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# Data Science Advanced with R

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Salt Lake, Kolkata

Course ID: 35675

Salt Lake, Kolkata

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This course provides extensive knowledge on Data Science and how it is related to Big Data. It covers most of the commonly used algorithms and details of Data Analytics programming using R. It also provides real life examples using some practical case studies and projects.

# Duration: 60 hours

Session 1: Getting started with R

•  History of R

• Installation of R and R studio

• Basic data types

• Functions for reading and writing data in R

• Working with Scripts

• Navigating the Workspace

Session 2: Basics of R Programming

• Control structures and functions

• First programming assignment

• Loop functions and debugging

• Second programming assignment

Session 3: Graphics in R

• Exploratory Graphs

• Plotting systems in R

• Base plotting system

• Graphics devices

• Hands on exercise

Session 4: Exploratory Data Analysis

• Summary Commands

• Name Commands

• Summarizing Samples

• Cumulative Statistics

• Summary Statistics for Data Frames

• Summary Statistics for Matrix Objects

• Summary Statistics for Lists

• Contingency Tables

• Cross Tabulation

Session 5-7: Elementary Statistical Inference

Probability and Expected Values:

• Introduction to Probability

• Probability mass functions

• Probability density functions

• Conditional probability

• Bayes' rule

• Independence

• Expected values

Variability, Distribution, & Asymptotics:

• Introduction to variability

• Standard error of the mean

• Variance data example

• Distributions Binomial, Normal, Poisson

• Asymptotics and LLN

• Asymptotics and the CLT

• Asymptotics and confidence intervals

Intervals, Testing, & Pvalues

• Confidence intervals

• Hypothesis testing

• P-values

• Practical R Exercises

Session 8: Resampling Techniques and Permutation Tests

Power, Bootstrapping, & Permutation Tests

• Power

• Resampling

• Permutation Tests

Session 9-10: Multiple Linear Regression & Diagnostics

Least Squares and Linear Regression:

• Introduction to Regression

• Introduction: Basic Least Squares

• Linear Least Squares

• Regression to the mean

• Practical R exercises

Linear Regression & Multivariable Regression

• Statistical Linear Regression Models

• Interpreting Coefficients

• Linear Regression for Prediction

• Residuals

• Residual Variance

• Inference in regression

• Prediction

• Introduction to Multivariable Regression

• Multivariate Examples

Multivariable Regression, Residuals, & Diagnostics

• Multivariable Regression Details

• Residuals and Diagnostics

• Model Selection

Session 11: Logistic & Poisson Regression

• GLMs

• Logistic Regression

• Poisson Regression

• Variance Inflation Factors

• Overfitting and Underfitting

• Binary Outcomes

• Count Outcomes

Session 12-13: Applied Multivariate Techniques

• Principal Component Analysis

• Multidimensional Scaling

• Factor Analysis

• Classification Problems

• Cluster Analysis

Session 13-14: Prediction & Cross-validation

• CART, BART, and Random Forest

• Bagging and Boosting

• Model-based Prediction

Session 15-16: Regularised Regressions

• Combining Predictors

• Unsupervised Learning

Session 17-18: Data Mining in Industry using R

• Data types, Data Reading, Data Storing,

• Organising and Manging Data Files.

• Raw and processed data

• Components of Tidy data

• Reading different file types(Excel, XML, JSON)

• table package details

• Reading from big data sources like HDFS/Hive

• Subsetting and sorting

• Managing dataframes with dplyr

• Merging data

• Tidying data with tidyr

• Regular expressions

• Working with dates

Session 19-21: Practical Machine Learning in industry with Projects

Project 1(Building a Regression Model):

• Types of Regression Models – Recap

• Data acquisition and extracting right features from the data

• Training and using regression models

• Evaluating the performance of regression models

• Improving model performance and tuning parameters

Project 2(Building a Clustering Model):

• Types of Clustering Models – Recap

• Data acquisition and extracting right features from the data

• Training a clustering model

• Making predictions using a clustering model

• Evaluating the performance of clustering models

• Tuning parameters for clustering models

Project 3(Dimensionality Reduction):

• Types of Dimensionality Reduction – Recap

• Data acquisition and extracting right features from the data

• Training a dimensionality reduction model

• Using a dimensionality reduction model

• Evaluating dimensionality reduction models

Not decided yet.

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The trainer is working in a leading IT services multinational based in Kolkata. He has around 10+ of experience in developing software solutions and accelerators in different kinds of Java based development and have 5-6 years of experience in Big Data and Data Analytics space.

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