Certified Business Analytics Professional

BY
Edvancer Eduventures

Mode

Online

Fees

₹ 22990 32990

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based
Frequency of Classes Weekends

Course and certificate fees

Fees information
₹ 22,990  ₹32,990
certificate availability

Yes

certificate providing authority

Edvancer Eduventures

The syllabus

Introduction to business analytics

  • What is analytics & why is it so important?
  • Applications of analytics
  • Different kinds of analytics
  • Various analytics tools
  • Analytics project methodology
  • Real world case study

R Training

Fundamentals of R
  • Installation of R & R Studio
  • Getting started with R
  • Basic & advanced data types in R
  • Variable operators in R
  • Working with R data frames
  • Reading and writing data files to R
  • R functions and loops
  • Special utility functions
  • Merging and sorting data
  • Case study on data management using R
  • Practice assignment
Data visualization in R
  • Need for data visualization
  • Components of data visualization
  • Utility and limitations
  • Introduction to grammar of graphics
  • Using the ggplot2 package in R to create visualizations
Data preparation and cleaning using R
  • Needs & methods of data preparation
  • Handling missing values
  • Outlier treatment
  • Transforming variables
  • Derived variables
  • Binning data
  • Modifying data with Base R
  • Data processing with dplyr package
  • Using SQL in R
  • Practice assignment

Setting the base of business analytics

Understanding the data using univariate statistics in R
  • Summarizing data, measures of central tendency
  • Measures of variability, distributions
  • Using R to summarize data
  • Case study on univariate statistics using R
  • Practice assignment
Hypothesis testing and ANOVA in R to guide decision making
  • Introducing statistical inference
  • Estimators and confidence intervals
  • Central Limit theorem
  • Parametric and non-parametric statistical tests
  • Analysis of variance (ANOVA)
  • Conducting statistical tests
  • Practice assignment

Predictive modelling in R

Correlation and Linear regression
  • Correlation
  • Simple linear regression
  • Multiple linear regression
  • Model diagnostics and validation
  • Case study
Logistic regression
  • Moving from linear to logistic
  • Model assumptions and Odds ratio
  • Model assessment and gains table
  • ROC curve and KS statistic
  • Case Study
Techniques of customer segmentation
  • Need for segmentation
  • Criterion of segmentation
  • Types of distances
  • Hierarchical clustering
  • K-means clustering
  • Deciding number of clusters
  • Case study
Time series forecasting techniques
  • Need for forecasting
  • What are time series?
  • Smoothing techniques
  • Time series models
  • ARIMA
Decision trees & Random Forests
  • What are decision trees
  • Entropy and Gini impurity index
  • Decision tree algorithms
  • CART
  • Random Forest
  • Case Study
Boosting Machines
  • Concept of weak learners
  • Introduction to boosting algorithms
  • Adaptive Boosting
  • Extreme Gradient Boosting (XGBoost)
  • Case study
Cross Validation & Parameter Tuning
  • Model performance measure with cross validation
  • Parameter tuning with grid & randomised grid search

Instructors

Mr Utsab Chakraborty

Mr Utsab Chakraborty
Analytics Manager
Flipkart Pvt. Ltd.

B.E /B.Tech, Other Masters

Mr Lalit Sachan

Mr Lalit Sachan
Co Founder
Edvancer Eduventures

B.E /B.Tech, Other Masters

Mr Renold Devaraj

Mr Renold Devaraj
Data Scientist
Dell

B.E /B.Tech, MBA

Mr Abhishek Nagarjuna

Mr Abhishek Nagarjuna
Data Scientist
Freelancer

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