utility functions
• Merging and sorting data
• Practice assignment

Univariate statistics in R

• Summarizing data, measures of central tendency
• Measures of data variability & distributions
• Using R language to summarize data
• Practice assignment

Data visualization in R

• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
• Creating Graphs ( Bar/pie/line chart/histogram/boxplot/scatter/density etc)
• R Packages for Exploratory Data Analysis (dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
• R Packages for Graphical Analysis (base, ggplot, lattice,etc)

Hypothesis testing and ANOVA in R

• Introducing statistical inference
• Estimators and confidence intervals
• Central Limit theorem
• Parametric and non-parametric statistical tests
• Analysis of variance (ANOVA)

Data preparation using R

• Needs & methods of data preparation
• Handling missing values
• Outlier treatment
• Transforming variables
• Data processing with dplyr package
• Practice

Artificial Intelligence                                                                                         Module – 5 (25 hrs)

Introduction to Deep Learning

 Deep Learning: A revolution in Artificial Intelligence

 Limitations of Machine Learning

 Deep Learning vs Machine learning

 Examples of Deep Learning

 Implementations where Deep Learning is applicable

Glance of Machine Learning Algorithms

 Regression

 Classification

 Clustering

 Reinforcement Learning

 Underfitting and Overfitting

 Optimization

Understanding Fundamentals of Neural Networks with Tensorflow

 How Deep Learning Works?

 Activation Functions

 Illustrate Perceptron

 Training a Perceptron

 Important Parameters of Perceptron

Tensor Flow

 What is TensorFlow?

 Use of TensorFlow in Deep Learning

 Working of TensorFlow

 How to install Tensorflow

 HelloWorld with TensorFlow

 Tensorflow code-basics

 Graph Visualization

 Constants, Placeholders, Variables

 Creating a Model

 Running a Machine learning algorithms on TensorFlow

Deep dive into Neural Networks with Tensorflow

 Understand limitations of A Single Perceptron

 Neural Networks in Detail

 Multi-Layer Perceptron

 Backpropagation – Learning Algorithm

 Understand Backpropagation – Using Neural Network Example

 MLP Digit-Classifier using TensorFlow

 TensorBoard

Convolutional Neural Networks (CNN)

 Define CNNs

 Discuss the Applications of CNN

 Explain the Architecture of a CNN

 List Convolution and Pooling Layers in CNN

 Illustrate CNN

Transfer Learning of CNNs

 Introduction to CNNs

 CNNs Application

 Architecture of a CNN

 Convolution and Pooling layers in a CNN

 Understanding and Visualizing a CNN

 Transfer Learning and Fine-tuning Convolutional Neural Networks

Recurrent Neural Networks (RNN)

 Intro to RNN Model

 Applications of RNN

 Modelling sequences

 Training RNNs with Backpropagation

 Long Short-Term memory (LSTM)

 Recursive Neural Tensor Network Theory

 Recurrent Neural Network Model

Hands-On Project

Machine Learning in Cloud   &    Big Data Analytics   Module -6 (20 hrs)

Machine Learning using Azure

• What is Microsoft Azure?
• Azure Machine Learning
• Diving into Azure Machine Learning
• Training a Model
• Deploy a Model
• Practical Example

Machine Learning using AWS

• What is AWS?
• AWS Machine Learning
• Diving into AWS Machine Learning
• Training A Model
• Deploy a Model
• Practical Example

Working with Mode Analytics

• What is Mode Analytics?
• Data Science on Web Model
• Using SQL in mode analytics
• R note book in mode analytics
• Python note book in Mode analytics
• Working on Practical Example

Introduction to Big Data analytics

• Hadoop – HDFS
• Mapreduce
• Hive
• Hbase
• Spark
• Spark SQL
• Spark Mlib

Machine Learning using Spark

• Introduction
• Data sets
• Data frames
• Machine Learning using spark
• SparkR
• PySpark
• Practical Example

It is not that you are require to be a ph.D. or Master Degree holder to learn the Data Science.

To learn the Data Science you must be with atleast a Bachelor’s Degree in computers or statistics or you must be a person with minimum programming background who has interest to work Python and R programming languages.

  Analytics Professionals
  Software Developers
  Testing and Data Base Admins
  Network Administrators
  Projects Managers
  Graduates and Post-Graduates
  Also, Job Seekers

Project#1: Movie Dataset

Industry: Entertainment Industry

Description: The goal of this Use-Case is to explore the movie dataset, given the parameters like: “duration”, “movie title”, “gross collection”, “budget”, “title year”, etc.  

• Know top ten movies with the highest profits.
• Know top rated movies in the list and average IMDB score.
• Plot a graphical representation to show number of movies released each year.
• Group the movies into clusters based on the Facebook likes.
• Group the directors based on movie collection and budget.