Data Science Online Training
 

Data science Online Training

Data science online training From India:

RVH Technologies provides Data science Online Training in live, real-time and interactive. It is not self paced or pre-recorded, and provides many of the same benefits as in-person classroom training, with live chat and online collaboration features, information flow between students and their instructors or peers can equal or even surpass that of the classroom training

RVH technologies mainly concentrates Data science online training.We provide Data science online training for a low price /Affordable price from India/Hyderabad by real time experts. We are providing Data science self paced online training vedeos for very affordable cost.

 


Data science Online Training Content

Duration : 45 Hours

 

DATASCIENCE CONTENT:

 DESCRIPTIVE STATISTICS AND PROBABILITY DISTRIBUTIONS:

  • Introduction about Statistics
  • Different Types of Variables
  • Measures of Central Tendency with examples
  • Measures of Dispersion
  • Probability & Distributions
  • Probability Basics
  • Binomial Distribution and its properties
  • Poisson distribution and its properties
  • Normal distribution and its properties

INFERENTIAL STATISTICS AND TESTING OF HYPOTHESIS

  • Sampling methods
  • Different methods of estimation
  • Testing of Hypothesis & Tests
  • Analysis of Variance

COVARIANCE & CORRELATION

PREDICTIVE MODELING STEPS AND METHODOLOGY WITH LIVE EXAMPLE:

  • Data Preparation
  • Exploratory Data analysis
  • Model Development
  • Model Validation
  • Model Implementation

SUPERVISED TECHNIQUES:

MULTIPLE LINEAR REGRESSION

  • Linear Regression - Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)
  • Validation of Linear Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc)
  • Interpretation of Results - Business Validation - Implementation on new data
  • Real time case study of Manufacturing and Telecom Industry to estimate the future revenue using the models

LOGISTIC REGRESSION - INTRODUCTION - APPLICATIONS

  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve)
  • Probability Cut-offs, Lift charts, Model equation, drivers etc)
  • Interpretation of Results - Business Validation - Implementation on new data
  • Real time case study to Predict the Churn customers in the Banking and Retail industry

PARTIAL LEAST SQUARE REGRESSION

  • Partial Least square Regression - Introduction - Applications
  • Difference between Linear Regression and Partial Least Square Regression
  • Building PLS Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)
  • Interpretation of Results - Business Validation - Implementation on new data
  • Sharing the real time example to identify the key factors which are driving the Revenue

VARIABLE REDUCTION TECHNIQUES

FACTOR ANALYSIS

PRINCIPLE COMPONENT ANALYSIS

  • Assumptions of PCA
  • Working Mechanism of PCA
  • Types of Rotations
  • Standardization
  • Positives and Negatives of PCA

SUPERVISED TECHNIQUES CLASSIFICATION:

CHAID

CART

DIFFERENCE BETWEEN CHAID AND CART

RANDOM FOREST

  • Decision tree vs. Random Forest
  • Data Preparation
  • Missing data imputation
  • Outlier detection
  • Handling imbalance data
  • Random Record selection
  • Random Forest R parameters
  • Random Variable selection
  • Optimal number of variables selection
  • Calculating Out Of Bag (OOB) error rate
  • Calculating Out of Bag Predictions

COUPLE OF REAL TIME USE CASES WHICH ARE RELATED TO TELECOM AND RETAIL INDUSTRY. IDENTIFICATION OF THE CHURN.

UNSUPERVISED TECHNIQUES:

SEGMENTATION FOR MARKETING ANALYSIS

  •  Need for segmentation
  • Criterion of segmentation
  • Types of distances
  • Clustering algorithms
  • Hierarchical clustering
  • K-means clustering
  • Deciding number of clusters
  • Case study

BUSINESS RULES CRITERIA

REAL TIME USE CASE TO IDENTIFY THE MOST VALUABLE REVENUE GENERATING CUSTOMERS.

TIME SERIES ANALYSIS:

TIME SERIES COMPONENTS( TREND, SEASONALITY, CYCLICITY AND LEVEL) AND DECOMPOSITION

BASIC TECHNIQUES

  • Averages,
  • Smoothening etc

ADVANCED TECHNIQUES

  • AR Models,
  • ARIMA
  • UCM
  • Hybrid Model

UNDERSTANDING FORECASTING ACCURACY - MAPE, MAD, MSE ETC

COUPLE OF USE CASES, TO FORECAST THE FUTURE SALES OF PRODUCTS

TEXT ANALYTICS:

GATHERING TEXT DATA FROM WEB AND OTHER SOURCES

PROCESSING RAW WEB DATA

COLLECTING TWITTER DATA WITH TWITTER API

NAIVE BAYES ALGORITHM

  • Assumptions and of Naïve Bayes
  • Processing of Text data
  • Handling Standard and Text data
  • Building Naïve Bayes Model
  • Understanding standard model metrics
  • Validation of the Models (Re running Vs. Scoring)

SENTIMENT ANALYSIS

  • Goal Setting
  • Text Preprocessing
  • Parsing the content
  • Text refinement
  • Analysis and Scoring

 

 

 

Data Science course highlights:


Ability to remain connected together with your real job whereas attending coaching.

Access to a live teacher for the whole period of the course

You will see the configuration steps live on your screen

Save time and cash by avoiding costly travel

Classes will be conducted weekdays and weekends too.

The institute has glorious and real time Data Science colleges.

We are charging terribly reasonable and economical worth for teaching Data Science Module.

We have a customised Data Science course of study and there’s a forthcoming batches weekly on Data Science Module

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