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Data Science Online & Classroom Training


Course Name : Data Science                                                                    
Duration         : 80 hours
Faculty            : Realtime experience

           sun trainings is a best Data Science online training institute in hyderabad . We are providing good online training on Hadoop.
Data Science Content:



During this course, you will learn:

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
USE CASE OF HEALTH CARE INDUSTRY, TO IDENTIFY THE SENTIMENT OF THE PATIENTS ON SPECIFIED HOSPITAL BY EXTRACTING THE DATA FROM THE TWITTER.
VISUALIZATION USING TABLEAU:
LIVE CONNECTIVITY FROM R TO TABLEAU

GENERATING THE REPORTS AND CHARTS

5+-REAL TIME PROJECTS BY USING DIFFERENT USE CASES

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