Online Training on Data Science with R or Machine Learning with R

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Date/Time
Date(s) - 10/05/2018 - 25/05/2018
8:30 am - 11:00 am

Categories


Data Science with R – Online Training

Online Training on Data Science with R or Machine Learning with or Statistical Learning with R

Target audience – IT working professionals, Data Analysts, Students, Researchers

Prerequisites if any – Basic knowledge of mathematics, statistics, programming language

Number of days – 15 days, 2.5 hrs each session (daily or weekend)

Fees – INR 20 K per person

Location – Online

Contact us – +91 99530 82451

 

Session wise list of topics :

Session 1,2 – Introduction to Data Science

Data Science Introduction, Data Science Toolkit, job outlook, Prerequisite, Target Audience, CRISP-DM Model

Session 3,4 – Basics of Statistics

Statistics Concepts, Random variable, Type of Random variables,Probability, Distribution of Random variables, Normal Distribution, Binomial Distribution, Poisson Distribution

Session 5 – Advaced Statistics

Central tendency, Inferential Statistics and Sampling Distribution, Simulation, Hypothesis Testing, 1 tail and 2 tail test, z test, t test

Session 6,7 – R Programming

Installation, Configuration, All the basic & intermediate Programming Concepts in R, Data Structure, Control Structure & Functions.

Session 8 – Applied Statistics using R

Normal distribution, Simulation, hypothesis testing, other statistical concepts using R

Session 9,10 – Graphics and Plot systems, EDA

Graphics and Plot systems in R, ggplot2 and other useful packages/functions in R, Exploratory Data Analysis Exercise in R

Session 11,12 – Machine Learning

Introduction to machine Learning, Supervised and Unsupervised ML, Machine Learning Algorithms & different Machine Learning techniques including Regression, Classification, Association, Clustering, Time Series, Decision tree, Random Forest, Support vector Machine

Session 13,14,15 – Applied data Science & Machine Learning using R

Real world Machine Learning Problem and Solution using R, Model Validation/Cross validation, parameter tuning, Model evaluation metrics, MSE,RMSE, R square, Adjusted R Square, Confusion Matrix,Bias and Variance, Under fitting, over Fitting.

 

Bookings

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