DATASCIENCE TRAINING IN HYDERABAD

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RS Trainings is a best training center for datascience given corporate trainings to different reputed companies. In datascience training all sessions are teaching with examples and with real time scenarios. We are helping in real time how approach job market, Resume preparation, Interview point of preparation, how to solve problem in projects in job environment, information about job market etc. Training also providing classroom Training in Hyderabad and online from anywhere. We provide all recordings for classes, materials, sample resumes, and other important stuff. datascience Online Training We provide datascience online training through worldwide like India, USA, Japan, UK, Malaysia, Singapore, Australia, Sweden, South Africa, and etc. Hadoop Corporate Training RStrainings providing corporate training world wide depending on Company requirements with well experience real time experts

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slide 1:

DATASCIENCE TRAINING IN HYDERABAD DATASCIENCE ONLINE TRAINING 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

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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

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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

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