LEARN DATA SCIENCE

Accelerate your career prospects with iteanz Data Science Training


Validate your skills as an Data Science expert

Enhance your skill set and boost your hirability through innovative and hands on Data Science training with iteanz.

Iteanz provides the most comprehensive and extraordinary technical training with our wealth of experience on Data Science.

We designed this cloud architect certification training for anyone seeking to learn the major components of Amazon Web Services (AWS). By the end of the course, you’ll be prepared to pass the associate-level AWS Certified Solutions Architect certification exam. The AWS certification is a must-have for any IT professional, and an AWS certified solution architects take home about $120,000 per year.

This course emphasizes AWS cloud best practices and recommended design patterns to help you think through the process of architecting optimal IT solutions on AWS. Case studies throughout the course showcase how some AWS customers have designed their infrastructures and the strategies and services they implemented.

Request more info..

Training Features

Instructor-led Sessions

36 Hours of Online Live Instructor-Led Classes. Weekend Class : 12 sessions of 3 hours each. Weekday Class : 18 sessions of 2 hours each.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various Data Science services.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Assignments

Live project based on any of the selected use cases, involving implementation of the various Data Science services.

Certification

Towards the end of the course, you will be working on a project. Iteanz certifies you as an Data Science Expert based on the project.

Course Outline



CHAPTER 1:Getting Started With Data Science And Recommender Systems

  • Data Science Overview
  • Reasons to use Data Science
  • Project Lifecycle
  • Data Acquirement
  • Evaluation of Input Data
  • Transforming Data
  • Statistical and analytical methods to work with data
  • Machine Learning basics
  • Introduction to Recommender systems
  • Apache Mahout Overview

CHAPTER 2:Reasons To Use, Project Lifecycle

  • What is Data Science?
  • What Kind of Problems can you solve?
  • Data Science Project Life Cycle
  • Data Science-Basic Principles
  • Data Acquisition
  • Data Collection
  • Understanding Data- Attributes in a Data, Different types of Variables
  • Build the Variable type Hierarchy
  • Two Dimensional Problem
  • Co-relation b/w the Variables- explain using Paint Tool
  • Outliers, Outlier Treatment
  • Boxplot, How to Draw a Boxplot

CHAPTER 3:Acquiring Data

  • Discussion on Boxplot- also Explain
  • Example to understand variable Distributions
  • What is Percentile? – Example using Rstudio tool
  • How do we identify outliers?
  • How do we handle outliers?
  • Outlier Treatment: Using Capping/Flooring General Method
  • Distribution- What is Normal Distribution
  • Why Normal Distribution is so popular
  • Uniform Distribution
  • Skewed Distribution
  • Transformation

CHAPTER 4:Machine Learning In Data Science

  • Discussion about Box plot and Outlier
  • Goal: Increase Profits of a Store
  • Areas of increasing the efficiency
  • Data Request
  • Business Problem: To maximize shop Profits
  • What are Interlinked variables
  • What is Strategy
  • Interaction b/w the Variables
  • Univariate analysis
  • Multivariate analysis
  • Bivariate analysis
  • Relation b/w Variables
  • Standardize Variables
  • What is Hypothesis?
  • Interpret the Correlation
  • Negative Correlation
  • Machine Learning

CHAPTER 5:Statistical And Analytical Methods Dealing With Data, Implementation Of Recommenders Using Apache Mahout And Transforming Data

  • Correlation b/w Nominal Variables
  • Contingency Table
  • What is Expected Value?
  • What is Mean?
  • How Expected Value is differ from Mean
  • Experiment – Controlled Experiment, Uncontrolled Experiment
  • Degree of Freedom
  • Dependency b/w Nominal Variable & Continuous Variable
  • Linear Regression
  • Extrapolation and Interpolation
  • Univariate Analysis for Linear Regression
  • Building Model for Linear Regression
  • Pattern of Data means?
  • Data Processing Operation
  • What is sampling?
  • Sampling Distribution
  • Stratified Sampling Technique
  • Disproportionate Sampling Technique
  • Balanced Allocation-part of Disproportionate Sampling
  • Systematic Sampling
  • Cluster Sampling
  • 2 angels of Data Science-Statistical Learning, Machine Learning

CHAPTER 6:Testing And Assessment, Production Deployment And More

  • Multi variable analysis
  • linear regration
  • Simple linear regration
  • Hypothesis testing
  • Speculation vs. claim(Query)
  • Sample
  • Step to test your hypothesis
  • performance measure
  • Generate null hypothesis
  • alternative hypothesis
  • Testing the hypothesis
  • Threshold value
  • Hypothesis testing explanation by example
  • Null Hypothesis
  • Alternative Hypothesis
  • Probability
  • Histogram of mean value
  • Revisit CHI-SQUARE independence test
  • Correlation between Nominal Variable

CHAPTER 7:Business Algorithms, Simple Approaches To Prediction, Building Model, Model Deployment

  • Machine Learning
  • Importance of Algorithms
  • Supervised and Unsupervised Learning
  • Various Algorithms on Business
  • Simple approaches to Prediction
  • Predict Algorithms
  • Population data
  • sampling
  • Disproportionate Sampling
  • Steps in Model Building
  • Sample the data
  • What is K?
  • Training Data
  • Test Data
  • Validation data
  • Model Building
  • Find the accuracy
  • Rules
  • Iteration
  • Deploy the model
  • Linear regression

CHAPTER 8:Getting Started With Segmentation Of Prediction And Analysis

  • Clustering
  • Cluster and Clustering with Example
  • Data Points, Grouping Data Points
  • Manual Profiling
  • Horizontal & Vertical Slicing
  • Clustering Algorithm
  • Criteria for take into Consideration before doing Clustering
  • Graphical Example
  • Clustering & Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy
  • Clustering
  • Simple Approaches to Prediction
  • Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity
  • Clustering Algorithm in Mahout
  • Probabilistic Clustering
  • Pattern Learning
  • Nearest Neighbor Prediction
  • Nearest Neighbor Analysis

CHAPTER 9:Integration Of R And Hadoop

  • R introduction
  • How R is typically used
  • Features of R
  • Introduction to Big data
  • R+Hadoop
  • Ways to connect with R and Hadoop
  • Products
  • Case Study
  • Architecture
  • Steps for Installing RIMPALA
  • How to create IMPALA packages

LIVE ONLINE TRAINING
$14999/Month

Why iteanz for AWS training?

  • Top technical trainers
  • Real Time and Hands on Experience Training
  • On Time Course Completion
  • Comprehensive curriculum
  • Innovative & interactive Training
  • Superb satisfaction scores
  • High Certification Pass Rate

Prerequisites

  • Basic Linux Commands (Optional)
  • You will need to set up an AWS Account (We help you to Setup)
  • A Windows, Linux or Mac PC/Laptop
  • 4G or At least 4MBPS Internet Speed

Intended Audience

  • Data Science Absolute Beginners. No prior Data Science experience necessary
  • Existing Solutions Architects
  • Programmers Interested in Deploying Applications on Data Science

What People Say

I've never seen any training company like iteanz, the training was very good and highly practical. Good support Staff to address any support request on time.

Testimonial sherif
sherif
Software Professional