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Certified Business Analytics Professional
Quick Facts
| particular | details | ||||
|---|---|---|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
|
Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekends
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Course and certificate fees
Fees information
₹ 22,990 ₹32,990
certificate availability
Yes
certificate providing authority
Edvancer Eduventures
The syllabus
Introduction to business analytics
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
Analytics Manager
Flipkart Pvt. Ltd.
B.E /B.Tech, Other Masters
Mr Lalit Sachan
Co Founder
Edvancer Eduventures
B.E /B.Tech, Other Masters
Mr Renold Devaraj
Data Scientist
Dell
B.E /B.Tech, MBA
Mr Abhishek Nagarjuna
Data Scientist
Freelancer
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