Data Science using R

Introduction to R

  1. Vectors
  2. Matrices
  3. Dataframes
  4. Lists
  1. Numeric Functions
  2. Character Functions
  3. Date Functions
  4. User Defined Functions
  1. Computing New Variables
  2. Duplicate Values
  3. Sorting
  1. Labelling
  2. Numeric Formatting
  3. Character Formatting
  4. Date Formatting
  1. Merge
  2. Append
  1. If-else statements
  2. For loops
  3. While loops
  4. Repeat, Next, Break
  1. Scatterplots, Histograms, Barcharts, Dotplots
  2. Labels, Legends, Titles, Axes
  1. Introduction
  2. Need of Machine Learning
  3. Difference between Statistics and Machine Learning
  4. Popular libraries in Machine Learning
  1. Basic Statistics
  2. Hypothesis Testing
  3. T Test, Z Test, F Test
  4. Correlation
  5. Hypothesis Testing
  6. ANOVA, MANOVA, ANCOVA
  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 15,000

 

Class Mode:
Live Online

 

Duration:
2 Months

 

Course Contents:
R, Predictive Analytics, Machine Learning

 

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

 

RS 20,000

Mode:Live Online(3 Months)

Data Science Pro

Data Science with R & Python

RS 15,000

Mode:Live Online(2 Months)

Data Science with using Python

Data Science with Python , Machine Learning

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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