Data Science with R Programming

HomeData Science with R Programming

Introduction to R

Data Structure

  1. Vectors
  2. Matrices
  3. Dataframes
  4. Lists

Functions

  1. Numeric Functions
  2. Character Functions
  3. Date Functions
  4. User Defined Functions

Data Transformation

  1. Computing New Variables
  2. Duplicate Values
  3. Sorting

Data Formatting

  1. Labelling
  2. Numeric Formatting
  3. Character Formatting
  4. Date Formatting

Restructuring Data

  1. Merge
  2. Append

Looping Structure

  1. If-else statements
  2. For loops
  3. While loops
  4. Repeat, Next, Break

Graphics

  1. Scatterplots, Histograms, Barcharts, Dotplots
  2. Labels, Legends, Titles, Axes

Introduction to Machine Learning

  1. Introduction
  2. Need of Machine Learning
  3. Difference between Statistics and Machine Learning
  4. Popular libraries in Machine Learning

Statistical Analysis using R

  1. Basic Statistics
  2. Hypothesis Testing
  3. T Test, Z Test, F Test
  4. Correlation
  5. Hypothesis Testing
  6. ANOVA, MANOVA, ANCOVA

Statistical Modelling using R

  1. Linear regression
    1. Introduction
    2. Assumption Testing
    3. Interpretation
    4. Validation
    5. Implementation
  2. Logistic regression
    1. Introduction
    2. Difference between linear and logistic
    3. Variable Selection Methods
    4. Interpretation
    5. Validation
  3. Cluster Analysis
    1. Introduction
    2. Hierarchal Clustering
    3. K-Means Clustering
    4. Finalization of Clusters
    5. Interpretation
    6. Output Validation
  4. Time Series
    1. Introduction
    2. Components
    3. Methods of Forecasting
    4. ARIMA/ARMA
    5. Interpretation
    6. Validation
    7. Forecasting
  5. Decision Tree
    1. Introduction
    2. Pruning of Trees
    3. CHAID
    4. CART
    5. C
    6. Interpretation
    7. Ensemble Method
  6. Random Forest
    1. Introduction
    2. Random Forest for Regression
    3. Random Forest for Classification
    4. Interpretation
    5. Validation
  7. Text Analytics & Word Cloud
    1. Tokenization
    2. Term Document Matrix
    3. Stemming
    4. Corpus
    5. Different Text Cleansing Method
  8. Sentimental Analysis using Twitter Data
    1. Introduction
    2. Setting Twitter Development Login and account
    3. Generating API and Secret Keys
    4. Extracting Data from Twitter
    5. Library for Positive and Negative Keywords
    6. Text Cleansing
    7. Classification of Negative and Positive Keywords
Course Detail
Fees:
Rs 25,000

 

Mode:Classroom/Online

 

Duration:
3 Months

 

Course Contents:
R, Predictive Analytics, Machine Learning

 

Course Benefits:
Student Will able to use R, Predictive Analytics
and perform Analytics

 

RS 35,000

Mode:Classroom/Online(3 Months)

Data Science Pro

Data Science with R & Python

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RS 25,000

Mode:Classroom/Online(3 Months)

Data Science with using Python

Data Science with Python , Machine Learning

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KEY FEATURES

3 Months of Classroom Training.

 

Session have assignments and daily task.

 

Recorded Videos and presentation for reference.

Books and related data sets in form of excel and csv.

 

Interview Questions for related topic.

 

Used Case Study and Projects to understand real scenarios.

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