CRASH COURSES
ANALYTICS BASE
ADVANCED ANALYTICS
BASE ANALYTICS WITH R
ADVANCED ANALYTICS WITH R
BASE ANALYTICS WITH SPSS
ADVANCED ANALYTICS WITH SPSS
BASE ANALYTICS WITH SAS
ADVANCED ANALYTICS WITH SAS
QLIKVIEW
TABLEAU
BIGDATA AND HADOOP



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R COURSE
Syllabus for R
Basics of Data Mining Using R
Types of Data-Only Two (Either Numerical or Text)
Exploratory Analysis
  • Single Variable- Histograms, Bar Charts, Trend Charts, Box Plots
  • Two Variables- Scatter Plots, Correlation Plots, Bar Charts
  • Multiple Variables- Parallel Coordinates,Mosaic Plots, Other Explotary visualization Techniques
Data Pre-Processing
  • Outlier Analysis
  • Binning
  • Variable Selection
  • Imputing Missing Values
  • Transformations
    • Variable Transformations
  • Feature Transformations
    • Base Data + Derived Variables
    • Continuous vs. Categorical/Discrete
    • Data Cleansing
    • Addressing Missing Values
    • Outliers
    • Multi-Collinearity
Business Foundation Analytics Using R
Data Flow and Management for Business Operations and Problem Solving
External Data and Information Sources
Typical Analytical Process Flow
Data Collections Method
Data Summarization and Presentation
Measures of Central Tendencies and Dispersions
Introduction to Statistics and Analytics Tools (R)
Getting Started on Analytics Tools (R)
Managing Data using Analytics Tools (R)
Data Manipulation and Report Generation (R)
Understanding the Problem and its Scope
Sampling Distributions and Central Limit Theory
Accessing and Aquiring Data
Understanding the Data
Hypothesis Testing
Developing Solution (Analytical Model Design)
Preparing Data
Regression Analysis-Basics
Running OLS Regression using Analytics Tools (Case Studies)
Running Logistic Regression using Analytics Tools (Case Studies)

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