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Online Trainings USA is famous on-line training institute in Hyderabad India. We offer online training on various tools and technologies we have excellent trainers with good experience, We provide job oriented online classes on various Tools & Technologies. We will provide 100% project support like solving the technical issues with in the time, We also provide Corporate trainings at client location & Online, We have a development center in Hyderabad, along with training we provide projects for required candidates. we will conduct mock interviews through on-line we will guide you for the interview we will support you in resume preparation we provide . Once you register in our web site and find coaching then our trainer supports and clarifies your doubts whereas doing job. This can be the special supply to our student to become an entire software system skilled. All told, courses we offer quality coaching to our students our passion is to coach every student in real. We will train in-depth studies all told courses.

Online Trainings USA Highlights·
  • IT training in all tools & technologies (online And class-room Corporate)
  • Real-time Job support (Technical Support project level)
  • Best ideas with career development planning
  • Excellent Resume & Interview preparation
  • Promotion and Job placement with our fortune 500 clients in USA, CANADA
  • Technical support to perform day 2 day activities of your project
  • Certification examination preparation training provided
Online Trainings USA advantages
  • All our trainers are considered professionals & experts in their technologies
  • All online trainings & Corporate Trainings are Live real time instructors
  • We use the best web conferencing technology to give the high Quality Training
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  • Excellent world-class Dedicated Conferencing Facility to organize the meetings High Quality.
  • Complete study material will be provided & Hands on Labs for each and Every session.

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I have done my SAP SD Training from Online Trainings USA. It was great training effort by instructor with real time experience. - JOHN
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I did Cognos IT Training from Online Trainings USA and it was professional trainers in teaching with quality and real time industry expert and i recommend my friends to join for any IT Software training course. -ASHA

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Data Science

Home  >>  Courses  >>  Data Science

Data science Introduction

  • Data Science motivating examples — Nate Silver, Netfilx, Money ball, okcupid, LinkedIn,
  • Introduction to Analytics, Types of Analytics,
  • Introduction to Analytics Methodology
  • Analytics Terminology, Analytics Tools
  • Introduction to Big Data
  • Introduction to Machine Learning

R software: 
1. Introduction and Overview of R Language :

  • Origin of R, Interface of R,R coding Practices
  • R Downloading and Installing R
  • Getting Help on a function
  • Viewing Documentation

Data Inputting in R Data Types

  • Data Types, Data Objects, Data Structures
  • Creating a vector and vector operations
  • Sub-setting
  • Writing data
  • Reading tabular data files
  • Reading from csv files
  • Initializing a data frame
  • Selecting data frame cols by position and name
  • Changing directories
  • Re-directing R output

Data Manipulation

  • Appending data to a vector
  • Combining multiple vectors
  • Merging data frames
  • Data transformation
  • Control structures
  • Nested Loops

splitting

  • Strings and dates
  • Handling NAs and Missing Values
  • Matrices and Arrays
  • The str Function
  • Logical operations
  • Relational operators
  • generating Random Variables
  • Accessing Variables
  • Matrix Multiplication and Inversion
  • Managing Subset of data
  • Character manipulation
  • Data aggregation
  • Subscripting

Functions and Programming

  • Flow Control: For loop
  • If condition
  • While conditions and repeat loop
  • Debugging tools
  • Concatenation of Data
  • Combining Vars, cbind, rbind
  • sapply, lapply, tapply functions

Basic Statistics

  • Descriptive Statistics Introduction to Advanced Data Analytics
  • Statistical inferences for various Business problems
  • Types of Variables, measures of central tendency and dispersion
  • Variable Distributions and Probability Distributions
  • Normal Distribution and Properties
  • Computing basic statistics
  • Comparing means of two samples
  • Testing a correlation for significance
  • Testing a proportion
  • Classical tests (t,z,F)
  • ANOVA
  • Summarizing Data
  • Data Munging Basics
  • Test of Hypothesis Null/Alternative Hypothesis formulation 7
  • One Sample, two sample (Paired and Independent) T/Z Test
  • P Value Interpretation
  • Analysis of Variance (ANOVA)
  • Non Parametric Tests (Chi-Square, Kruskal-Wallis, Mann-Whitney.)
  • Introduction to Correlation – Karl Pearson
  • Spearman Rank Correlation

Advanced Analytics with real world examples (Mini Projects)Part-II

  • Regression Theory
  • Linear regression
  • Logistic Regression Non Linear Regressions using Link functions
  • Logit Link Function
  • Binomial Propensity Modeling
  • Training-Validation approach
  • Factor Analysis Introduction to Factor Analysis – PCA
  • Reliability Test 4
  • KMO MSA tests, Eigen Value Interpretation
  • Factor Rotation and Extraction
  • Cluster Analysis Introduction to Cluster Techniques
  • Distance Methodologies
  • Hierarchical and Non-Hierarchical Procedures
  • K-Means clustering
  • Wards Method

Time Series Analysis

  • Introduction and Exponential Smoothening Introduction to Time Series Data and

Analysis

  • Decomposition of Time Series
  • Trend and Seasonality detection and forecasting
  • Exponential Smoothing (Single, double and triple)
  • ARIMA Modeling Box – Jenkins Methodology
  • Introduction to Auto Regression and Moving Averages, ACF, PACF

Data Mining : Machine learning

  • Introduction to Machine learning and various machine learning techniques
  • Introduction to Data Mining
  • Introduction to Text Mining
  • Text analytic Process
  • Sentiment Analysis

Part IV

  • Statistical Analysis & Data Mining/Machine Learning
  • Cluster Analysis using R-Rattle
  • Association Rule Mining
  • Predictive Modeling using Decision Trees
  • Supervised learning
  • Un- Supervised learning
  • Reinforcement learning
  • Neural Network
  • Support Vector machine
  • Evaluating & Deploying Models Evaluating performance of Model on Training and Validation data
  • ROC, Sensitivity, Specificity, Lift charts, Error Matrix
  • Deploying models using Score options
  • Opening and Saving models using Rattle

Analytics in Excel – 3 days

  • Data Preparation and Data Exploration in Excel
  • Network Analysis using NodeXL

Data Visualization in R

  • Creating a bar chart, dot plot
  • Creating a scatter plot, pie chart
  • Creating a histogram and box plot
  • Other plotting functions
  • Plotting with base graphics
  • Plotting with Lattice graphics
  • Plotting and coloring in R

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