## Best DATA SCIENCE / DATA ANALYTICS TRAINING THROUGH R

**Course Description: **

This course is targeted at novice to the technology. This course is intended to provide DataScience training with Python.

**Course Outline: **

**Topics Covered / Weekly Lecture Schedule: **

DATA SCIENCE WITH R

- INTRODUCTION TO DATA SCIENCE
- Introduction to Data Science
- The data science process
- Introduction to R
- Loading data into R
- Exploring data
- Managing data

- MODELING METHODS
- Introduction to Inferential Statistics
- Memorization methods
- Linear and logistic regression
- Choosing and evaluating models
- Unsupervised methods
- Exploring advanced methods

**CONTENT**

- INTRODUCTION TO DATA SCIENCE
- Introduction to Data Science
- what is Data Science
- What do data scientists do?
- Demystifying the ‘data science’ hype
- what is statistics?
- what is data mining
- Data Science Core Components
- Data Science:what it actually is
- use case implementation process
- Types of Data Scientists
- What distinguishes data science from statistics?

- The data science process
- The roles in a data science project
- Project roles

- Stages of a data science project
- Defining the goal
- Data collection and management
- Modeling
- Model evaluation and critique
- Presentation and documentation
- Model deployment and maintenance

- Setting expectations
- Determining lower and upper bounds on model performance

- Introduction to R
- Installations of R
- R Objects (character, numeric, integer, complex, logical)
- R attributes (names, dimnames, class, length, dimensions)
- R Data types (Vectors, lists, matrices, factors, Data frames)
- Other R attributes (missing values, summary, str)
- Subsetting in R
- Vectorized operation in R
- Control Structure (for, while, if-else, repeat, next, break)
- Functions
- Loops in R (lapply, sapply, apply, tapply, mapply, split)

- Loading data into R
- Working with data from files
- Working with well-structured data from files or URLs
- Using R on less-structured data

- Working with relational databases
- Loading data from a database into R

- Exploring data
- Introduction to statistics
- Descriptive Statistics
- Collection of data
- Measure of Central Tendancy
- Measure of Dispersion
- Correlation and Regression

- Using summary statistics to spot problems in R
- Typical problems revealed by data summaries

- Spotting problems using graphics and visualization in R
- Visually checking distributions for a single variable
- Visually checking relationships between two variables

- Managing data
- Cleaning data
- Treating missing values (NAs)
- Data transformations

- Sampling for modeling and validation
- Test and training splits
- Creating a sample group column
- Record grouping
- Data provenance

- Cleaning data

- Working with data from files

- The roles in a data science project

- MODELING METHODS
- Introduction to Inferential Statistics
- Memorization methods
- Building single-variable models
- Using categorical features
- Using numeric features
- Using cross-validation to estimate effects of overfitting

- Building models using many variables
- Variable selection
- Using decision trees
- nearest neighbor methods
- Using Naive Bayes

- Linear and logistic regression
- Using linear regression
- Understanding linear regression
- Building a linear
- regression model
- Making predictions
- Finding relations and extracting advice
- Reading the model summary and characterizing coefficient quality
- Linear regression takeaways

- Using logistic regression
- Understanding logistic regression
- Building a logistic
- regression model
- Making predictions
- Finding relations and extracting advice from logistic models
- Reading the model summary and characterizing coefficients
- Logistic regression takeaways

- Choosing and evaluating models
- Mapping problems to machine learning tasks
- Solving classification problems
- Solving scoring
- problems Working without known targets
- Problem-to-method mapping

- Evaluating models
- Evaluating classification models
- Evaluating scoring models
- Evaluating probability models
- Evaluating ranking models
- Evaluating clustering models

- Validating models
- Identifying common model problems
- Quantifying model
- soundness
- Ensuring model quality

- Unsupervised methods
- Cluster analysis
- Distances
- Preparing the data
- Hierarchical
- clustering with hclust()
- The k-means algorithm
- Assigning new points to clusters
- Clustering

- Association rules
- Overview of association rules
- The example problem
- Mining association rules with the arules package
- Association rule takeaway

- Exploring advanced methods
- Using bagging and random forests to reduce training variance
- Using bagging to improve prediction
- Using random forests
- to further improve prediction
- Bagging and random forest

- Using kernel methods to increase data separation
- Understanding kernel functions
- Using an explicit kernel on a problem
- Kernel takeaways

- Using SVMs to model complicated decision boundaries
- Understanding support vector machines
- Trying an SVM on artificial example data
- Using SVMs on real data

- Using bagging and random forests to reduce training variance

- Cluster analysis

- Mapping problems to machine learning tasks

- Using linear regression

- Building single-variable models

Support vector machine takea

**High Lights :**

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** Trainer is Microsoft certified Real Time working professional.**

**Main focus on Hands-on sessions.**

**Course aligned to Microsoft Certification.**

**Flexible timings for working people.**

**100% satisfactory job oriented real time training **

**Real-time projects.**

**Affordable course fee.**

**Guidance in Resume Preparation.**

**100% placement assistance.**

**Post training support.**

**Life validity for attending classes.**

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**For More Details, Please contact**

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