Business Analytics with R

Data Science commonly known as Business Analytics is one of the statistical and scientific approaches to derive insights from data and help in decision making. Going through this course you will be walked through numerous predictive models and how to derive results from those models. You will also learn when which models should be applied moving ahead. The predictive modeling in this course will be done using R only.
Course Summary
  • 80 hours of Instructor Led classes in R
  • Videos of classes available for one year after enrolment
  • Assignments and practice files available while learning
  • Support available 24x7
  • Fees: Rs. 25000 or 375 USD
  • Introduction to Business Analytics
    • Why Analytics?
    • Different types of Analytics
    • Applications of Analytics
  • R Fundamentals
    • Introduction to R and RStudio
    • CRAN
    • Using R Help
    • Loops in R
    • Statistics in R
    • Control Structures
    • Data Frames
    • Lists
    • Directories and Outputs
  • Basic Statistics in R
    • Basic Statistics
    • Correlations
    • Classical Tests (t, z, F)
    • ANOVA
    • Cross tabulation
  • Data Manipulation in R
    • Cleaning and Transforming data
    • Exploring and Visualizing data
    • Strings and Dates
    • Outlier detection
    • Logical Operators
    • Subscripting
    • Practice Assignment
  • Univariate Statistics
    • Data Distributions
    • Summarizing and measuring the data
  • Hypothesis Testing
    • Point Estimation
    • Central Limit Theorem
    • Confidence Intervals
    • Statistal Tests of Hypothesis Testing
    • ANOVA Test
    • Chisq and F-Test
  • Data Visualization
    • Creating basic charts
    • Other plotting functions
    • Plotting with ggplot2
    • Plotting and colouring in R
  • Data Preparation for modeling
    • Why data preparation?
    • Cleaning and Transforming Data
    • Exploring and Visualizing Data
    • Outlier Treatment
    • Handling Missing Values
    • Using SQL in R
    • Case Study
  • Linear Regression
    • Correlation
    • Linear Regression - What, How and Why?
    • Linear Regression Assumptions
    • Model Diagnostics
    • Case Studies
  • Logistic Regression
    • Why logistic and not linear?
    • Odds Ratio
    • Lift Chart, ROC Curves, KS Statistics
    • Case Studies
  • Clustering/Segmentation
    • Why do we need clustering?
    • Distance Types
    • Hierarchical Clustering
    • K-Means Clustering
    • Deciding number of clusters
    • Case Studies
  •  Decision Trees
    • Entropy
    • Deviance
    • Gini Impurity
    • C5.0
    • CHAID
    • CART
    • Random Forest
    • Case Studies
  • Time Series
    • What is Forecasting?
    • Why do we need Time Series?
    • Levels, Trends, Seasonality and Randomness
    • Holt-Winters Method
    • ARIMA
    • Case Studies
Demo video

single payment of $375

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